c53f3339bb
CUTLASS 2.3 adds GEMMs targeting Sparse Tensor Cores on the NVIDIA Ampere Architecture, fast SGEMM, and small matrix classes, bug fixes, and performance enhancements.
244 lines
6.7 KiB
C++
244 lines
6.7 KiB
C++
/***************************************************************************************************
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* Copyright (c) 2017-2020, NVIDIA CORPORATION. All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without modification, are permitted
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* provided that the following conditions are met:
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* * Redistributions of source code must retain the above copyright notice, this list of
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* conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above copyright notice, this list of
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* conditions and the following disclaimer in the documentation and/or other materials
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* provided with the distribution.
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* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
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* to endorse or promote products derived from this software without specific prior written
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* permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
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* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
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* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
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* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
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* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
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* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*
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**************************************************************************************************/
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/*! \file
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\brief Matrix multiply
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*/
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#pragma once
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#include <cuda_fp16.h>
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#include "cutlass/arch/mma.h"
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#include "cutlass/layout/matrix.h"
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/////////////////////////////////////////////////////////////////////////////////////////////////
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namespace cutlass {
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namespace arch {
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/////////////////////////////////////////////////////////////////////////////////////////////////
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/// Matrix multiply-add operation
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template <typename LayoutA, typename LayoutB, typename LayoutC>
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struct Mma<
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gemm::GemmShape<2,1,1>,
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1,
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half_t,
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LayoutA,
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half_t,
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LayoutB,
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half_t,
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LayoutC,
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OpMultiplyAdd> {
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using Shape = gemm::GemmShape<2, 1, 1>;
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using Operator = OpMultiplyAdd;
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CUTLASS_HOST_DEVICE
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void operator()(
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Array<half_t, 2> &d,
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Array<half_t, 2> const &a,
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Array<half_t, 1> const &b,
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Array<half_t, 2> const &c
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) {
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#if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 600))
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__half2 const & A = reinterpret_cast<__half2 const &>(a);
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__half2 B = __half2half2(reinterpret_cast<__half const &>(b));
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__half2 const & C = reinterpret_cast<__half2 const &>(c);
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__half2 D = __hfma2(A, B, C);
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d = reinterpret_cast<Array<half_t, 2> &>(D);
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#else
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CUTLASS_PRAGMA_UNROLL
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for (int i = 0; i < 2; ++i) {
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d[i] = a[i] * b[0] + c[i];
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}
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#endif
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}
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};
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/////////////////////////////////////////////////////////////////////////////////////////////////
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/// Matrix multiply-add operation
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template <typename LayoutA, typename LayoutB>
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struct Mma<
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gemm::GemmShape<1,2,1>,
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1,
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half_t,
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LayoutA,
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half_t,
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LayoutB,
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half_t,
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layout::RowMajor,
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OpMultiplyAdd> {
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using Shape = gemm::GemmShape<1, 2, 1>;
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using Operator = OpMultiplyAdd;
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CUTLASS_HOST_DEVICE
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void operator()(
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Array<half_t, 2> &d,
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Array<half_t, 1> const &a,
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Array<half_t, 2> const &b,
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Array<half_t, 2> const &c
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) {
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#if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 600))
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__half2 const & A = __half2half2(reinterpret_cast<__half const &>(a));
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__half2 B = reinterpret_cast<__half2 const &>(b);
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__half2 const & C = reinterpret_cast<__half2 const &>(c);
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__half2 D = __hfma2(A, B, C);
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d = reinterpret_cast<Array<half_t, 2> &>(D);
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#else
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CUTLASS_PRAGMA_UNROLL
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for (int i = 0; i < 2; ++i) {
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d[i] = a[0] * b[i] + c[i];
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}
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#endif
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}
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};
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/////////////////////////////////////////////////////////////////////////////////////////////////
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/// Matrix multiply-add operation
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template <>
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struct Mma <
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gemm::GemmShape<2, 2, 1>,
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1,
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half_t,
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layout::ColumnMajor,
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half_t,
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layout::RowMajor,
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half_t,
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layout::ColumnMajor,
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OpMultiplyAdd> {
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using Shape = gemm::GemmShape<2, 2, 1>;
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using Operator = OpMultiplyAdd;
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CUTLASS_HOST_DEVICE
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void operator()(
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Array<half_t, 4> &d,
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Array<half_t, 2> const &a,
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Array<half_t, 2> const &b,
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Array<half_t, 4> const &c
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) {
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#if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 600))
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__half2 const & A = reinterpret_cast<__half2 const &>(a);
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__half2 Blo = __low2half2(reinterpret_cast<__half2 const &>(b));
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__half2 Bhi = __high2half2(reinterpret_cast<__half2 const &>(b));
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__half2 const *C = reinterpret_cast<__half2 const *>(&c);
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__half2 Dlo = __hfma2(A, Blo, C[0]);
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__half2 Dhi = __hfma2(A, Bhi, C[1]);
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Array<half_t, 2> * D = reinterpret_cast<Array<half_t, 2> *>(&d);
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D[0] = reinterpret_cast<Array<half_t, 2> const &>(Dlo);
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D[1] = reinterpret_cast<Array<half_t, 2> const &>(Dhi);
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#else
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CUTLASS_PRAGMA_UNROLL
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for (int j = 0; j < 2; ++j) {
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CUTLASS_PRAGMA_UNROLL
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for (int i = 0; i < 2; ++i) {
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d[i + 2 * j] = a[i] * b[j] + c[i + 2 * j];
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}
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}
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#endif
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}
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};
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/////////////////////////////////////////////////////////////////////////////////////////////////
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/// Matrix multiply-add operation
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template <>
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struct Mma<
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gemm::GemmShape<2, 2, 1>,
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1,
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half_t,
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layout::ColumnMajor,
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half_t,
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layout::RowMajor,
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half_t,
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layout::RowMajor,
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OpMultiplyAdd> {
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using Shape = gemm::GemmShape<2, 2, 1>;
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using Operator = OpMultiplyAdd;
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CUTLASS_HOST_DEVICE
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void operator()(
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Array<half_t, 4> &d,
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Array<half_t, 2> const &a,
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Array<half_t, 2> const &b,
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Array<half_t, 4> const &c
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) {
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#if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 600))
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__half2 Alo = __low2half2(reinterpret_cast<__half2 const &>(a));
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__half2 Ahi = __high2half2(reinterpret_cast<__half2 const &>(a));
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__half2 const & B = reinterpret_cast<__half2 const &>(b);
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__half2 const *C = reinterpret_cast<__half2 const *>(&c);
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__half2 Dlo = __hfma2(Alo, B, C[0]);
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__half2 Dhi = __hfma2(Ahi, B, C[0]);
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Array<half_t, 2> * D = reinterpret_cast<Array<half_t, 2> *>(&d);
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D[0] = reinterpret_cast<Array<half_t, 2> &>(Dlo);
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D[1] = reinterpret_cast<Array<half_t, 2> &>(Dhi);
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#else
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CUTLASS_PRAGMA_UNROLL
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for (int i = 0; i < 2; ++i) {
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CUTLASS_PRAGMA_UNROLL
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for (int j = 0; j < 2; ++j) {
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d[i * 2 + j] = a[i] * b[j] + c[i * 2 + j];
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}
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}
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#endif
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}
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};
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/////////////////////////////////////////////////////////////////////////////////////////////////
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}
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}
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