877bdcace6
CUTLASS 1.3 Release - Efficient GEMM kernel targeting Volta Tensor Cores via mma.sync instruction added in CUDA 10.1.
255 lines
9.8 KiB
C++
255 lines
9.8 KiB
C++
/***************************************************************************************************
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* Copyright (c) 2017-2019, 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 Reference implementation for split-complex GEMM in device-side code.
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*/
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#pragma once
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#include "cutlass/coord.h"
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#include "cutlass/matrix_traits.h"
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#include "cutlass/tensor_view.h"
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#include "cutlass/gemm/gemm_coord.h"
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#include "cutlass/util/complex.h"
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namespace cutlass {
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namespace reference {
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namespace host {
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////////////////////////////////////////////////////////////////////////////////////////////////////
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/// Computes a complex-valued GEMM whose operands are in the split-complex format.
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template <
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typename TensorRefA, /// concept: ZipTensorRef
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typename TensorRefB, /// concept: ZipTensorRef
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typename TensorRefC, /// concept: ZipTensorRef
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typename ScalarType, /// real-valued type underlying complex scalars
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typename AccumulatorType /// real-valued type underlying complex accumulators
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>
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void SplitComplexGemm(
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gemm::GemmCoord problem_size,
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platform::complex<ScalarType> alpha,
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TensorRefA tensor_a,
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TensorRefB tensor_b,
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platform::complex<ScalarType> beta,
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TensorRefC tensor_c,
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platform::complex<AccumulatorType> initial_accum) {
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typedef typename TensorRefA::First::Storage AType;
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typedef typename TensorRefB::First::Storage BType;
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typedef typename TensorRefC::First::Storage CType;
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typedef platform::complex<AType> ComplexAType;
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typedef platform::complex<BType> ComplexBType;
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typedef platform::complex<CType> ComplexCType;
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typedef platform::complex<ScalarType> ComplexScalarType;
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typedef platform::complex<AccumulatorType> ComplexAccumulatorType;
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static_assert(
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TensorRefA::First::kRank == 2 && TensorRefA::Second::kRank == 2 &&
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TensorRefB::First::kRank == 2 && TensorRefB::Second::kRank == 2 &&
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TensorRefC::First::kRank == 2 && TensorRefC::Second::kRank == 2,
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"Tensors must be of rank 2");
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// Note: batch is ignored.
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int const M = problem_size.m();
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int const N = problem_size.n();
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int const K = problem_size.k();
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// Blocking necessary to speedup reference implementation
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int const Mblock = 32;
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int const Nblock = 32;
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for (int row_block = 0; row_block < M; row_block += Mblock) {
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for (int col_block = 0; col_block < N; col_block += Nblock) {
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ComplexAccumulatorType accum[Mblock][Nblock];
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for (int j = 0; j < Nblock; j++) {
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for (int i = 0; i < Mblock; i++) {
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accum[i][j] = initial_accum;
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}
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}
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for (int k_block = 0; k_block < K; ++k_block) {
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for (int j = 0; j < Nblock; j++) {
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for (int i = 0; i < Mblock; i++) {
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int row = row_block + i;
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int col = col_block + j;
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if (row < M && col < N) {
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ComplexAType a(
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tensor_a.first.at(MatrixCoord(row, k_block)),
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tensor_a.second.at(MatrixCoord(row, k_block))
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);
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ComplexBType b(
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tensor_b.first.at(MatrixCoord(k_block, col)),
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tensor_b.second.at(MatrixCoord(k_block, col))
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);
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accum[i][j] = detail::inner_product(a, b, accum[i][j]);
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}
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}
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}
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}
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for (int j = 0; j < Nblock; j++) {
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for (int i = 0; i < Mblock; i++) {
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int row = row_block + i;
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int col = col_block + j;
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MatrixCoord coord = MatrixCoord(row, col);
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if (row < M && col < N) {
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ComplexScalarType product(
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detail::Cast<AccumulatorType, ScalarType>::apply(accum[i][j].real()),
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detail::Cast<AccumulatorType, ScalarType>::apply(accum[i][j].imag())
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);
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ComplexScalarType source(
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detail::Cast<CType, ScalarType>::apply(tensor_c.first.at(coord)),
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detail::Cast<CType, ScalarType>::apply(tensor_c.second.at(coord))
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);
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ComplexScalarType result = alpha * product + beta * source;
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tensor_c.first.at(coord) = detail::Cast<ScalarType, CType>::apply(result.real());
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tensor_c.second.at(coord) = detail::Cast<ScalarType, CType>::apply(result.imag());
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}
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}
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}
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}
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}
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////
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/// Computes a complex-valued GEMM whose operands are in the split-complex format.
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template <
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typename TensorRefA, /// concept: ZipTensorRef
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typename TensorRefB, /// concept: ZipTensorRef
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typename TensorRefC, /// concept: ZipTensorRef
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typename ScalarType, /// real-valued type underlying complex scalars
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typename AccumulatorType /// real-valued type underlying complex accumulators
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>
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void SplitComplexGemm(
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gemm::GemmCoord problem_size,
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platform::complex<ScalarType> alpha,
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TensorRefA tensor_a,
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TensorRefB tensor_b,
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platform::complex<ScalarType> beta,
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TensorRefC tensor_c) {
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return SplitComplexGemm(problem_size, alpha, tensor_a, tensor_b,beta, tensor_c, ScalarType(0));
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////
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//
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// Batched Split-Complex GEMM
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//
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////////////////////////////////////////////////////////////////////////////////////////////////////
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/// Computes a complex-valued GEMM whose operands are in the split-complex format.
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template <
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typename TensorRefCollectionA, /// concept: Pair<TensorRefCollection, TensorRefCollection>
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typename TensorRefCollectionB, /// concept: Pair<TensorRefCollection, TensorRefCollection>
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typename TensorRefCollectionC, /// concept: Pair<TensorRefCollection, TensorRefCollection>
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typename ScalarType, /// real-valued type underlying complex scalars
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typename AccumulatorType /// real-valued type underlying complex accumulators
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>
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void BatchedSplitComplexGemm(
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gemm::GemmCoord problem_size,
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platform::complex<ScalarType> alpha,
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TensorRefCollectionA tensor_a,
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TensorRefCollectionB tensor_b,
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platform::complex<ScalarType> beta,
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TensorRefCollectionC tensor_c,
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platform::complex<AccumulatorType> initial_accum) {
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typename TensorRefCollectionA::ConstIterator tensor_a_real = tensor_a.first.begin();
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typename TensorRefCollectionA::ConstIterator tensor_a_imag = tensor_a.second.begin();
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typename TensorRefCollectionB::ConstIterator tensor_b_real = tensor_b.first.begin();
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typename TensorRefCollectionB::ConstIterator tensor_b_imag = tensor_b.second.begin();
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typename TensorRefCollectionC::ConstIterator tensor_c_real = tensor_c.first.begin();
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typename TensorRefCollectionC::ConstIterator tensor_c_imag = tensor_c.second.begin();
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for (int batch = 0; batch < problem_size.batch(); ++batch) {
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SplitComplexGemm(
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problem_size,
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alpha,
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make_ZipTensorRef(*tensor_a_real, *tensor_a_imag),
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make_ZipTensorRef(*tensor_b_real, *tensor_b_imag),
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beta,
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make_ZipTensorRef(*tensor_c_real, *tensor_c_imag),
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initial_accum);
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++tensor_a_real;
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++tensor_a_imag;
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++tensor_b_real;
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++tensor_b_imag;
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++tensor_c_real;
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++tensor_c_imag;
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}
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////
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/// Computes a complex-valued GEMM whose operands are in the split-complex format.
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template <
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typename TensorRefCollectionA, /// concept: pair<TensorRefCollection, TensorRefCollection>
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typename TensorRefCollectionB, /// concept: pair<TensorRefCollection, TensorRefCollection>
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typename TensorRefCollectionC, /// concept: pair<TensorRefCollection, TensorRefCollection>
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typename ScalarType, /// real-valued type underlying complex scalars
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typename AccumulatorType /// real-valued type underlying complex accumulators
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>
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void BatchedSplitComplexGemm(
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gemm::GemmCoord problem_size,
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platform::complex<ScalarType> alpha,
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TensorRefCollectionA tensor_a,
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TensorRefCollectionB tensor_b,
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platform::complex<ScalarType> beta,
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TensorRefCollectionC tensor_c) {
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BatchedSplitComplexGemm(
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problem_size,
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alpha,
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tensor_a,
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tensor_b,
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beta,
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tensor_c,
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platform::complex<ScalarType>(0, 0));
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////
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} // namespace host
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} // namespace reference
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} // namespace cutlass
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