877bdcace6
CUTLASS 1.3 Release - Efficient GEMM kernel targeting Volta Tensor Cores via mma.sync instruction added in CUDA 10.1.
226 lines
7.3 KiB
C++
226 lines
7.3 KiB
C++
/***************************************************************************************************
|
|
* Copyright (c) 2017-2019, NVIDIA CORPORATION. All rights reserved.
|
|
*
|
|
* Redistribution and use in source and binary forms, with or without modification, are permitted
|
|
* provided that the following conditions are met:
|
|
* * Redistributions of source code must retain the above copyright notice, this list of
|
|
* conditions and the following disclaimer.
|
|
* * Redistributions in binary form must reproduce the above copyright notice, this list of
|
|
* conditions and the following disclaimer in the documentation and/or other materials
|
|
* provided with the distribution.
|
|
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
|
|
* to endorse or promote products derived from this software without specific prior written
|
|
* permission.
|
|
*
|
|
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
|
|
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
|
|
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
|
|
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
|
|
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
|
|
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
|
|
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
|
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
|
*
|
|
**************************************************************************************************/
|
|
/*! \file
|
|
\brief Reference implementation for GEMM in host-side code.
|
|
*/
|
|
|
|
#pragma once
|
|
|
|
#include "cutlass/coord.h"
|
|
#include "cutlass/matrix_traits.h"
|
|
#include "cutlass/tensor_view.h"
|
|
#include "cutlass/gemm/gemm_coord.h"
|
|
|
|
#include "tools/util/reference/detail/inner_product.h"
|
|
|
|
namespace cutlass {
|
|
namespace reference {
|
|
namespace host {
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
/// Computes a general matrix product among matrices (tensors of rank=2) pointed to by TensorRef
|
|
/// objects.
|
|
///
|
|
/// Explicitly naming types needed by this template can be cumbersome, particularly for the
|
|
/// accumulator type, so a function argument 'initial_accum' is exposed. Passing
|
|
/// AccumulatorType(0) as the last function argument can be easier than naming all template
|
|
/// arguments explicitly.
|
|
template <
|
|
typename TensorRefA,
|
|
typename TensorRefB,
|
|
typename TensorRefC,
|
|
typename ScalarType,
|
|
typename AccumulatorType
|
|
>
|
|
void Gemm(
|
|
gemm::GemmCoord problem_size,
|
|
ScalarType alpha,
|
|
TensorRefA tensor_a,
|
|
TensorRefB tensor_b,
|
|
ScalarType beta,
|
|
TensorRefC tensor_c,
|
|
AccumulatorType initial_accum) {
|
|
|
|
typedef typename TensorRefA::Storage AType;
|
|
typedef typename TensorRefB::Storage BType;
|
|
typedef typename TensorRefC::Storage CType;
|
|
|
|
static_assert(
|
|
TensorRefA::kRank == 2 &&
|
|
TensorRefB::kRank == 2 &&
|
|
TensorRefC::kRank == 2, "Tensors must be of rank 2");
|
|
|
|
// Note: batch is ignored.
|
|
int const M = problem_size.m();
|
|
int const N = problem_size.n();
|
|
int const K = problem_size.k();
|
|
|
|
// Blocking necessary to speedup reference implementation
|
|
int const Mblock = 32;
|
|
int const Nblock = 32;
|
|
|
|
for (int row_block = 0; row_block < M; row_block += Mblock) {
|
|
for (int col_block = 0; col_block < N; col_block += Nblock) {
|
|
AccumulatorType accum[Mblock][Nblock];
|
|
|
|
for (int j = 0; j < Nblock; j++) {
|
|
for (int i = 0; i < Mblock; i++) {
|
|
accum[i][j] = initial_accum;
|
|
}
|
|
}
|
|
|
|
for (int k_block = 0; k_block < K; ++k_block) {
|
|
for (int j = 0; j < Nblock; j++) {
|
|
for (int i = 0; i < Mblock; i++) {
|
|
int row = row_block + i;
|
|
int col = col_block + j;
|
|
|
|
if (row < M && col < N) {
|
|
AType a = tensor_a.at(MatrixCoord(row, k_block));
|
|
BType b = tensor_b.at(MatrixCoord(k_block, col));
|
|
|
|
accum[i][j] = cutlass::reference::detail::inner_product(a, b, accum[i][j]);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
for (int j = 0; j < Nblock; j++) {
|
|
for (int i = 0; i < Mblock; i++) {
|
|
int row = row_block + i;
|
|
int col = col_block + j;
|
|
|
|
MatrixCoord coord = MatrixCoord(row, col);
|
|
if (row < M && col < N) {
|
|
|
|
tensor_c.at(coord) = cutlass::reference::detail::Cast<ScalarType, CType>::apply(
|
|
alpha * ScalarType(accum[i][j]) +
|
|
beta * ScalarType(tensor_c.at(coord)));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
/// Computes a general matrix product among matrices (tensors of rank=2) pointed to by TensorRef
|
|
/// objects.
|
|
///
|
|
/// This assumes the accumulator type is the same type as the scalars.
|
|
template <
|
|
typename TensorRefA,
|
|
typename TensorRefB,
|
|
typename TensorRefC,
|
|
typename ScalarType
|
|
>
|
|
void Gemm(
|
|
gemm::GemmCoord problem_size,
|
|
ScalarType alpha,
|
|
TensorRefA tensor_a,
|
|
TensorRefB tensor_b,
|
|
ScalarType beta,
|
|
TensorRefC tensor_c) {
|
|
|
|
Gemm(problem_size, alpha, tensor_a, tensor_b, beta, tensor_c, ScalarType(0));
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
//
|
|
// Batched GEMM
|
|
//
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
/// Computes a batch of GEMMs over a set of matrices of common dimension.
|
|
//
|
|
// TensorRefCollection* is a type satisfying the TensorRefCollection concept.
|
|
//
|
|
template <
|
|
typename TensorRefCollectionA,
|
|
typename TensorRefCollectionB,
|
|
typename TensorRefCollectionC,
|
|
typename ScalarType,
|
|
typename AccumulatorType
|
|
>
|
|
void BatchedGemm(
|
|
gemm::GemmCoord problem_size,
|
|
ScalarType alpha,
|
|
TensorRefCollectionA const& tensor_a,
|
|
TensorRefCollectionB const& tensor_b,
|
|
ScalarType beta,
|
|
TensorRefCollectionC &tensor_c,
|
|
AccumulatorType initial_accum) {
|
|
|
|
typename TensorRefCollectionA::ConstIterator tensor_a_it = tensor_a.begin();
|
|
typename TensorRefCollectionB::ConstIterator tensor_b_it = tensor_b.begin();
|
|
typename TensorRefCollectionC::ConstIterator tensor_c_it = tensor_c.begin();
|
|
|
|
for (int batch = 0;
|
|
batch < problem_size.batch();
|
|
++batch, ++tensor_a_it, ++tensor_b_it, ++tensor_c_it) {
|
|
|
|
Gemm(
|
|
problem_size,
|
|
alpha,
|
|
*tensor_a_it,
|
|
*tensor_b_it,
|
|
beta,
|
|
*tensor_c_it,
|
|
initial_accum);
|
|
}
|
|
}
|
|
|
|
/// Computes a general matrix product among matrices (tensors of rank=2) pointed to by TensorRef
|
|
/// objects.
|
|
//
|
|
// TensorRefCollection* is a type satisfying the TensorRefCollection concept.
|
|
//
|
|
template <
|
|
typename TensorRefCollectionA,
|
|
typename TensorRefCollectionB,
|
|
typename TensorRefCollectionC,
|
|
typename ScalarType,
|
|
typename AccumulatorType
|
|
>
|
|
void BatchedGemm(
|
|
gemm::GemmCoord problem_size,
|
|
ScalarType alpha,
|
|
TensorRefCollectionA const& tensor_a,
|
|
TensorRefCollectionB const& tensor_b,
|
|
ScalarType beta,
|
|
TensorRefCollectionC &tensor_c) {
|
|
|
|
BatchedGemm(problem_size, alpha, tensor_a, tensor_b, beta, tensor_c, ScalarType(0));
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
|
|
} // namespace host
|
|
} // namespace reference
|
|
} // namespace cutlass
|