CUTLASS 1.2
This commit is contained in:
akerr
@@ -0,0 +1,224 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017-2018, 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 device-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/device/kernel/gemm.h"
|
||||
|
||||
namespace cutlass {
|
||||
namespace reference {
|
||||
namespace device {
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
/// 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");
|
||||
|
||||
// Blocking structure potentially improves performance of reference implementation
|
||||
// with a minor increase in complexity.
|
||||
//
|
||||
// Note, this reference implementation is NOT expected to approach peak performance.
|
||||
typedef Shape<1, 4, 4> OutputTile;
|
||||
|
||||
dim3 block(16, 8);
|
||||
dim3 grid(
|
||||
(problem_size.m() + block.x * OutputTile::kW - 1) / (block.x * OutputTile::kW),
|
||||
(problem_size.n() + block.y * OutputTile::kH - 1) / (block.y * OutputTile::kH)
|
||||
);
|
||||
|
||||
// Launch a GEMM kernel
|
||||
kernel::Gemm<
|
||||
TensorRefA,
|
||||
TensorRefB,
|
||||
TensorRefC,
|
||||
ScalarType,
|
||||
AccumulatorType,
|
||||
OutputTile
|
||||
><<< grid, block >>>(
|
||||
problem_size,
|
||||
alpha,
|
||||
tensor_a,
|
||||
tensor_b,
|
||||
beta,
|
||||
tensor_c,
|
||||
initial_accum
|
||||
);
|
||||
}
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
/// 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 tensor_a,
|
||||
TensorRefCollectionB tensor_b,
|
||||
ScalarType beta,
|
||||
TensorRefCollectionC tensor_c,
|
||||
AccumulatorType initial_accum) {
|
||||
|
||||
typedef typename TensorRefCollectionA::Storage AType;
|
||||
typedef typename TensorRefCollectionB::Storage BType;
|
||||
typedef typename TensorRefCollectionC::Storage CType;
|
||||
|
||||
static_assert(
|
||||
TensorRefCollectionA::kRank == 2 &&
|
||||
TensorRefCollectionB::kRank == 2 &&
|
||||
TensorRefCollectionC::kRank == 2, "Tensors must be of rank 2");
|
||||
|
||||
// Blocking structure potentially improves performance of reference implementation
|
||||
// with a minor increase in complexity.
|
||||
//
|
||||
// Note, this reference implementation is NOT expected to approach peak performance.
|
||||
typedef Shape<1, 4, 4> OutputTile;
|
||||
|
||||
dim3 block(16, 8);
|
||||
dim3 grid(
|
||||
(problem_size.m() + block.x * OutputTile::kW - 1) / (block.x * OutputTile::kW),
|
||||
(problem_size.n() + block.y * OutputTile::kH - 1) / (block.y * OutputTile::kH),
|
||||
problem_size.batch()
|
||||
);
|
||||
|
||||
// Launch a GEMM kernel
|
||||
kernel::BatchedGemm<
|
||||
TensorRefCollectionA,
|
||||
TensorRefCollectionB,
|
||||
TensorRefCollectionC,
|
||||
ScalarType,
|
||||
AccumulatorType,
|
||||
OutputTile
|
||||
><<< grid, block >>>(
|
||||
problem_size,
|
||||
alpha,
|
||||
tensor_a,
|
||||
tensor_b,
|
||||
beta,
|
||||
tensor_c,
|
||||
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 tensor_a,
|
||||
TensorRefCollectionB 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
|
||||
Reference in New Issue
Block a user