mma_tensor_op.h
Go to the documentation of this file.
/***************************************************************************************************
* 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.
*
**************************************************************************************************/
#pragma once
#include "cutlass/cutlass.h"
#include "cutlass/array.h"
#include "cutlass/numeric_types.h"
#include "cutlass/matrix_shape.h"
#include "cutlass/arch/memory_sm75.h"
#include "cutlass/arch/mma_sm75.h"
#include "cutlass/gemm/gemm.h"
#include "cutlass/gemm/warp/mma.h"
#include "cutlass/gemm/warp/mma_tensor_op_policy.h"
#include "cutlass/gemm/warp/mma_tensor_op_tile_iterator.h"
namespace cutlass {
namespace gemm {
namespace warp {
template <
typename Shape_,
typename ElementA_,
typename LayoutA_,
typename ElementB_,
typename LayoutB_,
typename ElementC_,
typename LayoutC_,
typename Policy_,
int PartitionsK_ = 1,
bool AccumulatorsInRowMajor = false,
int PartitionsN_ = 1,
typename Enable = bool
>
class MmaTensorOp {
public:
using Shape = Shape_;
using ElementA = ElementA_;
using LayoutA = LayoutA_;
using ElementB = ElementB_;
using LayoutB = LayoutB_;
using ElementC = ElementC_;
using LayoutC = LayoutC_;
using Policy = Policy_;
using OperatorClass = arch::OpClassTensorOp;
static int const kThreadCount = 32;
static int const kPartitionsK = PartitionsK_;
static int const kPartitionsN = PartitionsN_;
public:
using IteratorA = MmaTensorOpMultiplicandTileIterator<
MatrixShape<Shape::kM, Shape::kK>, Operand::kA, ElementA, LayoutA,
MatrixShape<Policy::Operator::Shape::kM, Policy::Operator::Shape::kK>,
Policy::OpDelta::kRow, kThreadCount, kPartitionsK>;
using FragmentA = typename IteratorA::Fragment;
using IteratorB = MmaTensorOpMultiplicandTileIterator<
MatrixShape<Shape::kK, Shape::kN>, Operand::kB, ElementB, LayoutB,
MatrixShape<Policy::Operator::Shape::kK, Policy::Operator::Shape::kN>,
Policy::OpDelta::kRow, kThreadCount, kPartitionsK>;
using FragmentB = typename IteratorB::Fragment;
using IteratorC = MmaTensorOpAccumulatorTileIterator<
MatrixShape<Shape::kM, Shape::kN>, ElementC, LayoutC,
typename Policy::Operator::Shape, typename Policy::OpDelta>;
using FragmentC = typename IteratorC::Fragment;
private:
static_assert(
!(Shape::kM % Policy::Operator::Shape::kM) &&
!(Shape::kN % Policy::Operator::Shape::kN),
"Shape of warp-level Mma must be divisible by operator shape.");
using MmaIterations = MatrixShape<
Shape::kM / Policy::Operator::Shape::kM,
(Shape::kN / Policy::Operator::Shape::kN / kPartitionsN > 0) ?
Shape::kN / Policy::Operator::Shape::kN / kPartitionsN :
1
>;
public:
typename Policy::Operator mma;
public:
//
// Methods
//
CUTLASS_DEVICE
MmaTensorOp() {}
CUTLASS_DEVICE
void operator()(
FragmentC &D,
FragmentA const &A,
FragmentB const &B,
FragmentC const &C,
int const &partitionN_idx = 0) const {
using MmaOperandA = typename Policy::Operator::FragmentA;
using MmaOperandB = typename Policy::Operator::FragmentB;
using MmaOperandC = typename Policy::Operator::FragmentC;
D = C;
MmaOperandA const *ptr_A = reinterpret_cast<MmaOperandA const *>(&A);
MmaOperandB const *ptr_B = reinterpret_cast<MmaOperandB const *>(&B);
MmaOperandC *ptr_D = reinterpret_cast<MmaOperandC *>(&D);
// The offset of multilicand B for current partition
const int n_off = partitionN_idx * FragmentB::kElements / MmaOperandB::kElements / kPartitionsN;
// Serpentine visitation order maximizing reuse of Rb
CUTLASS_PRAGMA_UNROLL
for (int n = 0; n < MmaIterations::kColumn; ++n) {
CUTLASS_PRAGMA_UNROLL
for (int m = 0; m < MmaIterations::kRow; ++m) {
int m_serpentine = ((n % 2) ? (MmaIterations::kRow - 1 - m) : m);
if (AccumulatorsInRowMajor) { // matrix B is reordered
mma(
ptr_D[n + m_serpentine * MmaIterations::kColumn],
ptr_A[m_serpentine],
ptr_B[n],
ptr_D[n + m_serpentine * MmaIterations::kColumn]);
} else {
mma(
ptr_D[m_serpentine + (n + n_off) * MmaIterations::kRow],
ptr_A[m_serpentine],
ptr_B[n + n_off],
ptr_D[m_serpentine + (n + n_off) * MmaIterations::kRow]);
}
}
}
}
};
} // namespace warp
} // namespace gemm
} // namespace cutlass
cutlass::MatrixShape
Describes the size of a matrix tile.
Definition: matrix_shape.h:42
cutlass::gemm::warp::MmaTensorOp::FragmentA
typename IteratorA::Fragment FragmentA
Storage for A tile.
Definition: mma_tensor_op.h:129
cutlass
Definition: aligned_buffer.h:35
cutlass::gemm::warp::MmaTensorOp::LayoutB
LayoutB_ LayoutB
Layout of multiplicand B.
Definition: mma_tensor_op.h:97
cutlass::gemm::warp::MmaTensorOp::MmaTensorOp
CUTLASS_DEVICE MmaTensorOp()
Ctor.
Definition: mma_tensor_op.h:176
memory_sm75.h
Architecture-specific operators on memory added for SM75.
mma_tensor_op_tile_iterator.h
Defines iterators used by warp-level matrix multiply operations targeting Tensor Cores.
gemm.h
Defines common types used for all GEMM-like operators.
cutlass::gemm::warp::MmaTensorOp::kThreadCount
static int const kThreadCount
Number of threads participating in warp-level matrix product.
Definition: mma_tensor_op.h:112
cutlass::gemm::warp::MmaTensorOp::kPartitionsN
static int const kPartitionsN
PartitionsN indicating how many PartitionsN for multiplicand B.
Definition: mma_tensor_op.h:118
cutlass::gemm::warp::MmaTensorOp
Structure to compute the matrix product targeting CUDA cores and SIMT math instructions.
Definition: mma_tensor_op.h:82
cutlass::gemm::warp::MmaTensorOp::LayoutA
LayoutA_ LayoutA
Layout of multiplicand A.
Definition: mma_tensor_op.h:91
cutlass::gemm::warp::MmaTensorOp::FragmentB
typename IteratorB::Fragment FragmentB
Storage for B tile.
Definition: mma_tensor_op.h:138
array.h
Statically sized array of elements that accommodates all CUTLASS-supported numeric types and is safe ...
CUTLASS_PRAGMA_UNROLL
#define CUTLASS_PRAGMA_UNROLL
Definition: cutlass.h:110
mma.h
Templates exposing architecture support for warp-level multiply-add operations.
cutlass::gemm::Operand::kA
matrix_shape.h
Defines a Shape template for matrix tiles.
cutlass::gemm::warp::MmaTensorOp::FragmentC
typename IteratorC::Fragment FragmentC
Storage for C tile.
Definition: mma_tensor_op.h:146
cutlass::gemm::warp::MmaTensorOpAccumulatorTileIterator
Definition: mma_tensor_op_tile_iterator.h:1794
cutlass::gemm::warp::MmaTensorOp::operator()
CUTLASS_DEVICE void operator()(FragmentC &D, FragmentA const &A, FragmentB const &B, FragmentC const &C, int const &partitionN_idx=0) const
Performs a warp-level matrix multiply-accumulate operation.
Definition: mma_tensor_op.h:180
cutlass::gemm::warp::MmaTensorOp::mma
Policy::Operator mma
Underlying matrix multiply operator (concept: arch::Mma)
Definition: mma_tensor_op.h:166
cutlass::gemm::warp::MmaTensorOp::ElementC
ElementC_ ElementC
Data type of accumulator matrix C.
Definition: mma_tensor_op.h:100
numeric_types.h
Top-level include for all CUTLASS numeric types.
cutlass::gemm::warp::MmaTensorOpMultiplicandTileIterator
Definition: mma_tensor_op_tile_iterator.h:75
static_assert
#define static_assert(__e, __m)
Definition: platform.h:153
cutlass::gemm::warp::MmaTensorOp::LayoutC
LayoutC_ LayoutC
Layout of accumulator matrix C.
Definition: mma_tensor_op.h:103
cutlass::gemm::warp::MmaTensorOp::Policy
Policy_ Policy
Shape of the warp in units of thread (concept: MmaLanePolicySimt)
Definition: mma_tensor_op.h:106
cutlass::gemm::warp::MmaTensorOp::Shape
Shape_ Shape
Shape of warp-level matrix operation (concept: GemmShape)
Definition: mma_tensor_op.h:85
cutlass::gemm::warp::MmaTensorOp::ElementB
ElementB_ ElementB
Data type of multiplicand B.
Definition: mma_tensor_op.h:94
cutlass::gemm::warp::MmaTensorOp::kPartitionsK
static int const kPartitionsK
Number of partitions along K dimension.
Definition: mma_tensor_op.h:115
cutlass::gemm::warp::MmaTensorOp::OperatorClass
arch::OpClassTensorOp OperatorClass
Indicates class of matrix operator.
Definition: mma_tensor_op.h:109
cutlass::gemm::warp::MmaTensorOp::ElementA
ElementA_ ElementA
Data type of multiplicand A.
Definition: mma_tensor_op.h:88
mma_sm75.h
Matrix multiply for SM75.
cutlass::gemm::Operand::kB
A multiplicand.
cutlass.h
Basic include for CUTLASS.
mma_tensor_op_policy.h
Policy describing implementation details of warp-level GEMM targeting Tensor Cores.