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default_mma_with_reduction.h
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/***************************************************************************************************
* Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* 1. Redistributions of source code must retain the above copyright notice, this
* list of conditions and the following disclaimer.
*
* 2. 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.
*
* 3. Neither the name of the copyright holder 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 THE COPYRIGHT HOLDER OR CONTRIBUTORS 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 TORT (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 Template for a pipelined GEMM kernel. Does not compute batching or support split-K.
*/
#pragma once
#include "cutlass/cutlass.h"
#include "cutlass/numeric_types.h"
#include "cutlass/arch/arch.h"
#include "cutlass/layout/matrix.h"
#include "cutlass/transform/threadblock/predicated_tile_iterator.h"
#include "cutlass/transform/threadblock/predicated_tile_iterator_2dthreadtile.h"
#include "cutlass/gemm/threadblock/default_mma_core_with_reduction.h"
////////////////////////////////////////////////////////////////////////////////
namespace cutlass {
namespace gemm {
namespace threadblock {
////////////////////////////////////////////////////////////////////////////////
template <
/// Element type for A matrix operand
typename ElementA,
/// Layout type for A matrix operand
typename LayoutA,
/// Access granularity of A matrix in units of elements
int kAlignmentA,
/// Element type for B matrix operand
typename ElementB,
/// Layout type for B matrix operand
typename LayoutB,
/// Access granularity of B matrix in units of elements
int kAlignmentB,
/// Element type for internal accumulation
typename ElementAccumulator,
/// Layout type for C and D matrix operands
typename LayoutC,
/// Operator class tag
typename OperatorClass,
///
bool ReduceKForA_,
/// Tag indicating architecture to tune for
typename ArchTag,
/// Threadblock-level tile size (concept: GemmShape)
typename ThreadblockShape,
/// Warp-level tile size (concept: GemmShape)
typename WarpShape,
/// Instruction-level tile size (concept: GemmShape)
typename InstructionShape,
/// Number of stages used in the pipelined mainloop
int Stages,
/// Operation perfomed by GEMM
typename Operator,
/// Store the accumulators in row major or column major. Row major is used
/// when output layout is interleaved.
bool AccumulatorsInRowMajor = false,
/// Use zfill or predicate for SM80 out-of-bound cp.async
SharedMemoryClearOption SharedMemoryClear = SharedMemoryClearOption::kNone
>
struct DefaultMmaWithReduction {
static cutlass::arch::CacheOperation::Kind const CacheOpA =
((sizeof_bits<ElementA>::value * kAlignmentA) == 128)
? cutlass::arch::CacheOperation::Global
: cutlass::arch::CacheOperation::Always;
static cutlass::arch::CacheOperation::Kind const CacheOpB =
((sizeof_bits<ElementB>::value * kAlignmentB) == 128)
? cutlass::arch::CacheOperation::Global
: cutlass::arch::CacheOperation::Always;
// Define the MmaCore components
using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaWithReductionCore<
ThreadblockShape, WarpShape, InstructionShape, ElementA, LayoutA,
ElementB, LayoutB, ElementAccumulator, layout::RowMajor, arch::OpClassTensorOp,
ReduceKForA_, Stages, Operator, false, CacheOpA, CacheOpB>;
// Define iterators over tiles from the A operand
using ThreadMapA = typename MmaCore::IteratorThreadMapA;
using AccessTypeA = cutlass::Array<ElementA, kAlignmentA>;
using IteratorA =
cutlass::transform::threadblock::PredicatedTileAccessIterator<
cutlass::MatrixShape<ThreadblockShape::kM, ThreadblockShape::kK>,
ElementA, LayoutA, 1, ThreadMapA, AccessTypeA>;
// Define iterators over tiles from the B operand
using ThreadMapB = typename MmaCore::IteratorThreadMapB;
using AccessTypeB = cutlass::Array<ElementB, kAlignmentB>;
using IteratorB =
cutlass::transform::threadblock::PredicatedTileAccessIterator<
cutlass::MatrixShape<ThreadblockShape::kK, ThreadblockShape::kN>,
ElementB, LayoutB, 0, ThreadMapB, AccessTypeB>;
// Define the threadblock-scoped multistage matrix multiply
using ThreadblockMma = cutlass::gemm::threadblock::MmaWithReductionMultistage<
typename MmaCore::Shape, IteratorA, typename MmaCore::SmemIteratorA,
MmaCore::kCacheOpA, IteratorB, typename MmaCore::SmemIteratorB,
MmaCore::kCacheOpB, ElementAccumulator, layout::RowMajor,
typename MmaCore::MmaPolicy, Stages, SharedMemoryClear>;
};
////////////////////////////////////////////////////////////////////////////////
} // namespace threadblock
} // namespace gemm
} // namespace cutlass
////////////////////////////////////////////////////////////////////////////////