877bdcace6
CUTLASS 1.3 Release - Efficient GEMM kernel targeting Volta Tensor Cores via mma.sync instruction added in CUDA 10.1.
225 lines
7.1 KiB
C++
225 lines
7.1 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 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 "tools/util/reference/device/kernel/gemm.h"
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namespace cutlass {
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namespace reference {
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namespace device {
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////////////////////////////////////////////////////////////////////////////////////////////////////
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/// Computes a general matrix product among matrices (tensors of rank=2) pointed to by TensorRef
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/// objects.
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///
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/// Explicitly naming types needed by this template can be cumbersome, particularly for the
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/// accumulator type, so a function argument 'initial_accum' is exposed. Passing
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/// AccumulatorType(0) as the last function argument can be easier than naming all template
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/// arguments explicitly.
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template <
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typename TensorRefA,
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typename TensorRefB,
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typename TensorRefC,
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typename ScalarType,
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typename AccumulatorType
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>
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void Gemm(
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gemm::GemmCoord problem_size,
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ScalarType alpha,
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TensorRefA tensor_a,
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TensorRefB tensor_b,
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ScalarType beta,
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TensorRefC tensor_c,
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AccumulatorType initial_accum) {
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typedef typename TensorRefA::Storage AType;
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typedef typename TensorRefB::Storage BType;
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typedef typename TensorRefC::Storage CType;
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static_assert(
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TensorRefA::kRank == 2 &&
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TensorRefB::kRank == 2 &&
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TensorRefC::kRank == 2, "Tensors must be of rank 2");
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// Blocking structure potentially improves performance of reference implementation
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// with a minor increase in complexity.
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//
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// Note, this reference implementation is NOT expected to approach peak performance.
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typedef Shape<1, 4, 4> OutputTile;
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dim3 block(16, 8);
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dim3 grid(
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(problem_size.m() + block.x * OutputTile::kW - 1) / (block.x * OutputTile::kW),
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(problem_size.n() + block.y * OutputTile::kH - 1) / (block.y * OutputTile::kH)
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);
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// Launch a GEMM kernel
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kernel::Gemm<
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TensorRefA,
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TensorRefB,
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TensorRefC,
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ScalarType,
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AccumulatorType,
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OutputTile
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><<< grid, block >>>(
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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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initial_accum
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);
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////
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/// Computes a general matrix product among matrices (tensors of rank=2) pointed to by TensorRef
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/// objects.
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///
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/// This assumes the accumulator type is the same type as the scalars.
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template <
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typename TensorRefA,
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typename TensorRefB,
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typename TensorRefC,
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typename ScalarType
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>
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void Gemm(
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gemm::GemmCoord problem_size,
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ScalarType alpha,
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TensorRefA tensor_a,
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TensorRefB tensor_b,
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ScalarType beta,
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TensorRefC tensor_c) {
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Gemm(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 GEMM
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//
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////////////////////////////////////////////////////////////////////////////////////////////////////
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/// Computes a batch of GEMMs over a set of matrices of common dimension.
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//
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// TensorRefCollection* is a type satisfying the TensorRefCollection concept.
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//
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template <
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typename TensorRefCollectionA,
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typename TensorRefCollectionB,
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typename TensorRefCollectionC,
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typename ScalarType,
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typename AccumulatorType
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>
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void BatchedGemm(
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gemm::GemmCoord problem_size,
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ScalarType alpha,
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TensorRefCollectionA tensor_a,
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TensorRefCollectionB tensor_b,
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ScalarType beta,
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TensorRefCollectionC tensor_c,
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AccumulatorType initial_accum) {
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typedef typename TensorRefCollectionA::Storage AType;
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typedef typename TensorRefCollectionB::Storage BType;
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typedef typename TensorRefCollectionC::Storage CType;
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static_assert(
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TensorRefCollectionA::kRank == 2 &&
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TensorRefCollectionB::kRank == 2 &&
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TensorRefCollectionC::kRank == 2, "Tensors must be of rank 2");
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// Blocking structure potentially improves performance of reference implementation
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// with a minor increase in complexity.
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//
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// Note, this reference implementation is NOT expected to approach peak performance.
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typedef Shape<1, 4, 4> OutputTile;
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dim3 block(16, 8);
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dim3 grid(
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(problem_size.m() + block.x * OutputTile::kW - 1) / (block.x * OutputTile::kW),
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(problem_size.n() + block.y * OutputTile::kH - 1) / (block.y * OutputTile::kH),
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problem_size.batch()
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);
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// Launch a GEMM kernel
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kernel::BatchedGemm<
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TensorRefCollectionA,
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TensorRefCollectionB,
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TensorRefCollectionC,
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ScalarType,
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AccumulatorType,
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OutputTile
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><<< grid, block >>>(
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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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initial_accum
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);
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}
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/// Computes a general matrix product among matrices (tensors of rank=2) pointed to by TensorRef
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/// objects.
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//
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// TensorRefCollection* is a type satisfying the TensorRefCollection concept.
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//
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template <
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typename TensorRefCollectionA,
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typename TensorRefCollectionB,
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typename TensorRefCollectionC,
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typename ScalarType,
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typename AccumulatorType
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>
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void BatchedGemm(
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gemm::GemmCoord problem_size,
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ScalarType alpha,
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TensorRefCollectionA tensor_a,
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TensorRefCollectionB tensor_b,
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ScalarType beta,
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TensorRefCollectionC tensor_c) {
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BatchedGemm(problem_size, alpha, tensor_a, tensor_b, beta, tensor_c, ScalarType(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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