163 lines
5.3 KiB
C++
163 lines
5.3 KiB
C++
/***************************************************************************************************
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* Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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* SPDX-License-Identifier: BSD-3-Clause
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions are met:
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*
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* 1. Redistributions of source code must retain the above copyright notice, this
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* list of conditions and the following disclaimer.
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*
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* 2. Redistributions in binary form must reproduce the above copyright notice,
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* this list of conditions and the following disclaimer in the documentation
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* and/or other materials provided with the distribution.
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*
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* 3. Neither the name of the copyright holder nor the names of its
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* contributors may be used to endorse or promote products derived from
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* this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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* OR TORT (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 host-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/tensor_view.h"
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#include "cutlass/gemm/gemm.h"
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#include "cutlass/util/reference/device/thread/gemm.h"
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namespace cutlass {
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namespace reference {
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namespace device {
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namespace kernel {
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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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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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typename OutputTile,
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typename InnerProductOp,
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typename ConvertOp
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>
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__global__ 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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TensorRefC tensor_d,
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AccumulatorType initial_accum) {
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// Map each thread to a unique tile of the output matrix
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MatrixCoord output_coord(
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MatrixCoord::Index((threadIdx.x + blockIdx.x * blockDim.x) * OutputTile::kRow),
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MatrixCoord::Index((threadIdx.y + blockIdx.y * blockDim.y) * OutputTile::kColumn)
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);
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// Compute the general matrix product
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thread::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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InnerProductOp,
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ConvertOp
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> gemm(initial_accum);
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gemm.multiply_add(
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problem_size,
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tensor_a,
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tensor_b,
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output_coord);
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gemm.epilogue(problem_size, alpha, beta, tensor_c, tensor_d, output_coord);
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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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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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typename OutputTile,
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typename InnerProductOp,
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typename ConvertOp
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>
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__global__ void BatchedGemm(
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gemm::GemmCoord problem_size,
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ScalarType alpha,
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TensorRefCollectionA tensor_collection_a,
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TensorRefCollectionB tensor_collection_b,
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ScalarType beta,
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TensorRefCollectionC tensor_collection_c,
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AccumulatorType initial_accum) {
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// Obtain batch ID
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int batch_id = blockIdx.z;
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// Dereference based on batch_id
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typename TensorRefCollectionA::TensorRef tensor_a = tensor_collection_a.at(batch_id);
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typename TensorRefCollectionB::TensorRef tensor_b = tensor_collection_b.at(batch_id);
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typename TensorRefCollectionC::TensorRef tensor_c = tensor_collection_c.at(batch_id);
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// Map each thread to a unique tile of the output matrix
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MatrixCoord output_coord(
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(threadIdx.x + blockIdx.x * blockDim.x) * OutputTile::kColumn,
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(threadIdx.y + blockIdx.y * blockDim.y) * OutputTile::kRow
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);
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// Compute the general matrix product
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thread::Gemm<
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typename TensorRefCollectionA::TensorRef,
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typename TensorRefCollectionB::TensorRef,
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typename TensorRefCollectionC::TensorRef,
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ScalarType,
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AccumulatorType,
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OutputTile,
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InnerProductOp,
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ConvertOp
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> gemm(initial_accum);
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gemm.multiply_add(
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problem_size,
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tensor_a,
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tensor_b,
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output_coord);
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gemm.epilogue(problem_size, alpha, beta, tensor_c, output_coord);
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////
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} // namespace kernel
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} // namespace device
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} // namespace reference
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} // namespace cutlass
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