265 lines
8.5 KiB
C++
265 lines
8.5 KiB
C++
/***************************************************************************************************
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* Copyright (c) 2017 - 2023 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 Template for a pipelined GEMM kernel. Does not compute batching or support split-K.
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*/
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#pragma once
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#include "cutlass/cutlass.h"
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#include "cutlass/gemm/gemm.h"
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#include "cutlass/matrix_coord.h"
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/////////////////////////////////////////////////////////////////////////////////////////////////
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namespace cutlass {
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namespace gemm {
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namespace kernel {
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/////////////////////////////////////////////////////////////////////////////////////////////////
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template <
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typename Mma_, ///! Threadblock-scoped matrix multiply-accumulate
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typename Epilogue_, ///! Epilogue
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typename ThreadblockSwizzle_ ///! Threadblock swizzling function
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>
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struct GemmArray {
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using Mma = Mma_;
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using Epilogue = Epilogue_;
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using OutputOp = typename Epilogue::OutputOp;
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using ThreadblockSwizzle = ThreadblockSwizzle_;
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/// Warp count (concept: GemmShape)
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using WarpCount = typename Mma::WarpCount;
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static int const kThreadCount = 32 * WarpCount::kCount;
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/// Parameters structure
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struct Params {
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cutlass::gemm::GemmCoord problem_size;
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cutlass::gemm::GemmCoord grid_tiled_shape;
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int swizzle_log_tile;
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typename Mma::IteratorA::Params params_A;
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typename Mma::IteratorA::Element const * const * ptr_A;
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typename Mma::IteratorB::Params params_B;
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typename Mma::IteratorB::Element const * const * ptr_B;
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typename Epilogue::OutputTileIterator::Params params_C;
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typename Epilogue::OutputTileIterator::Element const * const * ptr_C;
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typename Epilogue::OutputTileIterator::Params params_D;
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typename Epilogue::OutputTileIterator::Element * const * ptr_D;
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int64_t stride_D;
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typename OutputOp::Params epilogue;
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int batch_count;
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int gemm_k_iterations;
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//
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// Methods
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//
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CUTLASS_HOST_DEVICE
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Params() :
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swizzle_log_tile(0) { }
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CUTLASS_HOST_DEVICE
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Params(
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cutlass::gemm::GemmCoord const & problem_size_,
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cutlass::gemm::GemmCoord const & grid_tiled_shape_,
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typename Mma::IteratorA::Element const * const * ptr_A_,
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typename Mma::IteratorA::Layout layout_A,
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typename Mma::IteratorB::Element const * const * ptr_B_,
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typename Mma::IteratorB::Layout layout_B,
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typename Epilogue::OutputTileIterator::Element const * const * ptr_C_,
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typename Epilogue::OutputTileIterator::Layout layout_C,
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typename Epilogue::OutputTileIterator::Element * const * ptr_D_,
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typename Epilogue::OutputTileIterator::Layout layout_D,
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typename OutputOp::Params epilogue_,
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int batch_count_
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):
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problem_size(problem_size_),
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grid_tiled_shape(grid_tiled_shape_),
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swizzle_log_tile(ThreadblockSwizzle().get_log_tile(grid_tiled_shape)),
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params_A(layout_A),
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ptr_A(ptr_A_),
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params_B(layout_B),
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ptr_B(ptr_B_),
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params_C(layout_C),
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ptr_C(ptr_C_),
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params_D(layout_D),
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ptr_D(ptr_D_),
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epilogue(epilogue_),
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batch_count(batch_count_),
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gemm_k_iterations((problem_size.k() + Mma::Shape::kK - 1) / Mma::Shape::kK) {
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}
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};
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/// Shared memory storage structure
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union SharedStorage {
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typename Mma::SharedStorage main_loop;
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typename Epilogue::SharedStorage epilogue;
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};
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//
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// Methods
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//
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CUTLASS_HOST_DEVICE
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GemmArray() { }
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/// Executes one GEMM
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CUTLASS_DEVICE
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void operator()(Params const ¶ms, SharedStorage &shared_storage) {
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// Compute threadblock location
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ThreadblockSwizzle threadblock_swizzle;
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cutlass::gemm::GemmCoord threadblock_tile_offset =
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threadblock_swizzle.get_tile_offset(params.swizzle_log_tile);
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// Early exit if CTA is out of range
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if (params.grid_tiled_shape.m() <= threadblock_tile_offset.m() ||
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params.grid_tiled_shape.n() <= threadblock_tile_offset.n()) {
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return;
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}
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// Each CTA handles multiple batch indices to accommodate limited range of CUDA grid's Z dimension
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for (int batch_idx = threadblock_swizzle.get_batch_idx();
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batch_idx < params.batch_count;
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batch_idx += gridDim.z) {
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// Compute initial location in logical coordinates
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cutlass::MatrixCoord tb_offset_A{
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threadblock_tile_offset.m() * Mma::Shape::kM,
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0
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};
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cutlass::MatrixCoord tb_offset_B{
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0,
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threadblock_tile_offset.n() * Mma::Shape::kN
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};
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// Compute position within threadblock
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int thread_idx = threadIdx.x;
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// Construct iterators to A and B operands
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typename Mma::IteratorA iterator_A(
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params.params_A,
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const_cast<typename Mma::IteratorA::Element *>(params.ptr_A[batch_idx]),
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params.problem_size.mk(),
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thread_idx,
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tb_offset_A);
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typename Mma::IteratorB iterator_B(
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params.params_B,
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const_cast<typename Mma::IteratorB::Element *>(params.ptr_B[batch_idx]),
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params.problem_size.kn(),
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thread_idx,
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tb_offset_B);
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//
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// Main loop
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//
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// Broadcast the warp_id computed by lane 0 to ensure dependent code
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// is compiled as warp-uniform.
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int warp_idx = canonical_warp_idx_sync();
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int lane_idx = threadIdx.x % 32;
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Mma mma(shared_storage.main_loop, thread_idx, warp_idx, lane_idx);
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typename Mma::FragmentC accumulators;
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accumulators.clear();
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// Compute threadblock-scoped matrix multiply-add
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mma(params.gemm_k_iterations, accumulators, iterator_A, iterator_B, accumulators);
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//
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// Epilogue
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//
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OutputOp output_op(params.epilogue);
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//
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// Masked tile iterators constructed from members
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//
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threadblock_tile_offset =
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threadblock_swizzle.get_tile_offset(params.swizzle_log_tile);
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//assume identity swizzle
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MatrixCoord threadblock_offset(
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threadblock_tile_offset.m() * Mma::Shape::kM,
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threadblock_tile_offset.n() * Mma::Shape::kN
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);
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// Tile iterator writing to output tile
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typename Epilogue::OutputTileIterator iterator_C(
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params.params_C,
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const_cast<typename Epilogue::OutputTileIterator::Element *>(params.ptr_C[batch_idx]),
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params.problem_size.mn(),
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thread_idx,
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threadblock_offset
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);
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// Tile iterator writing to output tile
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typename Epilogue::OutputTileIterator iterator_D(
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params.params_D,
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params.ptr_D[batch_idx],
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params.problem_size.mn(),
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thread_idx,
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threadblock_offset
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);
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Epilogue epilogue(
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shared_storage.epilogue,
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thread_idx,
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warp_idx,
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lane_idx);
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// run efficient epilogue
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epilogue(output_op, iterator_D, accumulators, iterator_C);
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}
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}
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};
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/////////////////////////////////////////////////////////////////////////////////////////////////
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} // namespace kernel
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} // namespace gemm
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} // namespace cutlass
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