159 lines
5.0 KiB
C++
159 lines
5.0 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/aligned_buffer.h"
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#include "cutlass/array.h"
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#include "cutlass/numeric_types.h"
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#include "cutlass/matrix_shape.h"
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#include "cutlass/gemm/gemm.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 <typename Mma, typename Epilogue, typename ThreadblockSwizzle>
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__global__ void GemmPipelined(
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cutlass::gemm::GemmCoord problem_size,
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cutlass::gemm::GemmCoord grid_tiled_shape,
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typename Mma::IteratorA::Params params_A,
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typename Mma::IteratorA::TensorRef ref_A,
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typename Mma::IteratorB::Params params_B,
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typename Mma::IteratorB::TensorRef ref_B,
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typename Epilogue::Params params_epilogue
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) {
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// Shared storage needed by threadblock-scoped matrix multiply-accumulate
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__shared__ union {
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typename Mma::SharedStorage main_loop;
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typename Epilogue::SharedStorage epilogue;
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} shared_storage;
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// Compute threadblock location
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ThreadblockSwizzle threadblock_swizzle;
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int swizzle_log_tile = ThreadblockSwizzle().get_log_tile(grid_tiled_shape);
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cutlass::gemm::GemmCoord tb_tile_offset = threadblock_swizzle.get_tile_offset(swizzle_log_tile);
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if (grid_tiled_shape.m() <= tb_tile_offset.m() ||
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grid_tiled_shape.n() <= tb_tile_offset.n()) {
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return;
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}
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// Compute initial location in logical coordinates
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cutlass::MatrixCoord tb_offset_A{
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tb_tile_offset.m() * Mma::Shape::kM,
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tb_tile_offset.k()
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};
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cutlass::MatrixCoord tb_offset_B{
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tb_tile_offset.k(),
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tb_tile_offset.n() * Mma::Shape::kN
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};
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// Compute position within threadblock
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int tb_thread_id = 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_A,
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ref_A.data(),
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{problem_size.m(), problem_size.k()},
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tb_thread_id,
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tb_offset_A);
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typename Mma::IteratorB iterator_B(
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params_B,
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ref_B.data(),
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{problem_size.k(), problem_size.n()},
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tb_thread_id,
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tb_offset_B);
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int warp_id = canonical_warp_idx_sync();
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int lane_id = threadIdx.x % 32;
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//
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// Main loop
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//
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// Construct thread-scoped matrix multiply
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Mma mma(shared_storage.main_loop, tb_thread_id, warp_id, lane_id);
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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(problem_size, accumulators, iterator_A, iterator_B, accumulators);
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//
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// Epilogue
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//
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Epilogue epilogue(
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params_epilogue,
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shared_storage.epilogue,
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tb_thread_id,
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warp_id,
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lane_id);
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tb_tile_offset = threadblock_swizzle.get_tile_offset(swizzle_log_tile);
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//assume identity swizzle
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MatrixCoord threadblock_offset(
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tb_tile_offset.m() * Mma::Shape::kM,
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tb_tile_offset.n() * Mma::Shape::kN
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);
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// run efficient epilogue
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epilogue({problem_size.m(), problem_size.n()}, accumulators, threadblock_offset);
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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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