239 lines
8.0 KiB
C++
239 lines
8.0 KiB
C++
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/***************************************************************************************************
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* Copyright (c) 2017 - 2024 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 Sparse GEMM with visitor.
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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/kernel/sparse_gemm.h"
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#include "cutlass/gemm/kernel/params_sparse_base.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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// Sparse Gemm that compute the epilogue visitor functor
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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 SparseGemmWithEpilogueVisitor : public SparseGemm<Mma_, Epilogue_, ThreadblockSwizzle_, false> {
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using Base = SparseGemm<Mma_, Epilogue_, ThreadblockSwizzle_, false>;
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using Mma = Mma_;
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using Epilogue = Epilogue_;
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using ThreadblockSwizzle = ThreadblockSwizzle_;
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using FusionCallbacks = typename Epilogue::FusionCallbacks;
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using ParamsA = typename Mma::IteratorA::Params;
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using TensorRefA = typename Mma::IteratorA::TensorRef;
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using ParamsB = typename Mma::IteratorB::Params;
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using TensorRefB = typename Mma::IteratorB::TensorRef;
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using ParamsE = typename Mma::IteratorE::Params;
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using TensorRefE = typename Mma::IteratorE::TensorRef;
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static int const kSparse = Base::kSparse;
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static int const kElementsPerElementE = Base::kElementsPerElementE;
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using SharedStorage = typename Base::SharedStorage;
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/// Parameters structure
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struct Params : public SparseParamsBase<
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ThreadblockSwizzle, ParamsA, TensorRefA, ParamsB, TensorRefB,
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ParamsE, TensorRefE> {
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using Base = SparseParamsBase<
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ThreadblockSwizzle, ParamsA, TensorRefA, ParamsB, TensorRefB,
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ParamsE, TensorRefE>;
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//
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// Data members
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//
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typename FusionCallbacks::Params output_op;
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cute::Shape<int32_t,int32_t,int32_t> problem_shape;
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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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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::TensorRef ref_A,
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typename Mma::IteratorB::TensorRef ref_B,
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typename Mma::IteratorE::TensorRef ref_E,
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typename FusionCallbacks::Arguments output_op = typename FusionCallbacks::Arguments()
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):
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Base(problem_size, grid_tiled_shape, ref_A, ref_B, ref_E, Mma::Shape::kK),
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output_op(FusionCallbacks::to_underlying_arguments(problem_size, output_op, nullptr /*workspace*/)),
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problem_shape(problem_size.m(), problem_size.n(), 1) {
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}
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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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SparseGemmWithEpilogueVisitor() { }
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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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// 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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threadblock_tile_offset.k() * params.gemm_k_size / kSparse,
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};
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cutlass::MatrixCoord tb_offset_B{
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threadblock_tile_offset.k() * params.gemm_k_size,
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threadblock_tile_offset.n() * Mma::Shape::kN
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};
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cutlass::MatrixCoord tb_offset_E{
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threadblock_tile_offset.m() * Mma::Shape::kM,
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threadblock_tile_offset.k() * params.gemm_k_size / kSparse,
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};
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// Problem size is a function of threadblock index in the K dimension
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int problem_size_k = min(
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params.problem_size.k(),
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(threadblock_tile_offset.k() + 1) * params.gemm_k_size);
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// Compute threadblock-scoped matrix multiply-add
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int gemm_k_iterations = (problem_size_k - tb_offset_B.row() + Mma::Shape::kK - 1) / Mma::Shape::kK;
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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, B, and E operands
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typename Mma::IteratorA iterator_A(
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params.params_A,
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params.ref_A.data(),
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{params.problem_size.m(), problem_size_k / kSparse},
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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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params.ref_B.data(),
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{problem_size_k, params.problem_size.n()},
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thread_idx,
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tb_offset_B);
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typename Mma::IteratorE iterator_E(
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params.params_E, params.ref_E.data(),
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{params.problem_size.m(),
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problem_size_k / kSparse / kElementsPerElementE},
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thread_idx, tb_offset_E);
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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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//
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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, thread_idx, warp_idx, lane_idx);
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typename Mma::FragmentC accumulators;
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accumulators.clear();
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if (gemm_k_iterations > 0) {
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// Compute threadblock-scoped matrix multiply-add
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mma(gemm_k_iterations, accumulators, iterator_A, iterator_B, iterator_E, accumulators);
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}
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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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int block_idx = threadblock_tile_offset.m() + threadblock_tile_offset.n() * params.grid_tiled_shape.m();
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//
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// Epilogue
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//
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Epilogue epilogue(
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params.output_op,
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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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// Execute the epilogue operator to update the destination tensor.
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epilogue(accumulators, threadblock_tile_offset, params.problem_shape, thread_idx);
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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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