825 lines
28 KiB
C++
825 lines
28 KiB
C++
/***************************************************************************************************
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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 Template for a Block-Ell sparse gemm kernel.
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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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#include "cutlass/semaphore.h"
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#include "cutlass/arch/arch.h"
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#include "cutlass/transform/threadblock/ell_iterator.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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bool SplitKSerial, ///! If true, code supporting split-K via serial reduction is enabled.
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bool IsASparse ///! If true, A is sparse matrix
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>
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struct EllGemm {
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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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static bool const kSplitKSerial = SplitKSerial;
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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{0};
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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::OutputTileIterator::Params params_C{};
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typename Epilogue::OutputTileIterator::TensorRef ref_C{};
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typename Epilogue::OutputTileIterator::Params params_D{};
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typename Epilogue::OutputTileIterator::TensorRef ref_D{};
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typename OutputOp::Params output_op{};
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int *semaphore = nullptr;
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int gemm_k_iterations{0};
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int gemm_k_size{0};
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const int* ell_idx = nullptr;
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int ell_ncol{0};
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int ell_blocksize{0};
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int ell_base_idx{0};
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//
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// Methods
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//
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Params() = default;
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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 Epilogue::OutputTileIterator::TensorRef ref_C,
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typename Epilogue::OutputTileIterator::TensorRef ref_D,
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const int* ell_idx,
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int ell_ncol,
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int ell_blocksize,
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int ell_base_idx,
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typename OutputOp::Params output_op = typename OutputOp::Params(),
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int *workspace = nullptr
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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(ref_A.layout()),
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ref_A(ref_A),
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params_B(ref_B.layout()),
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ref_B(ref_B),
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params_C(ref_C.layout()),
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ref_C(ref_C),
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params_D(ref_D.layout()),
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ref_D(ref_D),
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output_op(output_op),
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ell_idx(ell_idx),
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ell_ncol(ell_ncol),
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ell_blocksize(ell_blocksize),
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ell_base_idx(ell_base_idx)
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{
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int total_gemm_k_iterations = (problem_size.k() + Mma::Shape::kK - 1) / Mma::Shape::kK;
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int gemm_k_iterations = (total_gemm_k_iterations + grid_tiled_shape.k() - 1) / grid_tiled_shape.k();
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gemm_k_size = gemm_k_iterations * Mma::Shape::kK;
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semaphore = workspace;
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}
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};
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/// Shared memory storage structure
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struct SharedStorage {
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union{
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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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typename cutlass::transform::threadblock::ell::SharedStorage ell;
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};
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//
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// Methods
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//
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EllGemm() = default;
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/// Determines whether kernel satisfies alignment
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static Status can_implement(
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cutlass::gemm::GemmCoord const & problem_size,
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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 Epilogue::OutputTileIterator::TensorRef ref_C,
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typename Epilogue::OutputTileIterator::TensorRef ref_D) {
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static int const kAlignmentA = (platform::is_same<typename Mma::IteratorA::Layout,
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layout::ColumnMajorInterleaved<32>>::value)
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? 32
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: (platform::is_same<typename Mma::IteratorA::Layout,
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layout::ColumnMajorInterleaved<64>>::value)
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? 64
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: Mma::IteratorA::AccessType::kElements;
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static int const kAlignmentB = (platform::is_same<typename Mma::IteratorB::Layout,
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layout::RowMajorInterleaved<32>>::value)
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? 32
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: (platform::is_same<typename Mma::IteratorB::Layout,
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layout::RowMajorInterleaved<64>>::value)
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? 64
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: Mma::IteratorB::AccessType::kElements;
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static int const kAlignmentC = Epilogue::OutputTileIterator::kElementsPerAccess;
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if (!TensorRef_aligned(ref_A, kAlignmentA)) {
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return Status::kErrorMisalignedOperand;
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}
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if (!TensorRef_aligned(ref_B, kAlignmentB)) {
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return Status::kErrorMisalignedOperand;
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}
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if (!TensorRef_aligned(ref_C, kAlignmentC)) {
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return Status::kErrorMisalignedOperand;
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}
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if (!TensorRef_aligned(ref_D, kAlignmentC)) {
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return Status::kErrorMisalignedOperand;
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}
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if ((problem_size.m() % kAlignmentA) || (problem_size.k() % kAlignmentA) ||
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(problem_size.n() % kAlignmentB) || (problem_size.k() % kAlignmentB) ||
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(problem_size.m() % kAlignmentC) || (problem_size.n() % kAlignmentC)) {
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return Status::kErrorMisalignedOperand;
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}
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return Status::kSuccess;
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}
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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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int tile_in_ell_block = (params.ell_blocksize + Mma::Shape::kM - 1 ) / Mma::Shape::kM;
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int ell_block_offset_m = threadblock_tile_offset.m() / tile_in_ell_block;
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int tile_offset_m = threadblock_tile_offset.m() % tile_in_ell_block;
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// Compute position within threadblock
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int thread_idx = threadIdx.x;
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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 = __shfl_sync(0xffffffff, threadIdx.x / 32, 0);
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int lane_idx = threadIdx.x % 32;
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typename Mma::FragmentC accumulators;
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accumulators.clear();
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// skip computation if matrix is 0
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if (params.ell_ncol > 0) {
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// Compute initial location in logical coordinates
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cutlass::MatrixCoord tb_offset_A{
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ell_block_offset_m * params.ell_blocksize
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+ tile_offset_m * Mma::Shape::kM,
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threadblock_tile_offset.k() * params.gemm_k_size
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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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int ell_idx_start =
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(threadblock_tile_offset.m() / tile_in_ell_block) *
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(params.ell_ncol / params.ell_blocksize);
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const int* ell_idx_ptr = &(params.ell_idx[ell_idx_start]);
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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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problem_size_k = min(problem_size_k, params.ell_ncol);
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// Compute threadblock-scoped matrix multiply-add
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int gemm_k_iterations =
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(problem_size_k - tb_offset_A.column() + Mma::Shape::kK - 1) / Mma::Shape::kK;
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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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params.ref_A.data(),
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{params.problem_size.m(), problem_size_k},
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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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// Define coef for ELL index depending on LayoutB
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int ell_stride = iterator_B.get_stride();
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typename cutlass::transform::threadblock::ell::Iterator ell_iterator(
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shared_storage.ell,
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ell_idx_ptr,
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params.ell_blocksize,
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params.ell_base_idx,
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Mma::Shape::kK,
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problem_size_k,
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ell_stride,
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thread_idx
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);
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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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if (!kSplitKSerial || gemm_k_iterations > 0) {
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// check if index computations can be skipped
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static int const kAlignmentA = Mma::IteratorA::AccessType::kElements;
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static int const kAlignmentB = Mma::IteratorB::AccessType::kElements;
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static int const kAlignmentC = Epilogue::OutputTileIterator::kElementsPerAccess;
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constexpr bool is_double = (sizeof(Mma::IteratorA::Element) == 8);
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constexpr bool is_multiple_alignment =
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(kAlignmentA > 1) && (kAlignmentB > 1) && (kAlignmentC > 1);
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const bool is_specialized_blocksize =
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((params.ell_blocksize) & (params.ell_blocksize-1)) == 0
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&& params.ell_blocksize >= Mma::Shape::kK;
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// Compute threadblock-scoped matrix multiply-add
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if ((is_double || is_multiple_alignment) && is_specialized_blocksize) {
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mma.operator()<true, true>(
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gemm_k_iterations, accumulators, iterator_A, iterator_B, accumulators, ell_iterator);
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}
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else {
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mma.operator()<true, false>(
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gemm_k_iterations, accumulators, iterator_A, iterator_B, accumulators, ell_iterator);
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}
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}
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} // if (params.ell_ncols > 0)
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//
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// Epilogue
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//
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OutputOp output_op(params.output_op);
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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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ell_block_offset_m = threadblock_tile_offset.m() / tile_in_ell_block;
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tile_offset_m = threadblock_tile_offset.m() % tile_in_ell_block;
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//assume identity swizzle
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MatrixCoord threadblock_offset(
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ell_block_offset_m * params.ell_blocksize
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+ 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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//avoid out of bounds
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MatrixCoord threadblock_extent(
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min(params.problem_size.m(),
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ell_block_offset_m * params.ell_blocksize
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+ min((tile_offset_m + 1) * Mma::Shape::kM, params.ell_blocksize)),
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min(params.problem_size.n(),
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(threadblock_tile_offset.n()+1) * Mma::Shape::kN)
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);
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int block_idx = threadblock_tile_offset.m() + threadblock_tile_offset.n() * params.grid_tiled_shape.m();
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// Construct the semaphore.
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Semaphore semaphore(params.semaphore + block_idx, thread_idx);
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// If performing a reduction via split-K, fetch the initial synchronization
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if (kSplitKSerial && params.grid_tiled_shape.k() > 1) {
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// Fetch the synchronization lock initially but do not block.
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semaphore.fetch();
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// Indicate which position in a serial reduction the output operator is currently updating
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output_op.set_k_partition(threadblock_tile_offset.k(), params.grid_tiled_shape.k());
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}
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// Tile iterator loading from source tensor.
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typename Epilogue::OutputTileIterator iterator_C(
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params.params_C,
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params.ref_C.data(),
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threadblock_extent,
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thread_idx,
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threadblock_offset
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);
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// Tile iterator writing to destination tensor.
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typename Epilogue::OutputTileIterator iterator_D(
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params.params_D,
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params.ref_D.data(),
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threadblock_extent,
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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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// Wait on the semaphore - this latency may have been covered by iterator construction
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if (kSplitKSerial && params.grid_tiled_shape.k() > 1) {
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// For subsequent threadblocks, the source matrix is held in the 'D' tensor.
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if (threadblock_tile_offset.k()) {
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iterator_C = iterator_D;
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}
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semaphore.wait(threadblock_tile_offset.k());
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}
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// Execute the epilogue operator to update the destination tensor.
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epilogue(output_op, iterator_D, accumulators, iterator_C);
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//
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// Release the semaphore
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//
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if (kSplitKSerial && params.grid_tiled_shape.k() > 1) {
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int lock = 0;
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if (params.grid_tiled_shape.k() == threadblock_tile_offset.k() + 1) {
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// The final threadblock resets the semaphore for subsequent grids.
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lock = 0;
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}
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else {
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// Otherwise, the semaphore is incremented
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lock = threadblock_tile_offset.k() + 1;
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}
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semaphore.release(lock);
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}
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}
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};
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// B is Sparse
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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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bool SplitKSerial ///! If true, code supporting split-K via serial reduction is enabled.
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>
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struct EllGemm<Mma_, Epilogue_, ThreadblockSwizzle_, SplitKSerial, false> {
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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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static bool const kSplitKSerial = SplitKSerial;
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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{0};
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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::OutputTileIterator::Params params_C{};
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typename Epilogue::OutputTileIterator::TensorRef ref_C{};
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typename Epilogue::OutputTileIterator::Params params_D{};
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typename Epilogue::OutputTileIterator::TensorRef ref_D{};
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typename OutputOp::Params output_op{};
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int *semaphore = nullptr;
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int gemm_k_iterations{0};
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int gemm_k_size{0};
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const int* ell_idx = nullptr;
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int ell_ncol{0};
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int ell_blocksize{0};
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int ell_base_idx{0};
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//
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// Methods
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//
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Params() = default;
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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 Epilogue::OutputTileIterator::TensorRef ref_C,
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typename Epilogue::OutputTileIterator::TensorRef ref_D,
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const int* ell_idx,
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int ell_ncol,
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int ell_blocksize,
|
|
int ell_base_idx,
|
|
typename OutputOp::Params output_op = typename OutputOp::Params(),
|
|
int *workspace = nullptr
|
|
):
|
|
problem_size(problem_size),
|
|
grid_tiled_shape(grid_tiled_shape),
|
|
swizzle_log_tile(ThreadblockSwizzle().get_log_tile(grid_tiled_shape)),
|
|
params_A(ref_A.layout()),
|
|
ref_A(ref_A),
|
|
params_B(ref_B.layout()),
|
|
ref_B(ref_B),
|
|
params_C(ref_C.layout()),
|
|
ref_C(ref_C),
|
|
params_D(ref_D.layout()),
|
|
ref_D(ref_D),
|
|
output_op(output_op),
|
|
ell_idx(ell_idx),
|
|
ell_ncol(ell_ncol),
|
|
ell_blocksize(ell_blocksize),
|
|
ell_base_idx(ell_base_idx)
|
|
{
|
|
|
|
int total_gemm_k_iterations = (problem_size.k() + Mma::Shape::kK - 1) / Mma::Shape::kK;
|
|
int gemm_k_iterations = (total_gemm_k_iterations + grid_tiled_shape.k() - 1) / grid_tiled_shape.k();
|
|
|
|
gemm_k_size = gemm_k_iterations * Mma::Shape::kK;
|
|
|
|
semaphore = workspace;
|
|
}
|
|
};
|
|
|
|
/// Shared memory storage structure
|
|
struct SharedStorage {
|
|
union{
|
|
typename Mma::SharedStorage main_loop;
|
|
typename Epilogue::SharedStorage epilogue;
|
|
};
|
|
typename cutlass::transform::threadblock::ell::SharedStorage ell;
|
|
};
|
|
|
|
//
|
|
// Methods
|
|
//
|
|
|
|
CUTLASS_HOST_DEVICE
|
|
EllGemm() { }
|
|
|
|
/// Determines whether kernel satisfies alignment
|
|
static Status can_implement(
|
|
cutlass::gemm::GemmCoord const & problem_size,
|
|
typename Mma::IteratorA::TensorRef ref_A,
|
|
typename Mma::IteratorB::TensorRef ref_B,
|
|
typename Epilogue::OutputTileIterator::TensorRef ref_C,
|
|
typename Epilogue::OutputTileIterator::TensorRef ref_D) {
|
|
|
|
static int const kAlignmentA = (platform::is_same<typename Mma::IteratorA::Layout,
|
|
layout::ColumnMajorInterleaved<32>>::value)
|
|
? 32
|
|
: (platform::is_same<typename Mma::IteratorA::Layout,
|
|
layout::ColumnMajorInterleaved<64>>::value)
|
|
? 64
|
|
: Mma::IteratorA::AccessType::kElements;
|
|
static int const kAlignmentB = (platform::is_same<typename Mma::IteratorB::Layout,
|
|
layout::RowMajorInterleaved<32>>::value)
|
|
? 32
|
|
: (platform::is_same<typename Mma::IteratorB::Layout,
|
|
layout::RowMajorInterleaved<64>>::value)
|
|
? 64
|
|
: Mma::IteratorB::AccessType::kElements;
|
|
static int const kAlignmentC = Epilogue::OutputTileIterator::kElementsPerAccess;
|
|
|
|
if (!TensorRef_aligned(ref_A, kAlignmentA)) {
|
|
return Status::kErrorMisalignedOperand;
|
|
}
|
|
|
|
if (!TensorRef_aligned(ref_B, kAlignmentB)) {
|
|
return Status::kErrorMisalignedOperand;
|
|
}
|
|
|
|
if (!TensorRef_aligned(ref_C, kAlignmentC)) {
|
|
return Status::kErrorMisalignedOperand;
|
|
}
|
|
|
|
if (!TensorRef_aligned(ref_D, kAlignmentC)) {
|
|
return Status::kErrorMisalignedOperand;
|
|
}
|
|
|
|
if ((problem_size.m() % kAlignmentA) || (problem_size.k() % kAlignmentA) ||
|
|
(problem_size.n() % kAlignmentB) || (problem_size.k() % kAlignmentB) ||
|
|
(problem_size.m() % kAlignmentC) || (problem_size.n() % kAlignmentC)) {
|
|
|
|
return Status::kErrorMisalignedOperand;
|
|
}
|
|
|
|
return Status::kSuccess;
|
|
}
|
|
|
|
/// Executes one GEMM
|
|
CUTLASS_DEVICE
|
|
void operator()(Params const ¶ms, SharedStorage &shared_storage) {
|
|
|
|
// Compute threadblock location
|
|
ThreadblockSwizzle threadblock_swizzle;
|
|
|
|
cutlass::gemm::GemmCoord threadblock_tile_offset =
|
|
threadblock_swizzle.get_tile_offset(params.swizzle_log_tile);
|
|
|
|
// Early exit if CTA is out of range
|
|
if (params.grid_tiled_shape.m() <= threadblock_tile_offset.m() ||
|
|
params.grid_tiled_shape.n() <= threadblock_tile_offset.n()) {
|
|
|
|
return;
|
|
}
|
|
|
|
int tile_in_ell_block = (params.ell_blocksize + Mma::Shape::kN - 1 ) / Mma::Shape::kN;
|
|
int ell_block_offset_n = threadblock_tile_offset.n() / tile_in_ell_block;
|
|
int tile_offset_n = threadblock_tile_offset.n() % tile_in_ell_block;
|
|
|
|
// Compute position within threadblock
|
|
int thread_idx = threadIdx.x;
|
|
|
|
// Broadcast the warp_id computed by lane 0 to ensure dependent code
|
|
// is compiled as warp-uniform.
|
|
int warp_idx = __shfl_sync(0xffffffff, threadIdx.x / 32, 0);
|
|
int lane_idx = threadIdx.x % 32;
|
|
|
|
typename Mma::FragmentC accumulators;
|
|
|
|
accumulators.clear();
|
|
|
|
// skip computation if matrix is 0
|
|
if (params.ell_ncol > 0) {
|
|
|
|
// Compute initial location in logical coordinates
|
|
cutlass::MatrixCoord tb_offset_A{
|
|
threadblock_tile_offset.m() * Mma::Shape::kM,
|
|
threadblock_tile_offset.k() * params.gemm_k_size,
|
|
};
|
|
|
|
cutlass::MatrixCoord tb_offset_B{
|
|
threadblock_tile_offset.k() * params.gemm_k_size,
|
|
ell_block_offset_n * params.ell_blocksize
|
|
+ tile_offset_n * Mma::Shape::kN,
|
|
};
|
|
|
|
int ell_idx_start =
|
|
(threadblock_tile_offset.n() / tile_in_ell_block) *
|
|
(params.ell_ncol / params.ell_blocksize);
|
|
const int* ell_idx_ptr = &(params.ell_idx[ell_idx_start]);
|
|
|
|
// Problem size is a function of threadblock index in the K dimension
|
|
int problem_size_k = min(
|
|
params.problem_size.k(),
|
|
(threadblock_tile_offset.k() + 1) * params.gemm_k_size);
|
|
problem_size_k = min(problem_size_k, params.ell_ncol);
|
|
|
|
// Compute threadblock-scoped matrix multiply-add
|
|
int gemm_k_iterations =
|
|
(problem_size_k - tb_offset_A.column() + Mma::Shape::kK - 1) / Mma::Shape::kK;
|
|
|
|
// Construct iterators to A and B operands
|
|
typename Mma::IteratorA iterator_A(
|
|
params.params_A,
|
|
params.ref_A.data(),
|
|
{params.problem_size.m(), problem_size_k},
|
|
thread_idx,
|
|
tb_offset_A);
|
|
|
|
typename Mma::IteratorB iterator_B(
|
|
params.params_B,
|
|
params.ref_B.data(),
|
|
{problem_size_k, params.problem_size.n()},
|
|
thread_idx,
|
|
tb_offset_B);
|
|
|
|
// Define coef for ELL index depending on LayoutA
|
|
int ell_stride = iterator_A.get_stride();
|
|
|
|
typename cutlass::transform::threadblock::ell::Iterator ell_iterator(
|
|
shared_storage.ell,
|
|
ell_idx_ptr,
|
|
params.ell_blocksize,
|
|
params.ell_base_idx,
|
|
Mma::Shape::kK,
|
|
problem_size_k,
|
|
ell_stride,
|
|
thread_idx
|
|
);
|
|
|
|
//
|
|
// Main loop
|
|
//
|
|
|
|
// Construct thread-scoped matrix multiply
|
|
Mma mma(shared_storage.main_loop, thread_idx, warp_idx, lane_idx);
|
|
|
|
if (!kSplitKSerial || gemm_k_iterations > 0) {
|
|
// check if index computations can be skipped
|
|
static int const kAlignmentA = Mma::IteratorA::AccessType::kElements;
|
|
static int const kAlignmentB = Mma::IteratorB::AccessType::kElements;
|
|
static int const kAlignmentC = Epilogue::OutputTileIterator::kElementsPerAccess;
|
|
constexpr bool is_double = (sizeof(Mma::IteratorA::Element) == 8);
|
|
constexpr bool is_multiple_alignment =
|
|
(kAlignmentA > 1) && (kAlignmentB > 1) && (kAlignmentC > 1);
|
|
const bool is_specialized_blocksize =
|
|
((params.ell_blocksize) & (params.ell_blocksize-1)) == 0
|
|
&& params.ell_blocksize >= Mma::Shape::kK;
|
|
// Compute threadblock-scoped matrix multiply-add
|
|
if ((is_double || is_multiple_alignment) && is_specialized_blocksize) {
|
|
mma.operator()<false, true>(
|
|
gemm_k_iterations, accumulators, iterator_A, iterator_B, accumulators, ell_iterator);
|
|
}
|
|
else {
|
|
mma.operator()<false, false>(
|
|
gemm_k_iterations, accumulators, iterator_A, iterator_B, accumulators, ell_iterator);
|
|
}
|
|
}
|
|
} // if (params.ell_ncols > 0)
|
|
|
|
//
|
|
// Epilogue
|
|
//
|
|
|
|
OutputOp output_op(params.output_op);
|
|
|
|
//
|
|
// Masked tile iterators constructed from members
|
|
//
|
|
|
|
threadblock_tile_offset =
|
|
threadblock_swizzle.get_tile_offset(params.swizzle_log_tile);
|
|
|
|
ell_block_offset_n = threadblock_tile_offset.n() / tile_in_ell_block;
|
|
tile_offset_n = threadblock_tile_offset.n() % tile_in_ell_block;
|
|
|
|
//assume identity swizzle
|
|
MatrixCoord threadblock_offset(
|
|
threadblock_tile_offset.m() * Mma::Shape::kM,
|
|
ell_block_offset_n * params.ell_blocksize
|
|
+ tile_offset_n * Mma::Shape::kN
|
|
);
|
|
|
|
//avoid out of bounds
|
|
MatrixCoord threadblock_extent(
|
|
min(params.problem_size.m(),
|
|
(threadblock_tile_offset.m()+1) * Mma::Shape::kM),
|
|
min(params.problem_size.n(),
|
|
ell_block_offset_n * params.ell_blocksize
|
|
+ min((tile_offset_n + 1) * Mma::Shape::kN, params.ell_blocksize))
|
|
);
|
|
|
|
int block_idx = threadblock_tile_offset.m() + threadblock_tile_offset.n() * params.grid_tiled_shape.m();
|
|
|
|
// Construct the semaphore.
|
|
Semaphore semaphore(params.semaphore + block_idx, thread_idx);
|
|
|
|
// If performing a reduction via split-K, fetch the initial synchronization
|
|
if (kSplitKSerial && params.grid_tiled_shape.k() > 1) {
|
|
|
|
// Fetch the synchronization lock initially but do not block.
|
|
semaphore.fetch();
|
|
|
|
// Indicate which position in a serial reduction the output operator is currently updating
|
|
output_op.set_k_partition(threadblock_tile_offset.k(), params.grid_tiled_shape.k());
|
|
}
|
|
|
|
// Tile iterator loading from source tensor.
|
|
typename Epilogue::OutputTileIterator iterator_C(
|
|
params.params_C,
|
|
params.ref_C.data(),
|
|
threadblock_extent,
|
|
thread_idx,
|
|
threadblock_offset
|
|
);
|
|
|
|
// Tile iterator writing to destination tensor.
|
|
typename Epilogue::OutputTileIterator iterator_D(
|
|
params.params_D,
|
|
params.ref_D.data(),
|
|
threadblock_extent,
|
|
thread_idx,
|
|
threadblock_offset
|
|
);
|
|
|
|
Epilogue epilogue(
|
|
shared_storage.epilogue,
|
|
thread_idx,
|
|
warp_idx,
|
|
lane_idx);
|
|
|
|
// Wait on the semaphore - this latency may have been covered by iterator construction
|
|
if (kSplitKSerial && params.grid_tiled_shape.k() > 1) {
|
|
|
|
// For subsequent threadblocks, the source matrix is held in the 'D' tensor.
|
|
if (threadblock_tile_offset.k()) {
|
|
iterator_C = iterator_D;
|
|
}
|
|
|
|
semaphore.wait(threadblock_tile_offset.k());
|
|
}
|
|
|
|
// Execute the epilogue operator to update the destination tensor.
|
|
epilogue(output_op, iterator_D, accumulators, iterator_C);
|
|
|
|
//
|
|
// Release the semaphore
|
|
//
|
|
|
|
if (kSplitKSerial && params.grid_tiled_shape.k() > 1) {
|
|
|
|
int lock = 0;
|
|
if (params.grid_tiled_shape.k() == threadblock_tile_offset.k() + 1) {
|
|
|
|
// The final threadblock resets the semaphore for subsequent grids.
|
|
lock = 0;
|
|
}
|
|
else {
|
|
// Otherwise, the semaphore is incremented
|
|
lock = threadblock_tile_offset.k() + 1;
|
|
}
|
|
|
|
semaphore.release(lock);
|
|
}
|
|
}
|
|
};
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
} // namespace kernel
|
|
} // namespace gemm
|
|
} // namespace cutlass
|
|
|