66d9cddc83
* New updates. * Minor profiler updates Co-authored-by: Aniket Shivam <ashivam@nvidia.com>
490 lines
16 KiB
C++
490 lines
16 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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#include "cutlass/semaphore.h"
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#include "../threadblock/dual_mma_multistage.h"
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#include "../threadblock/dual_epilogue.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 DualMma_, ///! Threadblock-scoped matrix multiply-accumulate
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typename Epilogue0_, ///! Epilogue
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typename Epilogue1_, ///! Epilogue
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typename OutputOp2_, ///! 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 StoreD0,
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bool StoreD1
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>
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struct DualGemm {
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using DualMma = DualMma_;
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using Epilogue0 = Epilogue0_;
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using Epilogue1 = Epilogue1_;
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using OutputOp0 = typename Epilogue0::OutputOp;
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using OutputOp1 = typename Epilogue1::OutputOp;
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using OutputOp2 = OutputOp2_;
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using ThreadblockSwizzle = ThreadblockSwizzle_;
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static constexpr bool kStoreD0 = StoreD0;
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static constexpr bool kStoreD1 = StoreD1;
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using DualEpilogue = cutlass::epilogue::threadblock::DualEpilogue<
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typename Epilogue0::Shape,
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typename Epilogue0::WarpMmaOperator,
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Epilogue0::kPartitionsK,
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typename Epilogue0::OutputTileIterator,
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typename Epilogue0::AccumulatorFragmentIterator,
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typename Epilogue0::WarpTileIterator,
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typename Epilogue0::SharedLoadIterator,
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OutputOp0,
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OutputOp1,
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OutputOp2,
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typename Epilogue0::Padding,
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kStoreD0,
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kStoreD1,
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Epilogue0::kFragmentsPerIteration,
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true // IterationsUnroll
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>;
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static bool const kSplitKSerial = SplitKSerial;
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static_assert(!kSplitKSerial || (kStoreD0 && kStoreD1),
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"Split-K serial requires buffers for D0/D1 for reduction");
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/// Warp count (concept: GemmShape)
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using WarpCount0 = typename DualMma::WarpCount;
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static int const kThreadCount = 32 * WarpCount0::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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// Mma0
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typename DualMma::IteratorA::Params params_A0;
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typename DualMma::IteratorA::TensorRef ref_A0;
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typename DualMma::IteratorB::Params params_B0;
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typename DualMma::IteratorB::TensorRef ref_B0;
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typename Epilogue0::OutputTileIterator::Params params_C0;
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typename Epilogue0::OutputTileIterator::TensorRef ref_C0;
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typename Epilogue0::OutputTileIterator::Params params_D0;
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typename Epilogue0::OutputTileIterator::TensorRef ref_D0;
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typename OutputOp0::Params output_op_0;
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// Mma1
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typename DualMma::IteratorB::Params params_B1;
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typename DualMma::IteratorB::TensorRef ref_B1;
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typename Epilogue1::OutputTileIterator::Params params_C1;
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typename Epilogue1::OutputTileIterator::TensorRef ref_C1;
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typename Epilogue1::OutputTileIterator::Params params_D1;
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typename Epilogue1::OutputTileIterator::TensorRef ref_D1;
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typename OutputOp1::Params output_op_1;
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typename Epilogue1::OutputTileIterator::Params params_D2;
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typename Epilogue1::OutputTileIterator::TensorRef ref_D2;
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typename OutputOp2::Params output_op_2;
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int *semaphore;
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int gemm_k_size;
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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(): swizzle_log_tile(0), semaphore(0), gemm_k_size(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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// Mma0: D0 = A @ B0 + C0
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typename DualMma::IteratorA::TensorRef ref_A0,
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typename DualMma::IteratorB::TensorRef ref_B0,
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typename Epilogue0::OutputTileIterator::TensorRef ref_C0,
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typename Epilogue0::OutputTileIterator::TensorRef ref_D0,
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// Mma1: D1 = A @ B1 + C1
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typename DualMma::IteratorB::TensorRef ref_B1,
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typename Epilogue1::OutputTileIterator::TensorRef ref_C1,
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typename Epilogue1::OutputTileIterator::TensorRef ref_D1,
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typename Epilogue1::OutputTileIterator::TensorRef ref_D2,
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typename OutputOp0::Params output_op_0 = typename OutputOp0::Params(),
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typename OutputOp1::Params output_op_1 = typename OutputOp1::Params(),
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typename OutputOp2::Params output_op_2 = typename OutputOp2::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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// Mma0
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params_A0(ref_A0.layout()),
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ref_A0(ref_A0),
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params_B0(ref_B0.layout()),
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ref_B0(ref_B0),
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params_C0(ref_C0.layout()),
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ref_C0(ref_C0),
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params_D0(ref_D0.layout()),
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ref_D0(ref_D0),
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// Mma1
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params_B1(ref_B1.layout()),
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ref_B1(ref_B1),
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params_C1(ref_C1.layout()),
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ref_C1(ref_C1),
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params_D1(ref_D1.layout()),
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ref_D1(ref_D1),
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params_D2(ref_D2.layout()),
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ref_D2(ref_D2),
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output_op_0(output_op_0),
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output_op_1(output_op_1),
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output_op_2(output_op_2) {
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int total_gemm_k_iterations = (problem_size.k() + DualMma::Shape::kK - 1) / DualMma::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 * DualMma::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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union SharedStorage {
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typename DualMma::SharedStorage main_loop;
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typename DualEpilogue::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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DualGemm() { }
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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 DualMma::IteratorA::TensorRef ref_A0,
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typename DualMma::IteratorB::TensorRef ref_B0,
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typename Epilogue0::OutputTileIterator::TensorRef ref_C0,
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typename Epilogue0::OutputTileIterator::TensorRef ref_D0,
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typename DualMma::IteratorB::TensorRef ref_B1,
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typename Epilogue1::OutputTileIterator::TensorRef ref_C1,
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typename Epilogue1::OutputTileIterator::TensorRef ref_D1,
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typename Epilogue1::OutputTileIterator::TensorRef ref_D2) {
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static int const kAlignmentA = DualMma::IteratorA::AccessType::kElements;
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static int const kAlignmentB = DualMma::IteratorB::AccessType::kElements;
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static int const kAlignmentC = Epilogue0::OutputTileIterator::kElementsPerAccess;
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if (!TensorRef_aligned(ref_A0, kAlignmentA)) {
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return Status::kErrorMisalignedOperand;
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}
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if (!TensorRef_aligned(ref_B0, kAlignmentB)) {
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return Status::kErrorMisalignedOperand;
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}
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if (!TensorRef_aligned(ref_C0, kAlignmentC)) {
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return Status::kErrorMisalignedOperand;
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}
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if (!TensorRef_aligned(ref_D0, kAlignmentC)) {
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return Status::kErrorMisalignedOperand;
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}
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if (!TensorRef_aligned(ref_B1, kAlignmentB)) {
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return Status::kErrorMisalignedOperand;
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}
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if (!TensorRef_aligned(ref_C1, kAlignmentC)) {
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return Status::kErrorMisalignedOperand;
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}
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if (!TensorRef_aligned(ref_D1, kAlignmentC)) {
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return Status::kErrorMisalignedOperand;
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}
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if (!TensorRef_aligned(ref_D2, 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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// Compute initial location in logical coordinates
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cutlass::MatrixCoord tb_offset_A0{
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threadblock_tile_offset.m() * DualMma::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_B0{
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threadblock_tile_offset.k() * params.gemm_k_size,
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threadblock_tile_offset.n() * DualMma::Shape::kN
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};
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cutlass::MatrixCoord tb_offset_B1{
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threadblock_tile_offset.k() * params.gemm_k_size,
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threadblock_tile_offset.n() * DualMma::Shape::kN
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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 =
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(params.problem_size.k() < (threadblock_tile_offset.k() + 1) * params.gemm_k_size) ?
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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_A0.column() + DualMma::Shape::kK - 1) / DualMma::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 and B operands
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typename DualMma::IteratorA iterator_A0(
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params.params_A0,
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params.ref_A0.data(),
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{params.problem_size.m(), problem_size_k},
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thread_idx,
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tb_offset_A0);
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typename DualMma::IteratorB iterator_B0(
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params.params_B0,
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params.ref_B0.data(),
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{problem_size_k, params.problem_size.n()},
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thread_idx,
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tb_offset_B0);
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typename DualMma::IteratorB iterator_B1(
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params.params_B1,
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params.ref_B1.data(),
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{problem_size_k, params.problem_size.n()},
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thread_idx,
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tb_offset_B1);
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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(0x1f, threadIdx.x / 32, 0);
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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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typename DualMma::FragmentC accum0;
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typename DualMma::FragmentC accum1;
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accum0.clear();
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accum1.clear();
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DualMma 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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// Compute threadblock-scoped matrix multiply-add
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mma(gemm_k_iterations,
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accum0, accum1,
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iterator_A0, iterator_B0, iterator_B1,
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accum0, accum1);
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}
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//
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// Epilogue
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//
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OutputOp0 output_op_0(params.output_op_0);
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OutputOp1 output_op_1(params.output_op_1);
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OutputOp2 output_op_2(params.output_op_2);
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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() * DualMma::Shape::kM,
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threadblock_tile_offset.n() * DualMma::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_0.set_k_partition(threadblock_tile_offset.k(), params.grid_tiled_shape.k());
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output_op_1.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 Epilogue0::OutputTileIterator iterator_C0(
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params.params_C0,
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params.ref_C0.data(),
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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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typename Epilogue1::OutputTileIterator iterator_C1(
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params.params_C1,
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params.ref_C1.data(),
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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 destination tensor.
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typename Epilogue0::OutputTileIterator iterator_D0(
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params.params_D0,
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params.ref_D0.data(),
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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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typename Epilogue1::OutputTileIterator iterator_D1(
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params.params_D1,
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params.ref_D1.data(),
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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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typename Epilogue1::OutputTileIterator iterator_D2(
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params.params_D2,
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params.ref_D2.data(),
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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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DualEpilogue 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_C0 = iterator_D0;
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iterator_C1 = iterator_D1;
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}
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semaphore.wait(threadblock_tile_offset.k());
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__threadfence();
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}
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// Execute the epilogue operator to update the destination tensor.
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typename Epilogue0::OutputTileIterator source_iters[] = {
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iterator_C0, iterator_C1
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};
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const bool writeToD2 = (!kSplitKSerial || params.grid_tiled_shape.k() == threadblock_tile_offset.k() + 1);
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epilogue(
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output_op_0, output_op_1, output_op_2,
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iterator_D0, iterator_D1, iterator_D2,
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accum0, accum1,
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source_iters,
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writeToD2
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);
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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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__threadfence();
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semaphore.release(lock);
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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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