233 lines
7.7 KiB
C++
233 lines
7.7 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 double-buffered threadblock-scoped GEMM kernel.
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*/
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#pragma once
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#include "cutlass/aligned_buffer.h"
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#include "cutlass/arch/memory.h"
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#include "cutlass/array.h"
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#include "cutlass/cutlass.h"
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#include "cutlass/gemm/gemm.h"
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#include "cutlass/matrix_shape.h"
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#include "cutlass/numeric_types.h"
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#include "cutlass/gemm/threadblock/mma_base.h"
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////////////////////////////////////////////////////////////////////////////////
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namespace cutlass {
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namespace gemm {
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namespace threadblock {
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////////////////////////////////////////////////////////////////////////////////
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/// Structure to compute the matrix product targeting CUDA cores and SIMT math
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/// instructions.
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template <
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/// Size of the Gemm problem - concept: gemm::GemmShape<>
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typename Shape_,
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/// Policy describing tuning details (concept: MmaPolicy)
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typename Policy0_,
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/// B1-specific version of the policy (concept: MmaPolicy)
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typename Policy1_,
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/// Number of stages,
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int Stages,
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/// Used for partial specialization
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typename Enable = bool>
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class DualMmaBase {
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public:
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///< Size of the Gemm problem - concept: gemm::GemmShape<>
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using Shape = Shape_;
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///< Policy describing tuning details
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using Policy0 = Policy0_;
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using Policy1 = Policy1_;
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//
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// Dependent types
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//
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/// Warp-level Mma
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using Operator0 = typename Policy0::Operator;
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using Operator1 = typename Policy1::Operator;
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/// Shape describing the overall GEMM computed from shared memory
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/// by each warp.
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using WarpGemm = typename Policy0::Operator::Shape;
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/// Shape describing the number of warps filling the CTA
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using WarpCount = GemmShape<Shape::kM / WarpGemm::kM,
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Shape::kN / WarpGemm::kN,
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Shape::kK / WarpGemm::kK>;
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/// Number of warp-level GEMM oeprations
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static int const kWarpGemmIterations =
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(WarpGemm::kK / Operator0::Policy::MmaShape::kK);
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/// Number of stages
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static int const kStages = Stages;
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/// Tensor reference to the A operand
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using TensorRefA = TensorRef<typename Operator0::ElementA, typename Operator0::LayoutA>;
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/// Tensor reference to the B operand
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using TensorRefB0 = TensorRef<typename Operator0::ElementB, typename Operator0::LayoutB>;
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using TensorRefB1 = TensorRef<typename Operator1::ElementB, typename Operator1::LayoutB>;
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static_assert(kWarpGemmIterations > 1,
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"The pipelined structure requires at least two warp-level "
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"GEMM operations.");
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static_assert((kWarpGemmIterations % 2) == 0,
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"Inner loop iteration must be an even number.");
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//
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// Nested structs
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//
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/// Shared storage object needed by threadblock-scoped GEMM
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class SharedStorage {
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public:
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//
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// Type definitions
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//
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/// Shape of the A matrix operand in shared memory
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using ShapeA = MatrixShape<Shape::kM + Policy0::SmemPaddingA::kRow,
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Shape::kK * kStages +
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Policy0::SmemPaddingA::kColumn>;
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/// Shape of the B matrix operand in shared memory
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using ShapeB0 =
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MatrixShape<Shape::kK * kStages + Policy0::SmemPaddingB::kRow,
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Shape::kN + Policy0::SmemPaddingB::kColumn>;
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using ShapeB1 =
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MatrixShape<Shape::kK * kStages + Policy1::SmemPaddingB::kRow,
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Shape::kN + Policy1::SmemPaddingB::kColumn>;
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public:
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//
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// Data members
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//
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/// Buffer for A operand
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AlignedBuffer<typename Operator0::ElementA, ShapeA::kCount> operand_A;
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/// Buffer for B operand
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AlignedBuffer<typename Operator0::ElementB, ShapeB0::kCount> operand_B0;
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AlignedBuffer<typename Operator1::ElementB, ShapeB1::kCount> operand_B1;
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public:
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//
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// Methods
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//
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/// Returns a layout object for the A matrix
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CUTLASS_DEVICE
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static typename Operator0::LayoutA LayoutA() {
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return Operator0::LayoutA::packed({ShapeA::kRow, ShapeA::kColumn});
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}
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/// Returns a layout object for the B matrix
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CUTLASS_HOST_DEVICE
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static typename Operator0::LayoutB LayoutB0() {
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return Operator0::LayoutB::packed({ShapeB0::kRow, ShapeB0::kColumn});
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}
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/// Returns a layout object for the B matrix
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CUTLASS_HOST_DEVICE
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static typename Operator1::LayoutB LayoutB1() {
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return Operator1::LayoutB::packed({ShapeB1::kRow, ShapeB1::kColumn});
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}
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/// Returns a TensorRef to the A operand
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CUTLASS_HOST_DEVICE
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TensorRefA operand_A_ref() {
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return TensorRefA{operand_A.data(), LayoutA()};
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}
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/// Returns a TensorRef to the B operand
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CUTLASS_HOST_DEVICE
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TensorRefB0 operand_B0_ref() {
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return TensorRefB0{operand_B0.data(), LayoutB0()};
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}
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CUTLASS_HOST_DEVICE
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TensorRefB1 operand_B1_ref() {
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return TensorRefB1{operand_B1.data(), LayoutB1()};
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}
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};
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protected:
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//
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// Data members
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//
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/// Iterator to load a warp-scoped tile of A operand from shared memory
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typename Operator0::IteratorA warp_tile_iterator_A_;
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/// Iterator to load a warp-scoped tile of B operand from shared memory
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typename Operator0::IteratorB warp_tile_iterator_B0_;
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typename Operator1::IteratorB warp_tile_iterator_B1_;
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public:
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/// Construct from tensor references
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CUTLASS_DEVICE
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DualMmaBase(
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///< Shared storage needed for internal use by threadblock-scoped GEMM
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SharedStorage &shared_storage,
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///< ID within the threadblock
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int thread_idx,
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///< ID of warp
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int warp_idx,
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///< ID of each thread within a warp
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int lane_idx
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):
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warp_tile_iterator_A_(shared_storage.operand_A_ref(), lane_idx),
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warp_tile_iterator_B0_(shared_storage.operand_B0_ref(), lane_idx),
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warp_tile_iterator_B1_(shared_storage.operand_B1_ref(), lane_idx) {
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}
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};
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/////////////////////////////////////////////////////////////////////////////////////////////////
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} // namespace threadblock
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} // namespace gemm
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} // namespace cutlass
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/////////////////////////////////////////////////////////////////////////////////////////////////
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