343 lines
11 KiB
C++
343 lines
11 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 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/numeric_types.h"
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#include "cutlass/arch/arch.h"
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#include "cutlass/device_kernel.h"
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#include "cutlass/gemm/threadblock/threadblock_swizzle.h"
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#include "cutlass/gemm/kernel/sparse_gemm.h"
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#include "cutlass/gemm/kernel/default_gemm_sparse_with_visitor.h"
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#include "cutlass/gemm/device/default_gemm_configuration.h"
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#include "cutlass/epilogue/threadblock/fusion/visitor_2x.hpp"
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////////////////////////////////////////////////////////////////////////////////
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namespace cutlass {
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namespace gemm {
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namespace device {
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/////////////////////////////////////////////////////////////////////////////////////////////////
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/*! Sparse GEMM with visitor
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*/
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template <
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/// Element type for A matrix operand
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typename ElementA_,
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/// Layout type for A matrix operand
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typename LayoutA_,
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/// Element type for B matrix operand
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typename ElementB_,
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/// Layout type for B matrix operand
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typename LayoutB_,
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/// Element type for C and D matrix operands
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typename ElementC_,
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/// Layout type for C and D matrix operands
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typename LayoutC_,
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/// Element type for internal accumulation
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typename ElementAccumulator_ = ElementC_,
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/// Operator class tag
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typename OperatorClass_ = arch::OpClassSimt,
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/// Tag indicating architecture to tune for
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typename ArchTag_ = arch::Sm80,
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/// Threadblock-level tile size (concept: GemmShape)
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typename ThreadblockShape_ = typename DefaultGemmConfiguration<
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OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
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ElementAccumulator_>::ThreadblockShape,
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/// Warp-level tile size (concept: GemmShape)
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typename WarpShape_ = typename DefaultGemmConfiguration<
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OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
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ElementAccumulator_>::WarpShape,
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/// Instruction-level tile size (concept: GemmShape)
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typename InstructionShape_ = typename DefaultGemmConfiguration<
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OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
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ElementAccumulator_>::InstructionShape,
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/// Epilogue output operator
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typename FusionCallbacks_ =
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typename cutlass::epilogue::threadblock::detail::EmptyCallbacks,
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/// Threadblock-level swizzling operator
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typename ThreadblockSwizzle_ =
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typename threadblock::GemmIdentityThreadblockSwizzle<>,
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/// Number of stages used in the pipelined mainloop
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int Stages =
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DefaultGemmConfiguration<OperatorClass_, ArchTag_, ElementA_, ElementB_,
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ElementC_, ElementAccumulator_>::kStages,
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/// Access granularity of A matrix in units of elements
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int AlignmentA =
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DefaultGemmConfiguration<OperatorClass_, ArchTag_, ElementA_, ElementB_,
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ElementC_, ElementAccumulator_>::kAlignmentA,
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/// Access granularity of B matrix in units of elements
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int AlignmentB =
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DefaultGemmConfiguration<OperatorClass_, ArchTag_, ElementA_, ElementB_,
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ElementC_, ElementAccumulator_>::kAlignmentB,
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/// Operation performed by GEMM
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typename Operator_ = typename DefaultGemmConfiguration<
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OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
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ElementAccumulator_>::Operator,
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/// Number of stages used in the pipelined epilogue
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int EpilogueStages = 1>
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class SparseGemmWithVisitor {
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public:
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using ElementA = ElementA_;
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using LayoutA = LayoutA_;
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using TensorRefA = TensorRef<ElementA const, LayoutA>;
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using ElementB = ElementB_;
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using LayoutB = LayoutB_;
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using TensorRefB = TensorRef<ElementB const, LayoutB>;
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using ElementC = ElementC_;
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using LayoutC = LayoutC_;
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using ElementAccumulator = ElementAccumulator_;
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using OperatorClass = OperatorClass_;
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using ArchTag = ArchTag_;
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using ThreadblockShape = ThreadblockShape_;
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using WarpShape = WarpShape_;
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using InstructionShape = InstructionShape_;
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using FusionCallbacks = FusionCallbacks_;
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using ThreadblockSwizzle = ThreadblockSwizzle_;
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using Operator = Operator_;
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using MathOperator = Operator;
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static int const kStages = Stages;
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static int const kAlignmentA = AlignmentA;
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static int const kAlignmentB = AlignmentB;
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/// Define the kernel
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using GemmKernel = typename kernel::DefaultSparseGemmWithVisitor<
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ElementA,
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LayoutA,
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kAlignmentA,
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ElementB,
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LayoutB,
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kAlignmentB,
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ElementC,
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LayoutC,
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ElementAccumulator,
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OperatorClass,
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ArchTag,
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ThreadblockShape,
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WarpShape,
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InstructionShape,
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FusionCallbacks,
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ThreadblockSwizzle,
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kStages,
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Operator,
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EpilogueStages
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>::GemmKernel;
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using ElementE = typename GemmKernel::ElementE;
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using LayoutE = typename GemmKernel::LayoutE;
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static int const kAlignmentE = 128 / sizeof_bits<ElementE>::value;
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static int const kSparse = GemmKernel::kSparse;
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static int const kMetaSizeInBits = GemmKernel::kMetaSizeInBits;
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static int const kElementsPerElementE = GemmKernel::kElementsPerElementE;
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/// Argument structure
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struct Arguments {
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//
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// Data members
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//
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GemmCoord problem_size;
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TensorRef<ElementA const, LayoutA> ref_A;
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TensorRef<ElementB const, LayoutB> ref_B;
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TensorRef<ElementE const, LayoutE> ref_E;
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typename FusionCallbacks::Arguments epilogue;
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//
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// Methods
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//
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/// Default ctor
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CUTLASS_HOST_DEVICE
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Arguments(): problem_size(0, 0, 0) {
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}
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/// Constructs an Arguments structure
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CUTLASS_HOST_DEVICE
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Arguments(
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GemmCoord problem_size_,
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TensorRef<ElementA const, LayoutA> ref_A_,
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TensorRef<ElementB const, LayoutB> ref_B_,
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TensorRef<ElementE, LayoutE> ref_E_,
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typename FusionCallbacks::Arguments epilogue_ =
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typename FusionCallbacks::Arguments()
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):
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problem_size(problem_size_),
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ref_A(ref_A_),
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ref_B(ref_B_),
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ref_E(ref_E_),
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epilogue(epilogue_) {
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}
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};
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private:
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/// Kernel parameters object
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typename GemmKernel::Params params_;
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public:
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/// Constructs the GEMM.
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SparseGemmWithVisitor() { }
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/// Determines whether the GEMM can execute the given problem.
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static Status can_implement(Arguments const &args) {
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Status status = GemmKernel::can_implement(
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args.problem_size,
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args.ref_A.non_const_ref(),
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args.ref_B.non_const_ref(),
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cutlass::TensorRef<ElementC, LayoutC>(), // It only matters that it's empty.
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cutlass::TensorRef<ElementC, LayoutC>(), // Same as above.
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args.ref_E.non_const_ref()
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);
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if (status != Status::kSuccess) {
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return status;
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}
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return Status::kSuccess;
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}
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/// Gets the workspace size
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static size_t get_workspace_size(Arguments const &args) {
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size_t bytes = 0;
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return bytes;
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}
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/// Initializes GEMM state from arguments.
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Status initialize(Arguments const &args, void *workspace = nullptr, cudaStream_t stream = nullptr) {
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constexpr int SplitKSlices = 1;
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// Determine grid shape
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ThreadblockSwizzle threadblock_swizzle;
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cutlass::gemm::GemmCoord grid_shape = threadblock_swizzle.get_tiled_shape(
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args.problem_size,
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{ThreadblockShape::kM, ThreadblockShape::kN, ThreadblockShape::kK},
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SplitKSlices);
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// Initialize the Params structure
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params_ = typename GemmKernel::Params{
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args.problem_size,
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grid_shape,
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args.ref_A.non_const_ref(),
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args.ref_B.non_const_ref(),
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args.ref_E.non_const_ref(),
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args.epilogue
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};
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int smem_size = int(sizeof(typename GemmKernel::SharedStorage));
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if (smem_size >= (48 << 10)) {
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cudaError_t result = cudaFuncSetAttribute(Kernel<GemmKernel>,
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cudaFuncAttributeMaxDynamicSharedMemorySize,
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smem_size);
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if (result != cudaSuccess) {
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return Status::kErrorInternal;
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}
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}
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return Status::kSuccess;
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}
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/// Lightweight update given a subset of arguments
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Status update(Arguments const &args, void *workspace = nullptr) {
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params_.ref_A.reset(args.ref_A.non_const_ref().data());
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params_.ref_B.reset(args.ref_B.non_const_ref().data());
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params_.ref_E.reset(args.ref_E.non_const_ref().data());
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params_.output_op = args.epilogue;
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return Status::kSuccess;
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}
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/// Runs the kernel using initialized state.
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Status run(cudaStream_t stream = nullptr) {
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ThreadblockSwizzle threadblock_swizzle;
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dim3 grid = threadblock_swizzle.get_grid_shape(params_.grid_tiled_shape);
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dim3 block(GemmKernel::kThreadCount, 1, 1);
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int smem_size = int(sizeof(typename GemmKernel::SharedStorage));
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cutlass::Kernel<GemmKernel><<<grid, block, smem_size, stream>>>(params_);
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cudaError_t result = cudaGetLastError();
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return result == cudaSuccess ? Status::kSuccess : Status::kErrorInternal;
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}
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/// Runs the kernel using initialized state.
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Status operator()(cudaStream_t stream = nullptr) {
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return run(stream);
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}
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/// Runs the kernel using initialized state.
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Status operator()(
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Arguments const &args,
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void *workspace = nullptr,
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cudaStream_t stream = nullptr) {
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Status status = initialize(args, workspace, stream);
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if (status == Status::kSuccess) {
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status = run(stream);
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}
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return status;
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}
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};
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} // namespace device
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} // namespace gemm
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} // namespace cutlass
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////////////////////////////////////////////////////////////////////////////////
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