Add support for sparse GEMM with visitor epilogue (#1189)
* Add support for sparse GEMM with visitor epilogue * Refactor changes at the kernel level
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Aleksandar Samardžić
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342
include/cutlass/gemm/device/gemm_sparse_with_visitor.h
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342
include/cutlass/gemm/device/gemm_sparse_with_visitor.h
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/***************************************************************************************************
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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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* 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"
|
||||
* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
||||
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
||||
* 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
|
||||
* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
||||
* 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::Sm70,
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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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198
include/cutlass/gemm/kernel/default_gemm_sparse_with_visitor.h
Normal file
198
include/cutlass/gemm/kernel/default_gemm_sparse_with_visitor.h
Normal file
@@ -0,0 +1,198 @@
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/***************************************************************************************************
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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
|
||||
* list of conditions and the following disclaimer.
|
||||
*
|
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* 2. Redistributions in binary form must reproduce the above copyright notice,
|
||||
* this list of conditions and the following disclaimer in the documentation
|
||||
* and/or other materials provided with the distribution.
|
||||
*
|
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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
|
||||
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
||||
* 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
|
||||
* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
||||
* 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,
|
||||
* 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 Default sparse GEMM with visitor.
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*/
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#pragma once
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#include "cutlass/cutlass.h"
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#include "cutlass/layout/matrix.h"
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#include "cutlass/numeric_types.h"
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#include "cutlass/arch/wmma.h"
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#include "cutlass/epilogue/threadblock/epilogue.h"
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#include "cutlass/epilogue/thread/linear_combination.h"
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#include "cutlass/gemm/gemm.h"
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#include "cutlass/gemm/kernel/gemm.h"
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#include "cutlass/gemm/kernel/default_gemm_sparse.h"
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#include "cutlass/gemm/kernel/sparse_gemm_with_visitor.h"
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#include "cutlass/gemm/kernel/gemm_pipelined.h"
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#include "cutlass/gemm/threadblock/default_mma_core_sm75.h"
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#include "cutlass/gemm/threadblock/default_mma_core_sm70.h"
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#include "cutlass/gemm/threadblock/default_mma_core_sm80.h"
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#include "cutlass/gemm/threadblock/default_mma_core_sparse_sm80.h"
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#include "cutlass/gemm/threadblock/default_sparse_mma.h"
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#include "cutlass/gemm/threadblock/default_mma_core_simt.h"
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#include "cutlass/gemm/threadblock/threadblock_swizzle.h"
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#include "cutlass/epilogue/threadblock/default_epilogue_tensor_op.h"
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#include "cutlass/epilogue/threadblock/default_epilogue_volta_tensor_op.h"
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#include "cutlass/epilogue/threadblock/default_epilogue_simt.h"
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#include "cutlass/epilogue/threadblock/epilogue_with_visitor_callbacks.h"
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#include "cutlass/transform/threadblock/predicated_tile_iterator.h"
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#if defined(CUTLASS_ARCH_WMMA_ENABLED)
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#include "cutlass/epilogue/threadblock/default_epilogue_wmma_tensor_op.h"
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#endif //CUTLASS_ARCH_WMMA_ENABLED
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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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/// 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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/// Access granularity of A matrix in units of elements
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int kAlignmentA,
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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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/// Access granularity of B matrix in units of elements
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int kAlignmentB,
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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,
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/// Operator class tag
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typename OperatorClass,
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/// Tag indicating architecture to tune for
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typename ArchTag,
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/// Threadblock-level tile size (concept: GemmShape)
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typename ThreadblockShape,
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/// Warp-level tile size (concept: GemmShape)
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typename WarpShape,
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/// Warp-level tile size (concept: GemmShape)
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typename InstructionShape,
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/// Epilogue output operator
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typename FusionCallbacks,
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/// Threadblock-level swizzling operator
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typename ThreadblockSwizzle,
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/// Number of stages used in the pipelined mainloop
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int Stages,
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/// Operation performed by GEMM
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typename Operator,
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/// Number of stages used in the pipelined epilogue
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int EpilogueStages = 1>
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struct DefaultSparseGemmWithVisitor;
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////////////////////////////////////////////////////////////////////////////////
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///////////////////////////////////////////////////////////////////////////////
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/// Partial specialization for Ampere Architecture
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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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/// Access granularity of A matrix in units of elements
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int kAlignmentA,
|
||||
/// Element type for B matrix operand
|
||||
typename ElementB,
|
||||
/// Layout type for B matrix operand
|
||||
typename LayoutB,
|
||||
/// Access granularity of A matrix in units of elements
|
||||
int kAlignmentB,
|
||||
/// Element type for C and D matrix operands
|
||||
typename ElementC,
|
||||
/// Layout type for C and D matrix operands
|
||||
typename LayoutC,
|
||||
/// Element type for internal accumulation
|
||||
typename ElementAccumulator,
|
||||
/// Threadblock-level tile size (concept: GemmShape)
|
||||
typename ThreadblockShape,
|
||||
/// Warp-level tile size (concept: GemmShape)
|
||||
typename WarpShape,
|
||||
/// Warp-level tile size (concept: GemmShape)
|
||||
typename InstructionShape,
|
||||
/// Epilogue output operator
|
||||
typename FusionCallbacks,
|
||||
/// Threadblock-level swizzling operator
|
||||
typename ThreadblockSwizzle,
|
||||
/// Number of stages used in the pipelined mainloop
|
||||
int Stages,
|
||||
/// Operation performed by GEMM
|
||||
typename Operator,
|
||||
/// Number of stages used in the pipelined epilogue
|
||||
int EpilogueStages>
|
||||
struct DefaultSparseGemmWithVisitor<ElementA, LayoutA, kAlignmentA, ElementB, LayoutB, kAlignmentB,
|
||||
ElementC, LayoutC, ElementAccumulator, arch::OpClassTensorOp,
|
||||
arch::Sm80, ThreadblockShape, WarpShape, InstructionShape,
|
||||
FusionCallbacks, ThreadblockSwizzle, Stages, Operator,
|
||||
EpilogueStages> {
|
||||
/// Define the threadblock-scoped matrix multiply-accumulate
|
||||
using Mma = typename cutlass::gemm::threadblock::DefaultSparseMma<
|
||||
ElementA, LayoutA, kAlignmentA, ElementB, LayoutB, kAlignmentB,
|
||||
ElementAccumulator, layout::RowMajor, arch::OpClassTensorOp, arch::Sm80,
|
||||
ThreadblockShape, WarpShape, InstructionShape, Stages,
|
||||
Operator>::ThreadblockMma;
|
||||
|
||||
static constexpr int kAlignmentC = 128 / sizeof_bits<ElementC>::value;;
|
||||
using ElementEpilogue = ElementAccumulator;
|
||||
|
||||
static const int kPartitionsK = ThreadblockShape::kK / WarpShape::kK;
|
||||
using EpilogueOutputOp =
|
||||
typename epilogue::thread::LinearCombination<
|
||||
ElementC, kAlignmentC,
|
||||
ElementAccumulator, ElementEpilogue>;
|
||||
using BaseEpilogue =
|
||||
typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
||||
ThreadblockShape, typename Mma::Operator, kPartitionsK,
|
||||
EpilogueOutputOp, EpilogueOutputOp::kCount>::Epilogue;
|
||||
|
||||
// Define epilogue
|
||||
using Epilogue = cutlass::epilogue::threadblock::EpilogueWithVisitorCallbacks<
|
||||
BaseEpilogue,
|
||||
FusionCallbacks,
|
||||
EpilogueStages>;
|
||||
|
||||
/// Define the kernel-level GEMM operator.
|
||||
using GemmKernel = kernel::SparseGemmWithEpilogueVisitor<Mma, Epilogue, ThreadblockSwizzle>;
|
||||
};
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace kernel
|
||||
} // namespace gemm
|
||||
} // namespace cutlass
|
||||
|
||||
117
include/cutlass/gemm/kernel/params_sparse_base.h
Normal file
117
include/cutlass/gemm/kernel/params_sparse_base.h
Normal file
@@ -0,0 +1,117 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
||||
* SPDX-License-Identifier: BSD-3-Clause
|
||||
*
|
||||
* Redistribution and use in source and binary forms, with or without
|
||||
* modification, are permitted provided that the following conditions are met:
|
||||
*
|
||||
* 1. Redistributions of source code must retain the above copyright notice, this
|
||||
* list of conditions and the following disclaimer.
|
||||
*
|
||||
* 2. Redistributions in binary form must reproduce the above copyright notice,
|
||||
* this list of conditions and the following disclaimer in the documentation
|
||||
* and/or other materials provided with the distribution.
|
||||
*
|
||||
* 3. Neither the name of the copyright holder nor the names of its
|
||||
* contributors may be used to endorse or promote products derived from
|
||||
* this software without specific prior written permission.
|
||||
*
|
||||
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
||||
* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
||||
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
||||
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
|
||||
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
||||
* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
||||
* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
||||
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
|
||||
* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
||||
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
*
|
||||
**************************************************************************************************/
|
||||
|
||||
/*! \file
|
||||
\brief Base functionality for common types of sparse GEMM kernel parameters
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace gemm {
|
||||
namespace kernel {
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
/// Parameters structure
|
||||
template <
|
||||
typename ThreadblockSwizzle,
|
||||
typename ParamsA,
|
||||
typename TensorRefA,
|
||||
typename ParamsB,
|
||||
typename TensorRefB,
|
||||
typename ParamsE,
|
||||
typename TensorRefE>
|
||||
struct SparseParamsBase
|
||||
{
|
||||
//
|
||||
// Data members
|
||||
//
|
||||
|
||||
cutlass::gemm::GemmCoord problem_size;
|
||||
cutlass::gemm::GemmCoord grid_tiled_shape;
|
||||
int swizzle_log_tile;
|
||||
ParamsA params_A;
|
||||
TensorRefA ref_A;
|
||||
ParamsB params_B;
|
||||
TensorRefB ref_B;
|
||||
ParamsE params_E;
|
||||
TensorRefE ref_E;
|
||||
int gemm_k_iterations;
|
||||
int gemm_k_size;
|
||||
|
||||
//
|
||||
// Host dispatch API
|
||||
//
|
||||
|
||||
/// Default constructor
|
||||
CUTLASS_HOST_DEVICE
|
||||
SparseParamsBase() : swizzle_log_tile(0), gemm_k_iterations(0), gemm_k_size(0) { }
|
||||
|
||||
|
||||
/// Constructor
|
||||
CUTLASS_HOST_DEVICE
|
||||
SparseParamsBase(
|
||||
cutlass::gemm::GemmCoord const & problem_size,
|
||||
cutlass::gemm::GemmCoord const & grid_tiled_shape,
|
||||
TensorRefA ref_A,
|
||||
TensorRefB ref_B,
|
||||
TensorRefE ref_E,
|
||||
int const mma_shape_k)
|
||||
:
|
||||
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_E(ref_E.layout()),
|
||||
ref_E(ref_E)
|
||||
{
|
||||
int total_gemm_k_iterations = (problem_size.k() + mma_shape_k - 1) / mma_shape_k;
|
||||
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_k;
|
||||
}
|
||||
};
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace kernel
|
||||
} // namespace gemm
|
||||
} // namespace cutlass
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
@@ -37,6 +37,7 @@
|
||||
#include "cutlass/cutlass.h"
|
||||
|
||||
#include "cutlass/gemm/gemm.h"
|
||||
#include "cutlass/gemm/kernel/params_sparse_base.h"
|
||||
#include "cutlass/matrix_coord.h"
|
||||
#include "cutlass/semaphore.h"
|
||||
|
||||
@@ -74,66 +75,58 @@ struct SparseGemm {
|
||||
using WarpCount = typename Mma::WarpCount;
|
||||
static int const kThreadCount = 32 * WarpCount::kCount;
|
||||
|
||||
using ParamsA = typename Mma::IteratorA::Params;
|
||||
using TensorRefA = typename Mma::IteratorA::TensorRef;
|
||||
using ParamsB = typename Mma::IteratorB::Params;
|
||||
using TensorRefB = typename Mma::IteratorB::TensorRef;
|
||||
using ParamsE = typename Mma::IteratorE::Params;
|
||||
using TensorRefE = typename Mma::IteratorE::TensorRef;
|
||||
|
||||
/// Parameters structure
|
||||
struct Params {
|
||||
cutlass::gemm::GemmCoord problem_size;
|
||||
cutlass::gemm::GemmCoord grid_tiled_shape;
|
||||
int swizzle_log_tile;
|
||||
typename Mma::IteratorA::Params params_A;
|
||||
typename Mma::IteratorA::TensorRef ref_A;
|
||||
typename Mma::IteratorB::Params params_B;
|
||||
typename Mma::IteratorB::TensorRef ref_B;
|
||||
struct Params : public SparseParamsBase<
|
||||
ThreadblockSwizzle, ParamsA, TensorRefA, ParamsB, TensorRefB,
|
||||
ParamsE, TensorRefE> {
|
||||
|
||||
using Base = SparseParamsBase<
|
||||
ThreadblockSwizzle, ParamsA, TensorRefA, ParamsB, TensorRefB,
|
||||
ParamsE, TensorRefE>;
|
||||
|
||||
//
|
||||
// Data members
|
||||
//
|
||||
typename Epilogue::OutputTileIterator::Params params_C;
|
||||
typename Epilogue::OutputTileIterator::TensorRef ref_C;
|
||||
typename Epilogue::OutputTileIterator::Params params_D;
|
||||
typename Epilogue::OutputTileIterator::TensorRef ref_D;
|
||||
typename Mma::IteratorE::Params params_E;
|
||||
typename Mma::IteratorE::TensorRef ref_E;
|
||||
typename OutputOp::Params output_op;
|
||||
int *semaphore;
|
||||
int gemm_k_iterations;
|
||||
int gemm_k_size;
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params(): swizzle_log_tile(0), semaphore(0), gemm_k_iterations(0), gemm_k_size(0) { }
|
||||
Params() { }
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params(
|
||||
cutlass::gemm::GemmCoord const & problem_size,
|
||||
cutlass::gemm::GemmCoord const & grid_tiled_shape,
|
||||
typename Mma::IteratorA::TensorRef ref_A,
|
||||
typename Mma::IteratorB::TensorRef ref_B,
|
||||
TensorRefA ref_A,
|
||||
TensorRefB ref_B,
|
||||
typename Epilogue::OutputTileIterator::TensorRef ref_C,
|
||||
typename Epilogue::OutputTileIterator::TensorRef ref_D,
|
||||
typename Mma::IteratorE::TensorRef ref_E,
|
||||
TensorRefE ref_E,
|
||||
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),
|
||||
Base(problem_size, grid_tiled_shape, ref_A, ref_B, ref_E, Mma::Shape::kK),
|
||||
params_C(ref_C.layout()),
|
||||
ref_C(ref_C),
|
||||
params_D(ref_D.layout()),
|
||||
ref_D(ref_D),
|
||||
params_E(ref_E.layout()),
|
||||
ref_E(ref_E),
|
||||
output_op(output_op) {
|
||||
|
||||
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;
|
||||
output_op(output_op),
|
||||
semaphore(workspace) {
|
||||
}
|
||||
};
|
||||
|
||||
|
||||
237
include/cutlass/gemm/kernel/sparse_gemm_with_visitor.h
Normal file
237
include/cutlass/gemm/kernel/sparse_gemm_with_visitor.h
Normal file
@@ -0,0 +1,237 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
||||
* SPDX-License-Identifier: BSD-3-Clause
|
||||
*
|
||||
* Redistribution and use in source and binary forms, with or without
|
||||
* modification, are permitted provided that the following conditions are met:
|
||||
*
|
||||
* 1. Redistributions of source code must retain the above copyright notice, this
|
||||
* list of conditions and the following disclaimer.
|
||||
*
|
||||
* 2. Redistributions in binary form must reproduce the above copyright notice,
|
||||
* this list of conditions and the following disclaimer in the documentation
|
||||
* and/or other materials provided with the distribution.
|
||||
*
|
||||
* 3. Neither the name of the copyright holder nor the names of its
|
||||
* contributors may be used to endorse or promote products derived from
|
||||
* this software without specific prior written permission.
|
||||
*
|
||||
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
||||
* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
||||
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
||||
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
|
||||
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
||||
* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
||||
* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
||||
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
|
||||
* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
||||
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
*
|
||||
**************************************************************************************************/
|
||||
/*! \file
|
||||
\brief Sparse GEMM with visitor.
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
|
||||
#include "cutlass/gemm/kernel/sparse_gemm.h"
|
||||
#include "cutlass/gemm/kernel/params_sparse_base.h"
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace gemm {
|
||||
namespace kernel {
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
// Sparse Gemm that compute the epilogue visitor functor
|
||||
template <
|
||||
typename Mma_, ///! Threadblock-scoped matrix multiply-accumulate
|
||||
typename Epilogue_, ///! Epilogue
|
||||
typename ThreadblockSwizzle_ ///! Threadblock swizzling function
|
||||
>
|
||||
struct SparseGemmWithEpilogueVisitor : public SparseGemm<Mma_, Epilogue_, ThreadblockSwizzle_, false> {
|
||||
|
||||
using Base = SparseGemm<Mma_, Epilogue_, ThreadblockSwizzle_, false>;
|
||||
|
||||
using Mma = Mma_;
|
||||
using Epilogue = Epilogue_;
|
||||
using ThreadblockSwizzle = ThreadblockSwizzle_;
|
||||
|
||||
using FusionCallbacks = typename Epilogue::FusionCallbacks;
|
||||
|
||||
using ParamsA = typename Mma::IteratorA::Params;
|
||||
using TensorRefA = typename Mma::IteratorA::TensorRef;
|
||||
using ParamsB = typename Mma::IteratorB::Params;
|
||||
using TensorRefB = typename Mma::IteratorB::TensorRef;
|
||||
using ParamsE = typename Mma::IteratorE::Params;
|
||||
using TensorRefE = typename Mma::IteratorE::TensorRef;
|
||||
|
||||
static int const kSparse = Base::kSparse;
|
||||
static int const kElementsPerElementE = Base::kElementsPerElementE;
|
||||
using SharedStorage = typename Base::SharedStorage;
|
||||
|
||||
/// Parameters structure
|
||||
struct Params : public SparseParamsBase<
|
||||
ThreadblockSwizzle, ParamsA, TensorRefA, ParamsB, TensorRefB,
|
||||
ParamsE, TensorRefE> {
|
||||
|
||||
using Base = SparseParamsBase<
|
||||
ThreadblockSwizzle, ParamsA, TensorRefA, ParamsB, TensorRefB,
|
||||
ParamsE, TensorRefE>;
|
||||
|
||||
//
|
||||
// Data members
|
||||
//
|
||||
|
||||
typename FusionCallbacks::Params output_op;
|
||||
cute::Shape<int32_t,int32_t,int32_t> problem_shape;
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params() { }
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params(
|
||||
cutlass::gemm::GemmCoord const & problem_size,
|
||||
cutlass::gemm::GemmCoord const & grid_tiled_shape,
|
||||
typename Mma::IteratorA::TensorRef ref_A,
|
||||
typename Mma::IteratorB::TensorRef ref_B,
|
||||
typename Mma::IteratorE::TensorRef ref_E,
|
||||
typename FusionCallbacks::Arguments output_op = typename FusionCallbacks::Arguments()
|
||||
):
|
||||
Base(problem_size, grid_tiled_shape, ref_A, ref_B, ref_E, Mma::Shape::kK),
|
||||
output_op(FusionCallbacks::to_underlying_arguments(problem_size, output_op, nullptr /*workspace*/)),
|
||||
problem_shape(problem_size.m(), problem_size.n(), 1) {
|
||||
}
|
||||
};
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
SparseGemmWithEpilogueVisitor() { }
|
||||
|
||||
/// 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;
|
||||
}
|
||||
|
||||
// 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 / kSparse,
|
||||
};
|
||||
|
||||
cutlass::MatrixCoord tb_offset_B{
|
||||
threadblock_tile_offset.k() * params.gemm_k_size,
|
||||
threadblock_tile_offset.n() * Mma::Shape::kN
|
||||
};
|
||||
|
||||
cutlass::MatrixCoord tb_offset_E{
|
||||
threadblock_tile_offset.m() * Mma::Shape::kM,
|
||||
threadblock_tile_offset.k() * params.gemm_k_size / kSparse,
|
||||
};
|
||||
|
||||
// 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);
|
||||
|
||||
// Compute threadblock-scoped matrix multiply-add
|
||||
int gemm_k_iterations = (problem_size_k - tb_offset_B.row() + Mma::Shape::kK - 1) / Mma::Shape::kK;
|
||||
|
||||
// Compute position within threadblock
|
||||
int thread_idx = threadIdx.x;
|
||||
|
||||
// Construct iterators to A, B, and E operands
|
||||
typename Mma::IteratorA iterator_A(
|
||||
params.params_A,
|
||||
params.ref_A.data(),
|
||||
{params.problem_size.m(), problem_size_k / kSparse},
|
||||
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);
|
||||
|
||||
typename Mma::IteratorE iterator_E(
|
||||
params.params_E, params.ref_E.data(),
|
||||
{params.problem_size.m(),
|
||||
problem_size_k / kSparse / kElementsPerElementE},
|
||||
thread_idx, tb_offset_E);
|
||||
|
||||
// Broadcast the warp_id computed by lane 0 to ensure dependent code
|
||||
// is compiled as warp-uniform.
|
||||
int warp_idx = canonical_warp_idx_sync();
|
||||
int lane_idx = threadIdx.x % 32;
|
||||
|
||||
//
|
||||
// Main loop
|
||||
//
|
||||
|
||||
// Construct thread-scoped matrix multiply
|
||||
Mma mma(shared_storage.main_loop, thread_idx, warp_idx, lane_idx);
|
||||
|
||||
typename Mma::FragmentC accumulators;
|
||||
|
||||
accumulators.clear();
|
||||
|
||||
if (gemm_k_iterations > 0) {
|
||||
// Compute threadblock-scoped matrix multiply-add
|
||||
mma(gemm_k_iterations, accumulators, iterator_A, iterator_B, iterator_E, accumulators);
|
||||
}
|
||||
|
||||
//
|
||||
// Masked tile iterators constructed from members
|
||||
//
|
||||
|
||||
threadblock_tile_offset =
|
||||
threadblock_swizzle.get_tile_offset(params.swizzle_log_tile);
|
||||
|
||||
int block_idx = threadblock_tile_offset.m() + threadblock_tile_offset.n() * params.grid_tiled_shape.m();
|
||||
|
||||
//
|
||||
// Epilogue
|
||||
//
|
||||
|
||||
Epilogue epilogue(
|
||||
params.output_op,
|
||||
shared_storage.epilogue,
|
||||
thread_idx,
|
||||
warp_idx,
|
||||
lane_idx);
|
||||
|
||||
// Execute the epilogue operator to update the destination tensor.
|
||||
epilogue(accumulators, threadblock_tile_offset, params.problem_shape, thread_idx);
|
||||
}
|
||||
};
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace kernel
|
||||
} // namespace gemm
|
||||
} // namespace cutlass
|
||||
Reference in New Issue
Block a user