CUTLASS 2.5
This commit is contained in:
Andrew Kerr
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/***************************************************************************************************
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* Copyright (c) 2017-2021, NVIDIA CORPORATION. All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without modification, are permitted
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* provided that the following conditions are met:
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* * Redistributions of source code must retain the above copyright notice, this list of
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* conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above copyright notice, this list of
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* conditions and the following disclaimer in the documentation and/or other materials
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* provided with the distribution.
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* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
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* to endorse or promote products derived from this software without specific prior written
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* 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
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* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
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* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
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* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
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* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
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* STRICT LIABILITY, OR TOR (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/device/default_gemm_configuration.h"
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#include "cutlass/epilogue/thread/linear_combination_relu.h"
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#include "kernel/b2b_gemm.h"
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#include "kernel/default_b2b_gemm.h"
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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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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 ThreadblockShape0_ = typename DefaultGemmConfiguration<
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OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
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ElementAccumulator_>::ThreadblockShape,
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/// Threadblock-level tile size (concept: GemmShape)
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typename ThreadblockShape1_ = 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 WarpShape0_ = typename DefaultGemmConfiguration<
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OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
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ElementAccumulator_>::WarpShape,
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/// Warp-level tile size (concept: GemmShape)
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typename WarpShape1_ = 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 EpilogueOutputOp0_ = typename DefaultGemmConfiguration<
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OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
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ElementAccumulator_>::EpilogueOutputOp,
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/// Epilogue output operator
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typename EpilogueOutputOp1_ = typename DefaultGemmConfiguration<
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OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
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ElementAccumulator_>::EpilogueOutputOp,
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/// Threadblock-level swizzling operator
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typename ThreadblockSwizzle_ = 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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/// If true, kernel supports split-K with serial reduction
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bool SplitKSerial = false,
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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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/// Whether Beta is zero or not
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bool IsBetaZero = false>
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class B2bGemm {
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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 TensorRefC = TensorRef<ElementC const, LayoutC>;
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using TensorRefD = TensorRef<ElementC, 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 ThreadblockShape0 = ThreadblockShape0_;
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using ThreadblockShape1 = ThreadblockShape1_;
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using WarpShape0 = WarpShape0_;
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using WarpShape1 = WarpShape1_;
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using InstructionShape = InstructionShape_;
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using EpilogueOutputOp0 = EpilogueOutputOp0_;
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using EpilogueOutputOp1 = EpilogueOutputOp1_;
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using ThreadblockSwizzle = ThreadblockSwizzle_;
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using Operator = 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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static int const kAlignmentC = EpilogueOutputOp1::kCount;
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static bool const kSplitKSerial = SplitKSerial;
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static bool const kIsBetaZero = IsBetaZero;
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static ComplexTransform const kTransformA = ComplexTransform::kNone;
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static ComplexTransform const kTransformB = ComplexTransform::kNone;
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/// Define the kernel
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using B2bGemmKernel = typename kernel::DefaultB2bGemm<
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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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ThreadblockShape0,
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ThreadblockShape1,
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WarpShape0,
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WarpShape1,
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InstructionShape,
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EpilogueOutputOp0,
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EpilogueOutputOp1,
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ThreadblockSwizzle,
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kStages,
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kSplitKSerial,
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Operator,
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kIsBetaZero
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>::B2bGemmKernel;
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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_0;
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GemmCoord problem_size_1;
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TensorRef<ElementA const, LayoutA> ref_A0;
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TensorRef<ElementB const, LayoutB> ref_B0;
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TensorRef<ElementC const, LayoutC> ref_C0;
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TensorRef<ElementB const, LayoutB> ref_B1;
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TensorRef<ElementC const, LayoutC> ref_C1;
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TensorRef<ElementC, LayoutC> ref_D1;
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typename EpilogueOutputOp0::Params epilogue0;
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typename EpilogueOutputOp1::Params epilogue1;
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int split_k_slices;
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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, 0), problem_size_1(0, 0, 0), split_k_slices(1) {
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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_0_,
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GemmCoord problem_size_1_,
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TensorRef<ElementA const, LayoutA> ref_A0_,
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TensorRef<ElementB const, LayoutB> ref_B0_,
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TensorRef<ElementC const, LayoutC> ref_C0_,
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TensorRef<ElementB const, LayoutB> ref_B1_,
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TensorRef<ElementC const, LayoutC> ref_C1_,
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TensorRef<ElementC, LayoutC> ref_D1_,
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typename EpilogueOutputOp0::Params epilogue0_ =
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typename EpilogueOutputOp0::Params(),
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typename EpilogueOutputOp1::Params epilogue1_ =
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typename EpilogueOutputOp1::Params(),
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int split_k_slices_ = 1
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):
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problem_size_0(problem_size_0_),
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problem_size_1(problem_size_1_),
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ref_A0(ref_A0_),
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ref_B0(ref_B0_),
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ref_C0(ref_C0_),
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ref_B1(ref_B1_),
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ref_C1(ref_C1_),
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ref_D1(ref_D1_),
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epilogue0(epilogue0_),
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epilogue1(epilogue1_),
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split_k_slices(split_k_slices_) {
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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 B2bGemmKernel::Params params_;
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public:
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/// Constructs the GEMM.
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B2bGemm() { }
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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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if (!kSplitKSerial && args.split_k_slices > 1) {
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return Status::kErrorInvalidProblem;
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}
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Status status = B2bGemmKernel::can_implement(
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args.problem_size_0,
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args.problem_size_1,
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args.ref_A0.non_const_ref(),
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args.ref_B0.non_const_ref(),
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args.ref_C0.non_const_ref(),
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args.ref_B1.non_const_ref(),
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args.ref_C1.non_const_ref(),
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args.ref_D1
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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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// Determine grid shape
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ThreadblockSwizzle threadblock_swizzle;
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cutlass::gemm::GemmCoord tiled_shape = threadblock_swizzle.get_tiled_shape(
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args.problem_size_0,
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{ThreadblockShape0::kM, ThreadblockShape0::kN, ThreadblockShape0::kK},
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args.split_k_slices);
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if (kSplitKSerial && args.split_k_slices > 1) {
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bytes += sizeof(int) * size_t(tiled_shape.m()) * size_t(tiled_shape.n());
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}
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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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// 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_0,
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{ThreadblockShape0::kM, ThreadblockShape0::kN, ThreadblockShape0::kK},
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args.split_k_slices);
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// cutlass::gemm::GemmCoord grid_shape_1 = threadblock_swizzle.get_tiled_shape(
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// args.problem_size_1,
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// {ThreadblockShape1::kM, ThreadblockShape1::kN, ThreadblockShape1::kK},
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// args.split_k_slices);
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if (kSplitKSerial) {
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if (args.split_k_slices > 1) {
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if (!workspace) {
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return Status::kErrorWorkspaceNull;
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}
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size_t bytes = get_workspace_size(args);
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cudaError_t result = cudaMemsetAsync(workspace, 0, bytes, stream);
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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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}
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else {
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if (args.split_k_slices > 1) {
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return Status::kErrorInvalidProblem;
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}
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}
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// Initialize the Params structure
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params_ = typename B2bGemmKernel::Params{
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args.problem_size_0,
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args.problem_size_1,
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grid_shape,
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args.ref_A0.non_const_ref(),
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args.ref_B0.non_const_ref(),
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args.ref_C0.non_const_ref(),
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args.ref_B1.non_const_ref(),
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args.ref_C1.non_const_ref(),
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args.ref_D1,
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args.epilogue0,
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args.epilogue1,
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static_cast<int *>(workspace),
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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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if (kSplitKSerial && args.split_k_slices > 1) {
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if (!workspace) {
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return Status::kErrorWorkspaceNull;
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}
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}
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params_.ref_A0.reset(args.ref_A.non_const_ref().data());
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params_.ref_B0.reset(args.ref_B.non_const_ref().data());
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params_.ref_C0.reset(args.ref_C.non_const_ref().data());
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params_.ref_B1.reset(args.ref_B.non_const_ref().data());
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params_.ref_C1.reset(args.ref_C.non_const_ref().data());
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params_.ref_D1.reset(args.ref_D.data());
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params_.output_op_0 = args.epilogue0;
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params_.output_op_1 = args.epilogue1;
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params_.semaphore = static_cast<int *>(workspace);
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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(B2bGemmKernel::kThreadCount, 1, 1);
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cudaError_t result;
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int smem_size = int(sizeof(typename B2bGemmKernel::SharedStorage));
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if (smem_size >= (48 << 10)) {
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result = cudaFuncSetAttribute(Kernel<B2bGemmKernel>,
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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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result = cudaFuncSetAttribute(
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Kernel<B2bGemmKernel>,
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cudaFuncAttributePreferredSharedMemoryCarveout, 100);
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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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cutlass::Kernel<B2bGemmKernel><<<grid, block, smem_size, stream>>>(params_);
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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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||||
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Status status = initialize(args, workspace);
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||||
|
||||
if (status == Status::kSuccess) {
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status = run(stream);
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||||
}
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||||
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return status;
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||||
}
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||||
};
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||||
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||||
} // namespace device
|
||||
} // namespace gemm
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||||
} // namespace cutlass
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
@@ -0,0 +1,274 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017-2021, NVIDIA CORPORATION. All rights reserved.
|
||||
*
|
||||
* Redistribution and use in source and binary forms, with or without modification, are permitted
|
||||
* provided that the following conditions are met:
|
||||
* * Redistributions of source code must retain the above copyright notice, this list of
|
||||
* conditions and the following disclaimer.
|
||||
* * 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.
|
||||
* * Neither the name of the NVIDIA CORPORATION 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 NVIDIA CORPORATION 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 TOR (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 Template for device-level Implicit GEMM
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <limits>
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cutlass/device_kernel.h"
|
||||
#include "cutlass/conv/convolution.h"
|
||||
|
||||
#include "kernel/b2b_implicit_gemm_convolution.h"
|
||||
#include "kernel/default_b2b_conv2d_fprop.h"
|
||||
|
||||
namespace cutlass {
|
||||
namespace conv {
|
||||
namespace device {
|
||||
|
||||
template<typename B2bImplicitGemmKernel_>
|
||||
class B2bImplicitGemmConvolution {
|
||||
public:
|
||||
|
||||
using B2bImplicitGemmKernel = B2bImplicitGemmKernel_;
|
||||
|
||||
using ElementA = typename B2bImplicitGemmKernel::ElementA;
|
||||
using LayoutA = typename B2bImplicitGemmKernel::LayoutA;
|
||||
using ElementB = typename B2bImplicitGemmKernel::ElementB;
|
||||
using LayoutB = typename B2bImplicitGemmKernel::LayoutB;
|
||||
using ElementC = typename B2bImplicitGemmKernel::ElementC;
|
||||
using LayoutC = typename B2bImplicitGemmKernel::LayoutC;
|
||||
using ElementAccumulator = typename B2bImplicitGemmKernel::ElementAccumulator;
|
||||
using ElementCompute = typename B2bImplicitGemmKernel::ElementCompute;
|
||||
using OperatorClass = typename B2bImplicitGemmKernel::OperatorClass;
|
||||
using ArchTag = typename B2bImplicitGemmKernel::ArchTag;
|
||||
using ThreadblockShape0 = typename B2bImplicitGemmKernel::ThreadblockShape0;
|
||||
using ThreadblockShape1 = typename B2bImplicitGemmKernel::ThreadblockShape1;
|
||||
using WarpShape0 = typename B2bImplicitGemmKernel::WarpShape0;
|
||||
using WarpShape1 = typename B2bImplicitGemmKernel::WarpShape1;
|
||||
using InstructionShape = typename B2bImplicitGemmKernel::InstructionShape;
|
||||
using ThreadblockSwizzle = typename B2bImplicitGemmKernel::ThreadblockSwizzle;
|
||||
using EpilogueOutputOp0 = typename B2bImplicitGemmKernel::EpilogueOutputOp0;
|
||||
using EpilogueOutputOp1 = typename B2bImplicitGemmKernel::EpilogueOutputOp1;
|
||||
static int const kStages = B2bImplicitGemmKernel::kStages;
|
||||
static int const kConvDim = B2bImplicitGemmKernel::kConvDim;
|
||||
using WarpMmaOperator0 = typename B2bImplicitGemmKernel::WarpMmaOperator0;
|
||||
using WarpMmaOperator1 = typename B2bImplicitGemmKernel::WarpMmaOperator1;
|
||||
using ArchMmaOperator = typename B2bImplicitGemmKernel::ArchMmaOperator;
|
||||
using MathOperator = typename B2bImplicitGemmKernel::MathOperator;
|
||||
|
||||
static cutlass::conv::Operator const kConvolutionalOperator = B2bImplicitGemmKernel::kConvolutionalOperator;
|
||||
static cutlass::conv::IteratorAlgorithm const kIteratorAlgorithm = B2bImplicitGemmKernel::kIteratorAlgorithm;
|
||||
|
||||
static int const kWarpCount =
|
||||
(ThreadblockShape0::kM / WarpShape0::kM) *
|
||||
(ThreadblockShape0::kN / WarpShape0::kN);
|
||||
|
||||
/// Argument structure
|
||||
using Arguments = typename B2bImplicitGemmKernel::Arguments;
|
||||
|
||||
private:
|
||||
|
||||
/// Kernel parameters object
|
||||
typename B2bImplicitGemmKernel::Params params_;
|
||||
|
||||
public:
|
||||
|
||||
/// Constructs Implicit GEMM
|
||||
B2bImplicitGemmConvolution() { }
|
||||
|
||||
/// Determines whether the Implicit GEMM can execute the given problem.
|
||||
static Status can_implement(Arguments const &args) {
|
||||
|
||||
// dispatch to iterators
|
||||
Status status = B2bImplicitGemmKernel::B2bMma::IteratorA0::can_implement(args.problem_size_0);
|
||||
if (Status::kSuccess != status) {
|
||||
return status;
|
||||
}
|
||||
|
||||
status = B2bImplicitGemmKernel::B2bMma::IteratorB0::can_implement(args.problem_size_0);
|
||||
if (Status::kSuccess != status) {
|
||||
return status;
|
||||
}
|
||||
|
||||
status = B2bImplicitGemmKernel::B2bMma::IteratorB1::can_implement(args.problem_size_1);
|
||||
if (Status::kSuccess != status) {
|
||||
return status;
|
||||
}
|
||||
|
||||
// Determine grid shape
|
||||
ThreadblockSwizzle threadblock_swizzle;
|
||||
|
||||
dim3 grid = threadblock_swizzle.get_grid_shape(
|
||||
threadblock_swizzle.get_tiled_shape(
|
||||
cutlass::conv::implicit_gemm_problem_size(kConvolutionalOperator, args.problem_size_0),
|
||||
{ThreadblockShape0::kM, ThreadblockShape0::kN, ThreadblockShape0::kK},
|
||||
args.problem_size_0.split_k_slices));
|
||||
|
||||
if (!(grid.y <= std::numeric_limits<uint16_t>::max() &&
|
||||
grid.z <= std::numeric_limits<uint16_t>::max())) {
|
||||
|
||||
return Status::kErrorInvalidProblem;
|
||||
}
|
||||
|
||||
return Status::kSuccess;
|
||||
}
|
||||
|
||||
/// Gets the workspace size
|
||||
static size_t get_workspace_size(Arguments const &args) {
|
||||
|
||||
size_t workspace_bytes = 0;
|
||||
|
||||
// Determine grid shape
|
||||
ThreadblockSwizzle threadblock_swizzle;
|
||||
|
||||
cutlass::gemm::GemmCoord grid_tiled_shape = threadblock_swizzle.get_tiled_shape(
|
||||
cutlass::conv::implicit_gemm_problem_size(kConvolutionalOperator, args.problem_size_0),
|
||||
{ThreadblockShape0::kM, ThreadblockShape0::kN, ThreadblockShape0::kK},
|
||||
args.problem_size_0.split_k_slices);
|
||||
|
||||
if(args.split_k_mode == SplitKMode::kParallel) {
|
||||
|
||||
// Split-K parallel: CTAs in k-dimension write the partial results in a temporary workspace.
|
||||
// The user needs to call a reduction operator to optain the final output tensor
|
||||
workspace_bytes =
|
||||
sizeof(ElementAccumulator) *
|
||||
size_t(cutlass::conv::implicit_gemm_tensor_c_size(kConvolutionalOperator, args.problem_size_0)) *
|
||||
size_t(grid_tiled_shape.k());
|
||||
}
|
||||
|
||||
else if(args.split_k_mode == SplitKMode::kSerial && args.problem_size_0.split_k_slices > 1) {
|
||||
|
||||
// Split-K serial: The user workspace is used to store semaphore and serialize writing the
|
||||
// final reduced output to user's output tensor
|
||||
workspace_bytes = sizeof(int) * size_t(grid_tiled_shape.m()) * size_t(grid_tiled_shape.n());
|
||||
}
|
||||
|
||||
return workspace_bytes;
|
||||
}
|
||||
|
||||
/// Initializes GEMM state from arguments.
|
||||
Status initialize(
|
||||
Arguments const &args,
|
||||
void *workspace = nullptr,
|
||||
cudaStream_t stream = nullptr) {
|
||||
|
||||
if (args.problem_size_0.split_k_slices > 1) {
|
||||
|
||||
if (!workspace) {
|
||||
return Status::kErrorWorkspaceNull;
|
||||
}
|
||||
|
||||
cudaError_t status = cudaMemsetAsync(workspace, 0, get_workspace_size(args), stream);
|
||||
|
||||
if (status != cudaSuccess) {
|
||||
return Status::kErrorInternal;
|
||||
}
|
||||
}
|
||||
|
||||
// initialize the params structure from the arguments
|
||||
params_ = typename B2bImplicitGemmKernel::Params(
|
||||
args,
|
||||
static_cast<int *>(workspace)
|
||||
);
|
||||
|
||||
int smem_size = int(sizeof(typename B2bImplicitGemmKernel::SharedStorage));
|
||||
|
||||
if (smem_size >= (48 << 10)) {
|
||||
cudaError_t result = cudaFuncSetAttribute(cutlass::Kernel<B2bImplicitGemmKernel>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
smem_size);
|
||||
|
||||
if (result != cudaSuccess) {
|
||||
return Status::kErrorInternal;
|
||||
}
|
||||
|
||||
result = cudaFuncSetAttribute(
|
||||
cutlass::Kernel<B2bImplicitGemmKernel>,
|
||||
cudaFuncAttributePreferredSharedMemoryCarveout, 100);
|
||||
|
||||
if (result != cudaSuccess) {
|
||||
return Status::kErrorInternal;
|
||||
}
|
||||
}
|
||||
|
||||
return Status::kSuccess;
|
||||
}
|
||||
|
||||
/// Initializes GEMM state from arguments.
|
||||
Status update(Arguments const &args, void *workspace = nullptr) {
|
||||
|
||||
// update the params structure from the arguments
|
||||
params_.ptr_A0 = args.ref_A0.data();
|
||||
params_.ptr_B0 = args.ref_B0.data();
|
||||
params_.ptr_C0 = args.ref_C0.data();
|
||||
params_.ptr_B1 = args.ref_B1.data();
|
||||
params_.ptr_C1 = args.ref_C1.data();
|
||||
params_.ptr_D1 = args.ref_D1.data();
|
||||
params_.output_op_0 = args.output_op_0;
|
||||
params_.output_op_1 = args.output_op_1;
|
||||
params_.semaphore = static_cast<int *>(workspace);
|
||||
|
||||
return Status::kSuccess;
|
||||
}
|
||||
|
||||
/// Runs the kernel using initialized state.
|
||||
Status run(cudaStream_t stream = nullptr) {
|
||||
|
||||
ThreadblockSwizzle threadblock_swizzle;
|
||||
|
||||
dim3 grid = threadblock_swizzle.get_grid_shape(params_.grid_tiled_shape);
|
||||
dim3 block(32 * kWarpCount, 1, 1);
|
||||
|
||||
int smem_size = int(sizeof(typename B2bImplicitGemmKernel::SharedStorage));
|
||||
|
||||
cutlass::Kernel<B2bImplicitGemmKernel><<<grid, block, smem_size, stream>>>(params_);
|
||||
|
||||
cudaError_t result = cudaGetLastError();
|
||||
|
||||
return result == cudaSuccess ? Status::kSuccess : Status::kErrorInternal;
|
||||
}
|
||||
|
||||
/// Runs the kernel using initialized state.
|
||||
Status operator()(cudaStream_t stream = nullptr) {
|
||||
return run(stream);
|
||||
}
|
||||
|
||||
/// Runs the kernel using initialized state.
|
||||
Status operator()(
|
||||
Arguments const &args,
|
||||
void *workspace = nullptr,
|
||||
cudaStream_t stream = nullptr) {
|
||||
|
||||
Status status = initialize(args, workspace);
|
||||
|
||||
if (status == Status::kSuccess) {
|
||||
status = run(stream);
|
||||
}
|
||||
|
||||
return status;
|
||||
}
|
||||
};
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
} // namespace device
|
||||
} // namespace conv
|
||||
} // namespace cutlass
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
Reference in New Issue
Block a user