548 lines
18 KiB
C++
548 lines
18 KiB
C++
/***************************************************************************************************
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* Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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* SPDX-License-Identifier: BSD-3-Clause
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions are met:
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*
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* 1. Redistributions of source code must retain the above copyright notice, this
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* list of conditions and the following disclaimer.
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*
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* 2. Redistributions in binary form must reproduce the above copyright notice,
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* this list of conditions and the following disclaimer in the documentation
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* and/or other materials provided with the distribution.
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*
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* 3. Neither the name of the copyright holder nor the names of its
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* contributors may be used to endorse or promote products derived from
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* this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*
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**************************************************************************************************/
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/*! \file
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\brief Template for a pipelined Rank2K 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/blas3.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/rank_2k_universal.h"
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#include "cutlass/gemm/kernel/default_rank_2k_universal.h"
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#include "cutlass/gemm/device/default_gemm_configuration.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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/// Fill Mode for C (kLower or kUpper)
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FillMode FillModeC,
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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::OpClassTensorOp,
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/// Tag indicating architecture to tune for
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typename ArchTag_ = arch::Sm80,
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/// Threadblock-level tile size (concept: GemmShape)
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typename ThreadblockShape_ = typename DefaultGemmConfiguration<
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OperatorClass_, ArchTag_, ElementA_, ElementA_, 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_, ElementA_, 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_, ElementA_, ElementC_,
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ElementAccumulator_>::InstructionShape,
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/// Epilogue output operator
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typename EpilogueOutputOp_ = typename DefaultGemmConfiguration<
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OperatorClass_, ArchTag_, ElementA_, ElementA_, ElementC_,
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ElementAccumulator_>::EpilogueOutputOp,
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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_, ElementA_,
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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_, ElementA_,
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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_, ElementB_, 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 SYRK
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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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/// Complex elementwise transformation
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ComplexTransform TransformA = ComplexTransform::kNone,
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/// Complex elementwise transformation
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ComplexTransform TransformB = ComplexTransform::kNone,
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/// Blas3 computation mode (symmetric/hermitian)
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BlasMode BlasMode_ = BlasMode::kSymmetric>
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class Rank2K {
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public:
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using ElementA = ElementA_;
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using LayoutA = LayoutA_;
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using ElementB = ElementB_;
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using LayoutB = 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 EpilogueOutputOp = EpilogueOutputOp_;
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using ThreadblockSwizzle = ThreadblockSwizzle_;
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using Operator = Operator_;
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static FillMode const kFillModeC = FillModeC;
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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 = EpilogueOutputOp::kCount;
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static bool const kSplitKSerial = SplitKSerial;
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static ComplexTransform const kTransformA = TransformA;
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static ComplexTransform const kTransformB = TransformB;
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static BlasMode const kBlasMode = BlasMode_;
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static int const kUpdateRank = 2;
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// static asserts for rank 2k update kernel
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static_assert(platform::is_same<LayoutA, LayoutB>::value,
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"Rank 2K update operator support same layouts for operandA and B");
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/// Define the kernel
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using Rank2Kkernel = typename kernel::DefaultRank2KUniversal<
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ElementA,
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LayoutA,
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kTransformA,
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kAlignmentA,
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ElementB,
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LayoutB,
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kTransformB,
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kAlignmentB,
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ElementC,
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LayoutC,
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kFillModeC,
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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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EpilogueOutputOp,
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ThreadblockSwizzle,
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kStages,
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kSplitKSerial,
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Operator,
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kBlasMode
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>::Rank2Kkernel;
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using Arguments = typename Rank2Kkernel::Arguments;
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private:
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/// Kernel parameters object
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typename Rank2Kkernel::Params params_;
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public:
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/// Constructs the SYRK.
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Rank2K() { }
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/// Determines whether the SYRK can execute the given problem.
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static Status can_implement(Arguments const &args) {
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if (!kSplitKSerial && args.batch_count > 1) {
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return Status::kErrorInvalidProblem;
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}
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Status status = Rank2Kkernel::can_implement(args);
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if (FillModeC != FillMode::kLower && FillModeC != FillMode::kUpper) {
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return Status::kErrorInvalidProblem;
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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,
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{ThreadblockShape::kM, ThreadblockShape::kN, ThreadblockShape::kK},
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args.batch_count);
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if (kSplitKSerial && args.batch_count > 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 SYRK 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_tiled_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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args.batch_count);
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if (kSplitKSerial) {
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if (args.batch_count > 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.batch_count > 1) {
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return Status::kErrorInvalidProblem;
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}
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}
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int gemm_k_size = args.problem_size.k();
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// Initialize the Params structure
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params_ = typename Rank2Kkernel::Params{
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args,
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grid_tiled_shape,
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gemm_k_size,
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static_cast<int *>(workspace)
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};
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int smem_size = int(sizeof(typename Rank2Kkernel::SharedStorage));
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if (smem_size >= (48 << 10)) {
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cudaError_t result = cudaFuncSetAttribute(Kernel<Rank2Kkernel>,
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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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if (kSplitKSerial && args.batch_count > 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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size_t workspace_bytes = get_workspace_size(args);
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if (workspace_bytes && !workspace) {
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return Status::kErrorWorkspaceNull;
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}
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params_.update(args, 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(Rank2Kkernel::kThreadCount, 1, 1);
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int smem_size = int(sizeof(typename Rank2Kkernel::SharedStorage));
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cutlass::Kernel<Rank2Kkernel><<<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);
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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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////////////////////////////////////////////////////////////////////////////////
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/// Partial specialization for column-major output exchange operand.
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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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/// Fill Mode for C (kLower or kUpper)
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FillMode FillModeC,
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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. This is the minimum SM that
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/// supports the intended feature. The device kernel can be built
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/// targeting any SM larger than this number.
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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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/// Instruction-level tile size (concept: GemmShape)
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typename InstructionShape_,
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/// Epilogue output operator
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typename EpilogueOutputOp_,
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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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/// Access granularity of A matrix in units of elements
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int AlignmentA,
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/// Access granularity of B matrix in units of elements
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int AlignmentB,
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/// If true, kernel supports split-K with serial reduction
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bool SplitKSerial,
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/// Operation performed by Rank2K update kernel
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typename Operator_,
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/// Complex elementwise transformation
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ComplexTransform TransformA,
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/// Complex elementwise transformation
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ComplexTransform TransformB,
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/// Blas3 computation mode (symmetric/hermitian)
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BlasMode BlasMode_
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>
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class Rank2K<ElementA_, LayoutA_, ElementB_, LayoutB_, ElementC_,
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layout::ColumnMajor, // partially specialized on LayoutC
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FillModeC, ElementAccumulator_, OperatorClass_, ArchTag_, ThreadblockShape_,
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WarpShape_, InstructionShape_, EpilogueOutputOp_,
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ThreadblockSwizzle_, Stages, AlignmentA, AlignmentB,
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SplitKSerial, Operator_, TransformA, TransformB, BlasMode_> {
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public:
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using ElementA = ElementA_;
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using LayoutA = LayoutA_;
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using ElementB = ElementB_;
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using LayoutB = LayoutB_;
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using ElementC = ElementC_;
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using LayoutC = layout::ColumnMajor;
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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 EpilogueOutputOp = EpilogueOutputOp_;
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using ThreadblockSwizzle = ThreadblockSwizzle_;
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using Operator = Operator_;
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static FillMode const kFillModeC = FillModeC;
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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 = EpilogueOutputOp::kCount;
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static bool const kSplitKSerial = SplitKSerial;
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static BlasMode const kBlasMode = BlasMode_;
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static ComplexTransform const kTransformA = TransformA;
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static ComplexTransform const kTransformB = TransformB;
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static int const kUpdateRank = 2;
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/// Define the kernel
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using UnderlyingOperator = typename cutlass::gemm::device::Rank2K<
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ElementB,
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LayoutB,
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ElementA,
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LayoutA,
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ElementC,
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layout::RowMajor,
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InvertFillMode<FillModeC>::mode,
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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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EpilogueOutputOp,
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ThreadblockSwizzle,
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kStages,
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kAlignmentB,
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kAlignmentA,
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kSplitKSerial,
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Operator,
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kTransformA,
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kTransformB,
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kBlasMode
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>;
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/// Argument structure
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using Arguments = typename UnderlyingOperator::Arguments;
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using Rank2Kkernel = typename UnderlyingOperator::Rank2Kkernel;
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private:
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UnderlyingOperator underlying_operator_;
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public:
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/// Constructs the Rank2K.
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Rank2K() { }
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/// Helper to construct a transposed equivalent for the underying Rank2K operator
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static Arguments to_underlying_arguments(Arguments const &args) {
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return args.transposed_problem();
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}
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/// Determines whether the Rank2K can execute the given problem.
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static Status can_implement(Arguments const &args) {
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return UnderlyingOperator::can_implement(to_underlying_arguments(args));
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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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return UnderlyingOperator::get_workspace_size(to_underlying_arguments(args));
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}
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/// Computes the grid shape
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static dim3 get_grid_shape(Arguments const &args) {
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return UnderlyingOperator::get_grid_shape(to_underlying_arguments(args));
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}
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/// Computes the maximum number of active blocks per multiprocessor
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static int maximum_active_blocks(int smem_capacity = -1) {
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return UnderlyingOperator::maximum_active_blocks(smem_capacity);
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}
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/// Initializes Rank2K state from arguments.
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Status initialize(Arguments const &args, void *workspace = nullptr, cudaStream_t stream = nullptr) {
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return underlying_operator_.initialize(to_underlying_arguments(args), workspace, stream);
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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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return underlying_operator_.update(to_underlying_arguments(args), workspace);
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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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return underlying_operator_.run(stream);
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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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////////////////////////////////////////////////////////////////////////////////
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} // namespace device
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} // namespace Rank2K
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} // namespace cutlass
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////////////////////////////////////////////////////////////////////////////////
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