Manish Gupta
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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 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 fused activation's scale+bias+relu and Implicit GEMM kernel.
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*/
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#pragma once
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#include "cutlass/cutlass.h"
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#include "cutlass/aligned_buffer.h"
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#include "cutlass/array.h"
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#include "cutlass/numeric_types.h"
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#include "cutlass/matrix_shape.h"
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#include "cutlass/semaphore.h"
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#include "cutlass/tensor_ref.h"
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#include "cutlass/layout/tensor.h"
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#include "cutlass/gemm/gemm.h"
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#include "cutlass/conv/convolution.h"
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#include "cutlass/conv/conv2d_problem_size.h"
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#include "cutlass/conv/conv3d_problem_size.h"
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#include "cutlass/epilogue/threadblock/output_iterator_parameter.h"
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/////////////////////////////////////////////////////////////////////////////////////////////////
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namespace cutlass {
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namespace conv {
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namespace kernel {
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/////////////////////////////////////////////////////////////////////////////////////////////////
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template <
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typename Mma_, ///! Threadblock-scoped matrix multiply-accumulate
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typename Epilogue_, ///! Epilogue
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typename ThreadblockSwizzle_, ///! Threadblock swizzling function
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conv::Operator ConvOperator, ///! Convolutional operator (Fprop, Dgrad, Wgrad)
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typename ConvProblemSize_ = Conv2dProblemSize ///! Convolutional operator on 2D or 3D problem
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>
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struct ImplicitGemmConvolutionFusion {
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using Mma = Mma_;
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using Epilogue = Epilogue_;
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using EpilogueOutputOp = typename Epilogue::OutputOp;
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using ThreadblockSwizzle = ThreadblockSwizzle_;
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static Operator const kConvolutionalOperator = ConvOperator;
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using ElementA = typename Mma::IteratorA::Element;
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using LayoutA = typename Mma::IteratorA::Layout;
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using ElementB = typename Mma::IteratorB::Element;
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using LayoutB = typename Mma::IteratorB::Layout;
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using ElementScaleBias = typename Mma::IteratorScaleBias::Element;
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using LayoutScaleBias = typename Mma::IteratorScaleBias::Layout;
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using ElementC = typename EpilogueOutputOp::ElementOutput;
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using LayoutC = LayoutA;
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using ElementAccumulator = typename EpilogueOutputOp::ElementAccumulator;
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using ElementCompute = typename EpilogueOutputOp::ElementCompute;
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using WarpMmaOperator = typename Mma::Policy::Operator;
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using ArchMmaOperator = typename WarpMmaOperator::ArchMmaOperator;
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using MathOperator = typename ArchMmaOperator::Operator;
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using OperatorClass = typename WarpMmaOperator::OperatorClass;
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using ArchTag = typename WarpMmaOperator::ArchTag;
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using ThreadblockShape = typename Mma::Shape;
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using WarpShape = typename WarpMmaOperator::Shape;
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using InstructionShape = typename ArchMmaOperator::Shape;
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static int const kStages = Mma::kStages;
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static IteratorAlgorithm const kIteratorAlgorithm = Mma::IteratorA::kIteratorAlgorithm;
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/// Warp count (concept: GemmShape)
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using WarpCount = typename Mma::WarpCount;
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static int const kThreadCount = 32 * WarpCount::kCount;
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using TensorRefA = typename Mma::IteratorA::TensorRef;
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using TensorRefB = typename Mma::IteratorB::TensorRef;
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using TensorRefScaleBias = typename Mma::IteratorScaleBias::TensorRef;
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using TensorRefC = cutlass::TensorRef<ElementC, LayoutC>;
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/// Check iterator A and B convolution dimension are the same and
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// set device::ImplicitGemmConvolution::kConvDim
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static_assert(Mma::IteratorA::kConvDim == Mma::IteratorB::kConvDim,
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"Convolution on different different dimensions is not supported");
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static int const kConvDim = Mma::IteratorA::kConvDim;
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/// Conv dimension and problem size structure (Conv2d or Conv3d)
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using ConvProblemSize = ConvProblemSize_;
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/// Wgrad C stride idx for implicit gemm algorithm
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// Conv2d row-major matrix C (KxRSC)
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// Conv3d row-major matrix C (KxTRSC)
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static int const kWgradCStrideIdx =
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cutlass::platform::is_same<LayoutC, cutlass::layout::TensorNHWC>::value ? 2 : 3;
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/// This chooses the appropriate stride element of the C tensor.
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static int const kTensorCStrideIdx =
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(kConvolutionalOperator == conv::Operator::kWgrad ? kWgradCStrideIdx : 0);
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//
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//
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//
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using ConvOutputIteratorParameter = epilogue::threadblock::ConvOutputIteratorParameter<
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LayoutC,
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typename Epilogue::OutputTileIterator::Layout,
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TensorRefC,
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ConvOperator,
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ConvProblemSize
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>;
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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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ConvProblemSize problem_size;
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TensorRefA ref_A;
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TensorRefB ref_B;
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TensorRefScaleBias ref_scale;
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TensorRefScaleBias ref_bias;
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TensorRefC ref_C;
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TensorRefC ref_D;
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typename EpilogueOutputOp::Params output_op;
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SplitKMode split_k_mode;
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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() { }
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CUTLASS_HOST_DEVICE
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Arguments(
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ConvProblemSize const & problem_size
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):
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problem_size(problem_size) { }
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CUTLASS_HOST_DEVICE
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Arguments(
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ConvProblemSize const & problem_size,
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TensorRefA const & ref_A,
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TensorRefB const & ref_B,
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TensorRefScaleBias const & ref_scale,
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TensorRefScaleBias const & ref_bias,
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TensorRefC const & ref_C,
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TensorRefC const & ref_D,
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typename EpilogueOutputOp::Params const & output_op,
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SplitKMode const & split_k_mode = SplitKMode::kSerial
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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_scale(ref_scale),
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ref_bias(ref_bias),
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ref_C(ref_C),
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ref_D(ref_D),
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output_op(output_op),
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split_k_mode(split_k_mode)
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{
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}
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};
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/// Parameters structure
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struct Params {
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ConvProblemSize problem_size;
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cutlass::gemm::GemmCoord grid_tiled_shape;
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gemm::GemmCoord implicit_gemm_problem_size;
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int swizzle_log_tile;
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int gemm_k_iterations;
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typename Mma::IteratorA::Params iterator_A;
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typename Mma::IteratorA::Element const *ptr_A;
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typename Mma::IteratorB::Params iterator_B;
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typename Mma::IteratorB::Element const *ptr_B;
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typename Mma::IteratorScaleBias::Params iterator_scale_bias;
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typename Mma::IteratorScaleBias::Element const *ptr_scale;
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typename Mma::IteratorScaleBias::Element const *ptr_bias;
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typename Epilogue::OutputTileIterator::Params iterator_C;
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typename Epilogue::OutputTileIterator::Element *ptr_C;
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typename Epilogue::OutputTileIterator::Params iterator_D;
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typename Epilogue::OutputTileIterator::Element *ptr_D;
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typename EpilogueOutputOp::Params output_op;
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int *semaphore;
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SplitKMode split_k_mode;
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//
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// Methods
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//
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CUTLASS_HOST_DEVICE
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Params(): swizzle_log_tile(0), gemm_k_iterations(0) { }
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///
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CUTLASS_HOST_DEVICE
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Params(
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Arguments const &args,
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int *semaphore = nullptr
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):
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problem_size(args.problem_size),
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implicit_gemm_problem_size(cutlass::conv::implicit_gemm_problem_size(kConvolutionalOperator, args.problem_size)),
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iterator_A(Mma::IteratorA::getParams(args.problem_size, args.ref_A.layout())),
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ptr_A(args.ref_A.data()),
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iterator_B(args.problem_size, args.ref_B.layout()),
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ptr_B(args.ref_B.data()),
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iterator_scale_bias(args.problem_size, args.ref_scale.layout()),
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ptr_scale(args.ref_scale.data()),
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ptr_bias(args.ref_bias.data()),
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iterator_C(ConvOutputIteratorParameter::layout(args.ref_C)),
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ptr_C(args.ref_C.data()),
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iterator_D(ConvOutputIteratorParameter::layout(args.ref_D)),
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ptr_D(args.ref_D.data()),
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output_op(args.output_op),
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semaphore(semaphore),
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split_k_mode(args.split_k_mode)
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{
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gemm_k_iterations = implicit_gemm_k_iterations(kConvolutionalOperator, ThreadblockShape::kK, args.problem_size);
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ThreadblockSwizzle threadblock_swizzle;
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grid_tiled_shape = threadblock_swizzle.get_tiled_shape(
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implicit_gemm_problem_size,
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{ThreadblockShape::kM, ThreadblockShape::kN, ThreadblockShape::kK},
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args.problem_size.split_k_slices);
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swizzle_log_tile = threadblock_swizzle.get_log_tile(grid_tiled_shape);
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}
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};
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/// Shared memory storage structure
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union SharedStorage {
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typename Mma::SharedStorage main_loop;
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typename Epilogue::SharedStorage epilogue;
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};
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//
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// Methods
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//
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CUTLASS_HOST_DEVICE
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ImplicitGemmConvolutionFusion() { }
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/// Executes one ImplicitGEMM
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CUTLASS_DEVICE
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void operator()(Params const ¶ms, SharedStorage &shared_storage) {
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// Compute threadblock location
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ThreadblockSwizzle threadblock_swizzle;
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cutlass::gemm::GemmCoord threadblock_tile_idx =
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threadblock_swizzle.get_tile_offset(params.swizzle_log_tile);
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// Early exit if CTA is out of range
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if (params.grid_tiled_shape.m() <= threadblock_tile_idx.m() ||
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params.grid_tiled_shape.n() <= threadblock_tile_idx.n()) {
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return;
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}
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// Compute position within threadblock
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int thread_idx = threadIdx.x;
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// Construct iterators to A operand
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typename Mma::IteratorA iterator_A(
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params.iterator_A,
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params.problem_size,
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params.ptr_A,
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thread_idx,
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MatrixCoord(
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threadblock_tile_idx.m() * Mma::Shape::kM,
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threadblock_tile_idx.k() * Mma::Shape::kK
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)
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);
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// Construct iterators to B operand
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typename Mma::IteratorB iterator_B(
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params.iterator_B,
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params.problem_size,
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params.ptr_B,
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thread_idx,
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MatrixCoord(
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threadblock_tile_idx.k() * Mma::Shape::kK,
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threadblock_tile_idx.n() * Mma::Shape::kN
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)
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);
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// Construct iterators to A scale/bias vector
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typename Mma::IteratorScaleBias iterator_scale_bias(
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params.iterator_scale_bias,
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params.problem_size,
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params.ptr_scale,
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params.ptr_bias,
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thread_idx,
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MatrixCoord(
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0, (kConvolutionalOperator == conv::Operator::kFprop) ?
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(threadblock_tile_idx.k() * Mma::Shape::kK) :
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// Wgrad
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(threadblock_tile_idx.n() * Mma::Shape::kN)
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)
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);
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// Broadcast the warp_id computed by lane 0 to ensure dependent code
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// is compiled as warp-uniform.
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int warp_idx = __shfl_sync(0xffffffff, threadIdx.x / 32, 0);
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int lane_idx = threadIdx.x % 32;
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//
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// Main loop
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//
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// Construct thread-scoped matrix multiply
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Mma mma(shared_storage.main_loop, thread_idx, warp_idx, lane_idx);
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typename Mma::FragmentC accumulators;
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accumulators.clear();
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// Compute threadblock-scoped matrix multiply-add
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mma(params.gemm_k_iterations, accumulators, iterator_A,
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iterator_B, iterator_scale_bias, accumulators);
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//
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// Epilogue
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//
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EpilogueOutputOp output_op(params.output_op);
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// Construct the semaphore.
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int block_idx = threadblock_tile_idx.m() + threadblock_tile_idx.n() * params.grid_tiled_shape.m();
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Semaphore semaphore(params.semaphore + block_idx, thread_idx);
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// Compute logical position within grid
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threadblock_tile_idx =
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threadblock_swizzle.get_tile_offset(params.swizzle_log_tile);
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// If performing a reduction via split-K, fetch the initial synchronization
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if (params.split_k_mode == SplitKMode::kSerial && params.grid_tiled_shape.k() > 1) {
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// Fetch the synchronization lock initially but do not block.
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semaphore.fetch();
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// Indicate which position in a serial reduction the output operator is currently updating
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output_op.set_k_partition(threadblock_tile_idx.k(), params.grid_tiled_shape.k());
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}
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MatrixCoord threadblock_offset(
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threadblock_tile_idx.m() * Mma::Shape::kM,
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threadblock_tile_idx.n() * Mma::Shape::kN
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);
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// Tile iterator writing to destination tensor
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typename Epilogue::OutputTileIterator iterator_D(
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params.iterator_D,
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params.ptr_D,
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ConvOutputIteratorParameter::extent(params.problem_size),
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thread_idx,
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threadblock_offset
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);
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// Tile iterator reading from source accumulator tensor
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typename Epilogue::OutputTileIterator iterator_C(
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params.iterator_C,
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params.ptr_C,
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ConvOutputIteratorParameter::extent(params.problem_size),
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thread_idx,
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threadblock_offset
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);
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// Construct the epilogue
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Epilogue epilogue(
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shared_storage.epilogue,
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thread_idx,
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warp_idx,
|
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lane_idx);
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// Wait on the semaphore - this latency may have been covered by iterator construction
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if (params.split_k_mode == SplitKMode::kSerial && params.grid_tiled_shape.k() > 1) {
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// For subsequent threadblocks, the source matrix is held in the 'D' tensor.
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if (threadblock_tile_idx.k()) {
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iterator_C = iterator_D;
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}
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semaphore.wait(threadblock_tile_idx.k());
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}
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// Each split-k-slice writes to a unique tensor location
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else if (params.split_k_mode == SplitKMode::kParallel) {
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iterator_D.add_pointer_offset(threadblock_tile_idx.k() *
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cutlass::conv::implicit_gemm_tensor_c_size(ConvOperator, params.problem_size));
|
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}
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|
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// Run efficient epilogue
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epilogue(output_op, iterator_D, accumulators, iterator_C);
|
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|
||||
//
|
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// Release the semaphore
|
||||
//
|
||||
|
||||
if (params.split_k_mode == SplitKMode::kSerial && params.grid_tiled_shape.k() > 1) {
|
||||
|
||||
int lock = 0;
|
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if (params.grid_tiled_shape.k() == threadblock_tile_idx.k() + 1) {
|
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|
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// The final threadblock resets the semaphore for subsequent grids.
|
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lock = 0;
|
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}
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else {
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// Otherwise, the semaphore is incremented
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lock = threadblock_tile_idx.k() + 1;
|
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}
|
||||
|
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semaphore.release(lock);
|
||||
}
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||||
}
|
||||
};
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||||
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||||
/////////////////////////////////////////////////////////////////////////////////////////////////
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||||
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||||
} // namespace kernel
|
||||
} // namespace conv
|
||||
} // namespace cutlass
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
Reference in New Issue
Block a user