6615010cd0
CUTLASS 2.4 (Implicit GEMM Convolution) Co-authored-by: Manish Gupta <manigupta@nvidia.com>, Haicheng Wu <haichengw@nvidia.com>, Dustyn Blasig <dblasig@nvidia.com>, Andrew Kerr <akerr@nvidia.com>
264 lines
9.4 KiB
C++
264 lines
9.4 KiB
C++
/***************************************************************************************************
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* Copyright (c) 2017-2020, 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 device-level Implicit GEMM Convolution
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*/
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#pragma once
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#include <limits>
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#include "cutlass/cutlass.h"
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#include "cutlass/device_kernel.h"
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#include "cutlass/conv/convolution.h"
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/////////////////////////////////////////////////////////////////////////////////////////////////
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namespace cutlass {
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namespace conv {
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namespace device {
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/////////////////////////////////////////////////////////////////////////////////////////////////
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template<typename ImplicitGemmKernel_>
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class ImplicitGemmConvolution {
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public:
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using ImplicitGemmKernel = ImplicitGemmKernel_;
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using ElementA = typename ImplicitGemmKernel::ElementA;
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using LayoutA = typename ImplicitGemmKernel::LayoutA;
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using ElementB = typename ImplicitGemmKernel::ElementB;
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using LayoutB = typename ImplicitGemmKernel::LayoutB;
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using ElementC = typename ImplicitGemmKernel::ElementC;
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using LayoutC = typename ImplicitGemmKernel::LayoutC;
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using ElementAccumulator = typename ImplicitGemmKernel::ElementAccumulator;
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using ElementCompute = typename ImplicitGemmKernel::ElementCompute;
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using OperatorClass = typename ImplicitGemmKernel::OperatorClass;
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using ArchTag = typename ImplicitGemmKernel::ArchTag;
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using ThreadblockShape = typename ImplicitGemmKernel::ThreadblockShape;
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using WarpShape = typename ImplicitGemmKernel::WarpShape;
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using InstructionShape = typename ImplicitGemmKernel::InstructionShape;
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using ThreadblockSwizzle = typename ImplicitGemmKernel::ThreadblockSwizzle;
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using EpilogueOutputOp = typename ImplicitGemmKernel::EpilogueOutputOp;
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static int const kStages = ImplicitGemmKernel::kStages;
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static int const kConvDim = ImplicitGemmKernel::kConvDim;
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using WarpMmaOperator = typename ImplicitGemmKernel::WarpMmaOperator;
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using ArchMmaOperator = typename ImplicitGemmKernel::ArchMmaOperator;
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using MathOperator = typename ImplicitGemmKernel::MathOperator;
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static cutlass::conv::Operator const kConvolutionalOperator = ImplicitGemmKernel::kConvolutionalOperator;
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static cutlass::conv::IteratorAlgorithm const kIteratorAlgorithm = ImplicitGemmKernel::kIteratorAlgorithm;
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static int const kWarpCount =
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(ThreadblockShape::kM / WarpShape::kM) *
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(ThreadblockShape::kN / WarpShape::kN);
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/// Argument structure
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using Arguments = typename ImplicitGemmKernel::Arguments;
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private:
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/// Kernel parameters object
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typename ImplicitGemmKernel::Params params_;
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public:
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/// Constructs Implicit GEMM
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ImplicitGemmConvolution() { }
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/// Determines whether the Implicit GEMM can execute the given problem.
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static Status can_implement(Arguments const &args) {
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// dispatch to iterators
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Status status = ImplicitGemmKernel::Mma::IteratorA::can_implement(args.problem_size);
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if (Status::kSuccess != status) {
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return status;
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}
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status = ImplicitGemmKernel::Mma::IteratorB::can_implement(args.problem_size);
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if (Status::kSuccess != status) {
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return status;
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}
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// Determine grid shape
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ThreadblockSwizzle threadblock_swizzle;
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dim3 grid = threadblock_swizzle.get_grid_shape(
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threadblock_swizzle.get_tiled_shape(
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cutlass::conv::implicit_gemm_problem_size(kConvolutionalOperator, args.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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if (!(grid.y <= std::numeric_limits<uint16_t>::max() &&
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grid.z <= std::numeric_limits<uint16_t>::max())) {
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return Status::kErrorInvalidProblem;
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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 workspace_bytes = 0;
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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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cutlass::conv::implicit_gemm_problem_size(kConvolutionalOperator, args.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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if(args.split_k_mode == SplitKMode::kParallel) {
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// Split-K parallel: CTAs in k-dimension write the partial results in a temporary workspace.
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// The user needs to call a reduction operator to optain the final output tensor
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workspace_bytes =
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sizeof(ElementAccumulator) *
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size_t(cutlass::conv::implicit_gemm_tensor_c_size(kConvolutionalOperator, args.problem_size)) *
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size_t(grid_tiled_shape.k());
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}
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else if(args.split_k_mode == SplitKMode::kSerial && args.problem_size.split_k_slices > 1) {
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// Split-K serial: The user workspace is used to store semaphore and serialize writing the
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// final reduced output to user's output tensor
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workspace_bytes = sizeof(int) * size_t(grid_tiled_shape.m()) * size_t(grid_tiled_shape.n());
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}
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return workspace_bytes;
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}
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/// Initializes GEMM state from arguments.
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Status initialize(
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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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if (args.problem_size.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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cudaError_t status = cudaMemsetAsync(workspace, 0, get_workspace_size(args), stream);
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if (status != cudaSuccess) {
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return Status::kErrorInternal;
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}
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}
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// initialize the params structure from the arguments
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params_ = typename ImplicitGemmKernel::Params(
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args,
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static_cast<int *>(workspace)
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);
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int smem_size = int(sizeof(typename ImplicitGemmKernel::SharedStorage));
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if (smem_size >= (48 << 10)) {
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cudaError_t result = cudaFuncSetAttribute(cutlass::Kernel<ImplicitGemmKernel>,
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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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cutlass::Kernel<ImplicitGemmKernel>,
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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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return Status::kSuccess;
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}
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/// Initializes GEMM state from arguments.
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Status update(Arguments const &args, void *workspace = nullptr) {
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// update the params structure from the arguments
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params_.ptr_A = args.ref_A.data();
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params_.ptr_B = args.ref_B.data();
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params_.ptr_C = args.ref_C.data();
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params_.ptr_D = args.ref_D.data();
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params_.output_op = args.output_op;
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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(32 * kWarpCount, 1, 1);
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int smem_size = int(sizeof(typename ImplicitGemmKernel::SharedStorage));
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cutlass::Kernel<ImplicitGemmKernel><<<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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}
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}
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}
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/////////////////////////////////////////////////////////////////////////////////////////////////
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