363 lines
13 KiB
C++
363 lines
13 KiB
C++
/***************************************************************************************************
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* Copyright (c) 2017 - 2024 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 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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#include "cutlass/cuda_host_adapter.hpp"
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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 UnderlyingKernel = ImplicitGemmKernel_;
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using ElementA = typename UnderlyingKernel::ElementA;
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using LayoutA = typename UnderlyingKernel::LayoutA;
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using ElementB = typename UnderlyingKernel::ElementB;
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using LayoutB = typename UnderlyingKernel::LayoutB;
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using ElementC = typename UnderlyingKernel::ElementC;
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using LayoutC = typename UnderlyingKernel::LayoutC;
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using ElementAccumulator = typename UnderlyingKernel::ElementAccumulator;
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using ElementCompute = typename UnderlyingKernel::ElementCompute;
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using OperatorClass = typename UnderlyingKernel::OperatorClass;
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using ArchTag = typename UnderlyingKernel::ArchTag;
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using ThreadblockShape = typename UnderlyingKernel::ThreadblockShape;
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using WarpShape = typename UnderlyingKernel::WarpShape;
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using InstructionShape = typename UnderlyingKernel::InstructionShape;
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using ThreadblockSwizzle = typename UnderlyingKernel::ThreadblockSwizzle;
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using EpilogueOutputOp = typename UnderlyingKernel::EpilogueOutputOp;
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static int const kStages = UnderlyingKernel::kStages;
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static int const kConvDim = UnderlyingKernel::kConvDim;
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using WarpMmaOperator = typename UnderlyingKernel::WarpMmaOperator;
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using ArchMmaOperator = typename UnderlyingKernel::ArchMmaOperator;
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using MathOperator = typename UnderlyingKernel::MathOperator;
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static cutlass::conv::Operator const kConvolutionalOperator = UnderlyingKernel::kConvolutionalOperator;
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static cutlass::conv::IteratorAlgorithm const kIteratorAlgorithm = UnderlyingKernel::kIteratorAlgorithm;
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static cutlass::conv::StrideSupport const kStrideSupport = UnderlyingKernel::kStrideSupport;
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static cutlass::conv::GroupMode const kGroupMode = UnderlyingKernel::kGroupMode;
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static bool const kEnableCudaHostAdapter = CUTLASS_ENABLE_CUDA_HOST_ADAPTER;
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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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(ThreadblockShape::kK / WarpShape::kK);
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/// Argument structure
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using Arguments = typename UnderlyingKernel::Arguments;
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private:
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/// Kernel parameters object
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typename UnderlyingKernel::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 = UnderlyingKernel::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 = UnderlyingKernel::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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// check group conv constraint
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if (args.problem_size.groups != 1) {
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if (kGroupMode == conv::GroupMode::kNone) {
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return Status::kErrorInvalidProblem;
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}
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// C and K should be multiple of groups
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if (args.problem_size.K % args.problem_size.groups ||
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args.problem_size.C % args.problem_size.groups) {
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return Status::kErrorInvalidProblem;
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}
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// split-k is not supported
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if (args.problem_size.split_k_slices != 1) {
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return Status::kErrorInvalidProblem;
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}
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int k_per_group = args.problem_size.K / args.problem_size.groups;
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// k_per_group should be multiple of ThreadblockShape N, one CTA calculate one group
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if (kGroupMode == conv::GroupMode::kSingleGroup && k_per_group % ThreadblockShape::kN) {
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return Status::kErrorInvalidProblem;
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}
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// ThreadblockShape::kN should be divisible by k_per_group, one CTA calculate multiple groups
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if (kGroupMode == conv::GroupMode::kMultipleGroup && ThreadblockShape::kN % k_per_group) {
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return Status::kErrorInvalidProblem;
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}
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// current optimized iterator algo only supports SingleGroup mode
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if (kIteratorAlgorithm == IteratorAlgorithm::kOptimized &&
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kGroupMode != conv::GroupMode::kSingleGroup) {
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return Status::kErrorInvalidProblem;
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}
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}
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static int const kAlignmentC = UnderlyingKernel::Epilogue::OutputTileIterator::kElementsPerAccess;
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if (kConvolutionalOperator == conv::Operator::kFprop) {
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if (args.problem_size.K % kAlignmentC)
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return Status::kErrorMisalignedOperand;
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} else if (kConvolutionalOperator == conv::Operator::kDgrad || kConvolutionalOperator == conv::Operator::kDeconv) {
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if (args.problem_size.C % kAlignmentC)
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return Status::kErrorMisalignedOperand;
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} else if (kConvolutionalOperator == conv::Operator::kWgrad) {
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if (args.problem_size.C % kAlignmentC)
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return Status::kErrorMisalignedOperand;
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}
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// check for unsupported problem sizes for strided dgrad / deconv implementation
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if ((kConvolutionalOperator == conv::Operator::kDgrad || kConvolutionalOperator == conv::Operator::kDeconv) &&
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kStrideSupport == conv::StrideSupport::kStrided) {
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// split-k (serial or parallel) is not supported for strided dgrad / deconv
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if(args.problem_size.split_k_slices > 1) {
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return Status::kErrorNotSupported;
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}
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// dilation > {1x1} is not supported for strided dgrad / deconv
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if(args.problem_size.dilation_h > 1 || args.problem_size.dilation_w > 1) {
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return Status::kErrorNotSupported;
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}
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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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kConvolutionalOperator,
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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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kConvolutionalOperator,
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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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CudaHostAdapter *cuda_adapter = 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 UnderlyingKernel::Params(
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args,
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static_cast<int *>(workspace)
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);
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if constexpr (kEnableCudaHostAdapter) {
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CUTLASS_ASSERT(cuda_adapter);
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return Status::kSuccess;
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}
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else {
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int smem_size = int(sizeof(typename UnderlyingKernel::SharedStorage));
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if (smem_size >= (48 << 10)) {
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cudaError_t result = cudaFuncSetAttribute(cutlass::Kernel<UnderlyingKernel>,
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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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}
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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, CudaHostAdapter *cuda_adapter = 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 UnderlyingKernel::SharedStorage));
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cutlass::Status launch_result = cutlass::Status::kSuccess ;
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if constexpr (kEnableCudaHostAdapter) {
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//
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// Use the cuda host adapter
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//
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CUTLASS_ASSERT(cuda_adapter);
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if (cuda_adapter) {
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void* kernel_params[] = {¶ms_};
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launch_result = cuda_adapter->launch(
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grid, dim3(1,1,1), block, smem_size, stream, kernel_params, 0
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);
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}
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else {
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launch_result = Status::kErrorInternal;
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}
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}
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else {
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cutlass::Kernel<UnderlyingKernel><<<grid, block, smem_size, stream>>>(params_);
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}
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cudaError_t result = cudaGetLastError();
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if (cudaSuccess == result && Status::kSuccess == launch_result) {
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return Status::kSuccess;
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}
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else {
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CUTLASS_TRACE_HOST(" Kernel launch failed. Reason: " << result);
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return Status::kErrorInternal;
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}
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}
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/// Runs the kernel using initialized state.
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Status operator()(cudaStream_t stream = nullptr, CudaHostAdapter *cuda_adapter = nullptr) {
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return run(stream, cuda_adapter);
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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, CudaHostAdapter *cuda_adapter = nullptr) {
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Status status = initialize(args, workspace, stream, cuda_adapter);
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if (status == Status::kSuccess) {
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status = run(stream, cuda_adapter);
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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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