ANIKET SHIVAMandAniket Shivam
66d9cddc83
* New updates. * Minor profiler updates Co-authored-by: Aniket Shivam <ashivam@nvidia.com>
506 lines
18 KiB
C++
506 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 multi-staged Depthwise Convolution 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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/// Parameters structure
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template <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 Arguments_, ///! Kernel Arguments
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typename ConvOutputIteratorParameter_, ///! Output Iterator Params
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typename ConvProblemSize_ = Conv2dProblemSize, ///! Convolutional operator on 2D or 3D problem
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conv::GroupMode GroupMode_ = conv::GroupMode::kNone, ///! Group mode
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typename ThreadBlockOutputShape_ = cutlass::conv::TensorNHWCShape<1, 1, 1, 1> > ///! OutputShape per ThreadBlock
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struct DirectConvolutionParams {
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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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using ThreadBlockOutputShape = ThreadBlockOutputShape_;
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static Operator const kConvolutionalOperator = ConvOperator;
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using ConvProblemSize = ConvProblemSize_;
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using Arguments = Arguments_;
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using ConvOutputIteratorParameter = ConvOutputIteratorParameter_;
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using ThreadblockShape = typename Mma::Shape;
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static IteratorAlgorithm const kIteratorAlgorithm = Mma::IteratorA::kIteratorAlgorithm;
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static conv::GroupMode const kGroupMode = GroupMode_;
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static int const kStages = Mma::kStages;
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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 smem_size_;
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int gemm_k_iterations;
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int gemm_k_iterations_per_channel;
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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::IteratorB::Element *ptr_reordered_B;
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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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int split_k_slices;
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//
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// Methods
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//
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CUTLASS_HOST_DEVICE
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DirectConvolutionParams() : swizzle_log_tile(0), gemm_k_iterations(0) {}
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///
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CUTLASS_HOST_DEVICE
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DirectConvolutionParams(Arguments const &args, int *semaphore = nullptr)
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: problem_size(args.problem_size),
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implicit_gemm_problem_size(
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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(Mma::IteratorB::getParams(args.problem_size, args.ref_B.layout())),
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ptr_B(args.ref_B.data()),
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ptr_reordered_B(args.ref_reordered_B.data()),
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iterator_C(ConvOutputIteratorParameter::layout(args.ref_C), args.problem_size),
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ptr_C(args.ref_C.data()),
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iterator_D(ConvOutputIteratorParameter::layout(args.ref_D), args.problem_size),
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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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split_k_slices(args.problem_size.split_k_slices) {
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gemm_k_iterations =
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depthwise_gemm_k_iterations<ThreadBlockOutputShape::kN,
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ThreadBlockOutputShape::kH,
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ThreadBlockOutputShape::kW>(kConvolutionalOperator,
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ThreadblockShape::kK,
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args.problem_size,
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kIteratorAlgorithm,
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kGroupMode,
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ThreadblockShape::kN);
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gemm_k_iterations_per_channel = implicit_gemm_k_iterations_per_channel(
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kConvolutionalOperator, ThreadblockShape::kK, args.problem_size, kIteratorAlgorithm);
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ThreadblockSwizzle threadblock_swizzle;
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grid_tiled_shape = threadblock_swizzle.get_tiled_shape(
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kConvolutionalOperator,
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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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// Dynamic SMEM usage because stride and dilation are runtime params.
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smem_size_ = (iterator_A.activation_size * kStages + iterator_B.filter_size);
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}
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CUTLASS_HOST_DEVICE
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int get_smem_size() {
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// Dynamic Smem Size
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return smem_size_;
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}
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};
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/////////////////////////////////////////////////////////////////////////////////////////////////
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template <typename Params_, typename ElementB_>
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struct ReorderKernel {
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using Params = Params_;
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using ElementB = ElementB_;
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union SharedStorage {};
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static unsigned int const kReorderKernelThreadPerCTA = 128;
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CUTLASS_HOST_DEVICE
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ReorderKernel() {}
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CUTLASS_HOST_DEVICE
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static dim3 get_grid_shape(Params const ¶ms) {
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return dim3{static_cast<unsigned int>(
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(params.problem_size.filter_size() + kReorderKernelThreadPerCTA - 1) /
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kReorderKernelThreadPerCTA),
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1,
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1};
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}
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CUTLASS_HOST_DEVICE
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static dim3 get_block_shape() { return dim3{kReorderKernelThreadPerCTA, 1, 1}; }
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CUTLASS_HOST_DEVICE
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void operator()(Params const ¶ms, SharedStorage &shared_storage) {
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int64_t m = static_cast<int64_t>(params.problem_size.groups);
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int64_t n = static_cast<int64_t>(params.problem_size.filter_size() / params.problem_size.K);
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const ElementB *src_with_type = static_cast<const ElementB *>(params.ptr_B);
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ElementB *dst_with_type = static_cast<ElementB *>(params.ptr_reordered_B);
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int64_t linear_index = blockIdx.x * kReorderKernelThreadPerCTA + threadIdx.x;
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int64_t index_m = linear_index / n;
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int64_t index_n = linear_index % n;
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int64_t new_linear_index = index_m + index_n * m;
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if (linear_index < m * n) {
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dst_with_type[new_linear_index] = src_with_type[linear_index];
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}
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return;
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}
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};
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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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conv::GroupMode GroupMode_ = conv::GroupMode::kNone, ///! Group mode
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typename ThreadBlockOutputShape_ = cutlass::conv::TensorNHWCShape<1, 1, 1, 1>
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>
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struct DirectConvolution {
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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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using ThreadBlockOutputShape = ThreadBlockOutputShape_;
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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 ElementC = typename EpilogueOutputOp::ElementOutput;
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/// Set output tensor C layout
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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 cutlass::gemm::GemmShape<1, 1, 1>;
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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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static StrideSupport const kStrideSupport = Mma::IteratorA::kStrideSupport;
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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 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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static conv::GroupMode const kGroupMode = GroupMode_;
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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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TensorRefB ref_reordered_B;
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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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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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TensorRefB const & ref_reordered_B = nullptr,
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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_C(ref_C),
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ref_D(ref_D),
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output_op(output_op),
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ref_reordered_B(ref_reordered_B),
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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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using Params =
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typename cutlass::conv::kernel::DirectConvolutionParams<Mma,
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Epilogue,
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ThreadblockSwizzle,
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kConvolutionalOperator,
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Arguments,
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ConvOutputIteratorParameter,
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ConvProblemSize,
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kGroupMode,
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ThreadBlockOutputShape>;
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using ReorderKernel = typename cutlass::conv::kernel::ReorderKernel<Params, ElementB>;
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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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DirectConvolution() { }
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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 threadblock 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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int iterator_column_offset = 0;
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int filter_row_offset = 0;
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if (kGroupMode != GroupMode::kNone) {
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if (kGroupMode == GroupMode::kDepthwise) {
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iterator_column_offset += threadblock_tile_idx.n() * Mma::Shape::kN;
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}
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}
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// Construct iterators to A and B operands
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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() + threadblock_tile_idx.k(),
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iterator_column_offset
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)
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);
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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_reordered_B,
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thread_idx,
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MatrixCoord(
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filter_row_offset,
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iterator_column_offset
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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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//
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// Epilogue
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//
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EpilogueOutputOp output_op(params.output_op);
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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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MatrixCoord threadblock_offset(
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threadblock_tile_idx.m() + threadblock_tile_idx.k(),
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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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// Compute threadblock-scoped matrix multiply-add
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// Epilogue is fused in the mainloop
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mma(params.gemm_k_iterations,
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accumulators,
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iterator_A,
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params.iterator_A,
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iterator_B,
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params.iterator_B,
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accumulators,
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epilogue,
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output_op,
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iterator_D,
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iterator_C,
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params.split_k_slices);
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}
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};
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/////////////////////////////////////////////////////////////////////////////////////////////////
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} // namespace kernel
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} // namespace conv
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} // namespace cutlass
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/////////////////////////////////////////////////////////////////////////////////////////////////
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