358 lines
12 KiB
C++
358 lines
12 KiB
C++
/***************************************************************************************************
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* Copyright (c) 2017 - 2025 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
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Default kernel-level fused activation's scale+bias+relu and implicit GEMM convolution
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definitions that combine threadblock-scoped matrix multiply-add with the
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appropriate threadblock-scoped epilogue.
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*/
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#pragma once
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#include "cutlass/cutlass.h"
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#include "cutlass/conv/kernel/default_conv2d.h"
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#include "cutlass/conv/threadblock/conv2d_fprop_activation_tile_access_iterator_analytic.h"
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#include "cutlass/conv/threadblock/conv2d_fprop_filter_tile_access_iterator_analytic.h"
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#include "cutlass/conv/threadblock/conv2d_fprop_activation_tile_access_iterator_optimized.h"
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#include "cutlass/conv/threadblock/conv2d_fprop_filter_tile_access_iterator_optimized.h"
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#include "cutlass/conv/threadblock/predicated_scale_bias_vector_access_iterator.h"
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#include "cutlass/transform/threadblock/regular_scale_bias_vector_access_iterator.h"
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#include "cutlass/gemm/warp/scale_bias_tile_iterator.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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/// Defines a kernel for fused batch norm and Conv2dFprop
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template <
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typename ElementA,
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typename LayoutA,
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typename ElementB,
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typename LayoutB,
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typename ElementScaleBias,
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typename LayoutScaleBias,
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typename ElementC,
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typename LayoutC,
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typename ElementAccumulator,
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typename OperatorClass,
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typename ArchTag,
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typename ThreadblockShape,
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typename WarpShape,
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typename InstructionShape,
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typename EpilogueOutputOp,
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typename ThreadblockSwizzle,
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int Stages,
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typename MathOperatorTag,
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conv::IteratorAlgorithm IteratorAlgorithm = IteratorAlgorithm::kOptimized,
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conv::StrideSupport StrideSupport = StrideSupport::kUnity
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> struct DefaultConv2dFpropFusion;
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/////////////////////////////////////////////////////////////////////////////////////////////////
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// OpClassTensorOp convolutions
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/////////////////////////////////////////////////////////////////////////////////////////////////
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/// Defines a kernel for Conv2dFprop specialization for Analytic IteratorAlgorithm and multistage
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/// pipeline.
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template <
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typename ElementA,
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typename LayoutA,
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typename ElementB,
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typename LayoutB,
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typename ElementScaleBias,
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typename LayoutScaleBias,
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typename ElementC,
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typename LayoutC,
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typename ElementAccumulator,
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typename ArchTag,
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typename ThreadblockShape,
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typename WarpShape,
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typename InstructionShape,
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typename EpilogueOutputOp,
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typename ThreadblockSwizzle,
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int Stages,
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typename MathOperatorTag
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>
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struct DefaultConv2dFpropFusion <
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ElementA,
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LayoutA,
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ElementB,
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LayoutB,
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ElementScaleBias,
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LayoutScaleBias,
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ElementC,
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LayoutC,
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ElementAccumulator,
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arch::OpClassTensorOp,
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ArchTag,
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ThreadblockShape,
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WarpShape,
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InstructionShape,
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EpilogueOutputOp,
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ThreadblockSwizzle,
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Stages,
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MathOperatorTag,
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IteratorAlgorithm::kAnalytic
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> {
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// Define the core components from GEMM
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using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore<
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ThreadblockShape, WarpShape, InstructionShape, ElementA, layout::RowMajor,
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ElementB, layout::ColumnMajor, ElementAccumulator, layout::RowMajor, arch::OpClassTensorOp,
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Stages, MathOperatorTag>;
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// Define iterators over tiles from the A operand
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using ThreadMapA = typename MmaCore::IteratorThreadMapA;
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using IteratorA =
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cutlass::conv::threadblock::Conv2dFpropActivationTileAccessIteratorAnalytic<
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cutlass::MatrixShape<ThreadblockShape::kM, ThreadblockShape::kK>,
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ElementA, LayoutA,
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ThreadMapA
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>;
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using SmemIteratorA = typename MmaCore::SmemIteratorA;
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// Define iterators over tiles from the B operand
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using ThreadMapB = typename MmaCore::IteratorThreadMapB;
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using IteratorB =
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cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorAnalytic<
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cutlass::MatrixShape<ThreadblockShape::kK, ThreadblockShape::kN>,
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ElementB, LayoutB,
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ThreadMapB
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>;
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using SmemIteratorB = typename MmaCore::SmemIteratorB;
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/// Define iterators over tiles from scale/bias vectors
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using IteratorScaleBias =
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cutlass::conv::threadblock::PredicatedScaleBiasVectorAccessIterator<
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cutlass::MatrixShape<1, ThreadblockShape::kK>, ElementScaleBias,
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LayoutScaleBias>;
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using SmemIteratorScaleBias =
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cutlass::transform::threadblock::RegularScaleBiasVectorAccessIterator<
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cutlass::MatrixShape<1, ThreadblockShape::kK>, ElementScaleBias,
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LayoutScaleBias>;
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// Warp-level GEMM components
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using WarpMmaTensorOp = typename MmaCore::MmaTensorOp;
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using MmaPolicy = typename MmaCore::MmaPolicy;
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static int const kThreadCount = 32;
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// Warp-level iterators to load scale and bias vectors
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using WarpIteratorScaleBias = cutlass::gemm::warp::ScaleBiasTileIterator<
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MatrixShape<WarpShape::kM, WarpShape::kK>, ElementScaleBias,
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LayoutScaleBias, MatrixShape<InstructionShape::kM, InstructionShape::kK>,
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typename WarpMmaTensorOp::IteratorA::Base::Policy, kThreadCount,
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MmaCore::WarpCount::kK>;
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// Define the Mma
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using Mma = threadblock::ImplicitGemmFpropFusionMultistage<
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ThreadblockShape,
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IteratorA,
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SmemIteratorA,
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arch::CacheOperation::Always,
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IteratorB,
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SmemIteratorB,
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arch::CacheOperation::Global,
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IteratorScaleBias,
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SmemIteratorScaleBias,
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arch::CacheOperation::Always,
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MmaPolicy,
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WarpIteratorScaleBias,
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Stages
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>;
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// Define the epilogue
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using Epilogue = typename epilogue::threadblock::DefaultEpilogueTensorOp<
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ThreadblockShape,
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WarpMmaTensorOp,
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1,
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EpilogueOutputOp,
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EpilogueOutputOp::kCount
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>::Epilogue;
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// Define the kernel
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using Kernel = cutlass::conv::kernel::ImplicitGemmConvolutionFusion<
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Mma,
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Epilogue,
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ThreadblockSwizzle,
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conv::Operator::kFprop
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>;
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};
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/////////////////////////////////////////////////////////////////////////////////////////////////
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/// Defines a kernel for Conv2dFprop specialization for Optimized IteratorAlgorithm and
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/// multistage pipeline.
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template <
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typename ElementA,
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typename LayoutA,
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typename ElementB,
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typename LayoutB,
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typename ElementScaleBias,
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typename LayoutScaleBias,
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typename ElementC,
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typename LayoutC,
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typename ElementAccumulator,
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typename ArchTag,
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typename ThreadblockShape,
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typename WarpShape,
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typename InstructionShape,
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typename EpilogueOutputOp,
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typename ThreadblockSwizzle,
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int Stages,
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typename MathOperatorTag
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>
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struct DefaultConv2dFpropFusion <
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ElementA,
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LayoutA,
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ElementB,
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LayoutB,
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ElementScaleBias,
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LayoutScaleBias,
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ElementC,
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LayoutC,
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ElementAccumulator,
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arch::OpClassTensorOp,
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ArchTag,
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ThreadblockShape,
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WarpShape,
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InstructionShape,
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EpilogueOutputOp,
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ThreadblockSwizzle,
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Stages,
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MathOperatorTag,
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IteratorAlgorithm::kOptimized
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> {
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// Define the core components from GEMM
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using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore<
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ThreadblockShape, WarpShape, InstructionShape, ElementA, layout::RowMajor,
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ElementB, layout::ColumnMajor, ElementAccumulator, layout::RowMajor, arch::OpClassTensorOp,
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Stages, MathOperatorTag
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>;
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// Define iterators over tiles from the A operand
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using ThreadMapA = typename MmaCore::IteratorThreadMapA;
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using IteratorA =
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cutlass::conv::threadblock::Conv2dFpropActivationTileAccessIteratorOptimized<
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cutlass::MatrixShape<ThreadblockShape::kM, ThreadblockShape::kK>,
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ElementA,
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LayoutA,
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ThreadMapA
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>;
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using SmemIteratorA = typename MmaCore::SmemIteratorA;
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// Define iterators over tiles from the B operand
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using ThreadMapB = typename MmaCore::IteratorThreadMapB;
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using IteratorB =
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cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorOptimized<
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cutlass::MatrixShape<ThreadblockShape::kK, ThreadblockShape::kN>,
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ElementB,
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LayoutB,
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ThreadMapB
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>;
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using SmemIteratorB = typename MmaCore::SmemIteratorB;
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/// Define iterators over tiles from scale/bias vectors
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using IteratorScaleBias =
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cutlass::conv::threadblock::PredicatedScaleBiasVectorAccessIterator<
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cutlass::MatrixShape<1, ThreadblockShape::kK>, ElementScaleBias,
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LayoutScaleBias>;
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using SmemIteratorScaleBias =
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cutlass::transform::threadblock::RegularScaleBiasVectorAccessIterator<
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cutlass::MatrixShape<1, ThreadblockShape::kK>, ElementScaleBias,
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LayoutScaleBias>;
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// Warp-level GEMM components
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using WarpMmaTensorOp = typename MmaCore::MmaTensorOp;
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using MmaPolicy = typename MmaCore::MmaPolicy;
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static int const kThreadCount = 32;
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// Warp-level iterators to load scale and bias vectors
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using WarpIteratorScaleBias = cutlass::gemm::warp::ScaleBiasTileIterator<
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MatrixShape<WarpShape::kM, WarpShape::kK>, ElementScaleBias,
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LayoutScaleBias, MatrixShape<InstructionShape::kM, InstructionShape::kK>,
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typename WarpMmaTensorOp::IteratorA::Base::Policy, kThreadCount,
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MmaCore::WarpCount::kK>;
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// Define the Mma
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using Mma = threadblock::ImplicitGemmFpropFusionMultistage<
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ThreadblockShape,
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IteratorA,
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SmemIteratorA,
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arch::CacheOperation::Always,
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IteratorB,
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SmemIteratorB,
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arch::CacheOperation::Global,
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IteratorScaleBias,
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SmemIteratorScaleBias,
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arch::CacheOperation::Always,
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MmaPolicy,
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WarpIteratorScaleBias,
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Stages
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>;
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// Define the epilogue
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using Epilogue = typename epilogue::threadblock::DefaultEpilogueTensorOp<
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ThreadblockShape,
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WarpMmaTensorOp,
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1,
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EpilogueOutputOp,
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EpilogueOutputOp::kCount
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>::Epilogue;
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// Define the kernel
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using Kernel = cutlass::conv::kernel::ImplicitGemmConvolutionFusion<
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Mma,
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Epilogue,
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ThreadblockSwizzle,
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conv::Operator::kFprop
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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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