/***************************************************************************************************
 * Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 * SPDX-License-Identifier: BSD-3-Clause
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are met:
 *
 * 1. Redistributions of source code must retain the above copyright notice, this
 * list of conditions and the following disclaimer.
 *
 * 2. Redistributions in binary form must reproduce the above copyright notice,
 * this list of conditions and the following disclaimer in the documentation
 * and/or other materials provided with the distribution.
 *
 * 3. Neither the name of the copyright holder nor the names of its
 * contributors may be used to endorse or promote products derived from
 * this software without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
 * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
 * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
 * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
 * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
 * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
 * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
 * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
 * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 *
 **************************************************************************************************/

/*! \file
    \brief 
    Default kernel-level implicit GEMM convolution definitions combine threadblock-scoped 
      matrix multiply-add with the appropriate threadblock-scoped epilogue.  
*/

#pragma once

#include "cutlass/cutlass.h"
#include "cutlass/conv/kernel/default_conv2d.h"

#include "cutlass/conv/threadblock/conv2d_fprop_activation_tile_access_iterator_analytic.h"
#include "cutlass/conv/threadblock/conv2d_fprop_activation_tile_access_iterator_optimized.h"
#include "cutlass/conv/threadblock/conv2d_fprop_activation_tile_access_iterator_fixed_channels.h"
#include "cutlass/conv/threadblock/conv2d_fprop_activation_tile_access_iterator_few_channels.h"

#include "cutlass/conv/threadblock/conv2d_fprop_filter_tile_access_iterator_analytic.h"
#include "cutlass/conv/threadblock/conv2d_fprop_filter_tile_access_iterator_optimized.h"
#include "cutlass/conv/threadblock/conv2d_fprop_filter_tile_access_iterator_fixed_channels.h"
#include "cutlass/conv/threadblock/conv2d_fprop_filter_tile_access_iterator_few_channels.h"

/////////////////////////////////////////////////////////////////////////////////////////////////

namespace cutlass {
namespace conv {
namespace kernel {

/////////////////////////////////////////////////////////////////////////////////////////////////
/// Defines a kernel for Conv2dGroupFpro
template <
  typename ElementA,
  typename LayoutA,
  typename ElementB,
  typename LayoutB,
  typename ElementC,
  typename LayoutC,
  typename ElementAccumulator,
  typename OperatorClass,
  typename ArchTag,
  typename ThreadblockShape,
  typename WarpShape,
  typename InstructionShape,
  typename EpilogueOutputOp,
  typename ThreadblockSwizzle,
  int Stages,
  typename MathOperatorTag,
  conv::GroupMode GroupMode,
  conv::IteratorAlgorithm IteratorAlgorithm = IteratorAlgorithm::kOptimized,
  conv::StrideSupport StrideSupport = StrideSupport::kStrided,
  /// Access granularity of A matrix in units of elements
  int AlignmentA = 128 / cutlass::sizeof_bits<ElementA>::value,
  /// Access granularity of B matrix in units of elements
  int AlignmentB = 128 / cutlass::sizeof_bits<ElementB>::value
> struct DefaultConv2dGroupFprop;

/////////////////////////////////////////////////////////////////////////////////////////////////
//                         OpClassTensorOp convolutions 
/////////////////////////////////////////////////////////////////////////////////////////////////

/// Defines a kernel for Conv2dGroupFprop specialization for Analytic IteratorAlgorithm and multistage 
/// pipeline.
template <
  typename ElementA,
  typename LayoutA,
  typename ElementB,
  typename LayoutB,
  typename ElementC,
  typename LayoutC,
  typename ElementAccumulator,
  typename ArchTag,
  typename ThreadblockShape,
  typename WarpShape,
  typename InstructionShape,
  typename EpilogueOutputOp,
  typename ThreadblockSwizzle,
  int Stages,
  typename MathOperatorTag,
  conv::GroupMode GroupMode,
  conv::StrideSupport StrideSupport, 
  int AlignmentA,
  int AlignmentB
>
struct DefaultConv2dGroupFprop <
  ElementA,
  LayoutA,
  ElementB,
  LayoutB,
  ElementC,
  LayoutC,
  ElementAccumulator,
  arch::OpClassTensorOp,
  ArchTag,
  ThreadblockShape,
  WarpShape,
  InstructionShape,
  EpilogueOutputOp,
  ThreadblockSwizzle,
  Stages,
  MathOperatorTag,
  GroupMode,
  IteratorAlgorithm::kAnalytic,
  StrideSupport,
  AlignmentA,
  AlignmentB
> {

  // Define the core components from GEMM
  using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore<
      ThreadblockShape, WarpShape, InstructionShape, ElementA, layout::RowMajor,
      ElementB, layout::ColumnMajor, ElementAccumulator, layout::RowMajor, arch::OpClassTensorOp,
      Stages, MathOperatorTag>;

  // Define iterators over tiles from the A operand
  using ThreadMapA = typename MmaCore::IteratorThreadMapA;
  using AccessTypeA = cutlass::AlignedArray<ElementA, AlignmentA>;
  using IteratorA =
    cutlass::conv::threadblock::Conv2dFpropActivationTileAccessIteratorAnalytic<
      cutlass::MatrixShape<ThreadblockShape::kM, ThreadblockShape::kK>,
      ElementA, LayoutA,
      ThreadMapA,
      AccessTypeA,
      GroupMode
    >;

  using SmemIteratorA = typename MmaCore::SmemIteratorA;

  // Define iterators over tiles from the B operand
  using ThreadMapB = typename MmaCore::IteratorThreadMapB;
  using AccessTypeB = cutlass::AlignedArray<ElementB, AlignmentB>;
  using IteratorB =
    cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorAnalytic<
      cutlass::MatrixShape<ThreadblockShape::kK, ThreadblockShape::kN>,
      ElementB, LayoutB,
      ThreadMapB,
      AccessTypeB,
      GroupMode
    >;
  
  using SmemIteratorB = typename MmaCore::SmemIteratorB;

  // Warp-level GEMM components
  using WarpMmaTensorOp = typename MmaCore::MmaTensorOp;
  using MmaPolicy = typename MmaCore::MmaPolicy;

  static cutlass::arch::CacheOperation::Kind const CacheOpB =
      ((sizeof_bits<ElementB>::value * AlignmentB) == 128)
          ? cutlass::arch::CacheOperation::Global
          : cutlass::arch::CacheOperation::Always;

  // Define the Mma
  using Mma = threadblock::ImplicitGemmMultistage<
    ThreadblockShape,
    IteratorA,
    SmemIteratorA,
    arch::CacheOperation::Always,
    IteratorB,
    SmemIteratorB,
    CacheOpB,
    MmaPolicy,
    Stages 
  >;

  static const int kPartitionsK = ThreadblockShape::kK / WarpShape::kK;

  // Define the epilogue
  using Epilogue = typename epilogue::threadblock::DefaultEpilogueTensorOp<
    ThreadblockShape,
    WarpMmaTensorOp,
    kPartitionsK,
    EpilogueOutputOp,
    EpilogueOutputOp::kCount
  >::Epilogue;

  // Define the kernel
  using Kernel = cutlass::conv::kernel::ImplicitGemmConvolution<
    Mma,
    Epilogue,
    ThreadblockSwizzle,
    conv::Operator::kFprop,
    Conv2dProblemSize,
    GroupMode
  >;
};

/////////////////////////////////////////////////////////////////////////////////////////////////

} // namespace kernel
} // namespace conv
} // namespace cutlass

/////////////////////////////////////////////////////////////////////////////////////////////////
