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/*! \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_wgrad_output_gradient_tile_access_iterator_analytic.h"
#include "cutlass/conv/threadblock/conv2d_wgrad_activation_tile_access_iterator_analytic.h"
#include "cutlass/conv/threadblock/conv2d_wgrad_output_gradient_tile_access_iterator_optimized.h"
#include "cutlass/conv/threadblock/conv2d_wgrad_activation_tile_access_iterator_optimized.h"
#include "cutlass/conv/threadblock/conv2d_tile_iterator.h"
#include "cutlass/conv/threadblock/predicated_scale_bias_vector_iterator.h"

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

namespace cutlass {
namespace conv {
namespace kernel {

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

/// Defines a kernel for Conv2dWgrad
template <
  typename ElementA,
  typename LayoutA,
  typename ElementB,
  typename LayoutB,
  typename ElementScaleBias,
  typename LayoutScaleBias,
  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::IteratorAlgorithm IteratorAlgorithm = IteratorAlgorithm::kOptimized,
  conv::StrideSupport StrideSupport = StrideSupport::kStrided
> struct DefaultConv2dWgradFusion;

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

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

/// Defines a kernel for Conv2dWgrad specialization for Analytic IteratorAlgorithm and multistage 
// pipeline.
template <
  typename ElementA,
  typename LayoutA,
  typename ElementB,
  typename LayoutB,
  typename ElementScaleBias,
  typename LayoutScaleBias,
  typename ElementC,
  typename LayoutC,
  typename ElementAccumulator,
  typename OperatorClass,
  typename ArchTag,
  typename ThreadblockShape,
  typename WarpShape,
  typename InstructionShape,
  typename EpilogueOutputOp,
  typename ThreadblockSwizzle,
  int Stages,
  typename MathOperatorTag
>
struct DefaultConv2dWgradFusion <
  ElementA,
  LayoutA,
  ElementB,
  LayoutB,
  ElementScaleBias,
  LayoutScaleBias,
  ElementC,
  LayoutC,
  ElementAccumulator,
  OperatorClass,
  ArchTag,
  ThreadblockShape,
  WarpShape,
  InstructionShape,
  EpilogueOutputOp,
  ThreadblockSwizzle,
  Stages,
  MathOperatorTag,
  IteratorAlgorithm::kAnalytic
>  {

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

  // Define iterators over tiles from the A operand
  using ThreadMapA = typename MmaCore::IteratorThreadMapA;
  using IteratorA =
    cutlass::conv::threadblock::Conv2dWgradOutputGradientTileAccessIteratorAnalytic<
      cutlass::MatrixShape<ThreadblockShape::kM, ThreadblockShape::kK>,
      ElementA,
      ThreadMapA
    >;

  using SmemIteratorA = typename MmaCore::SmemIteratorA;

  // Define iterators over tiles from the B operand
  using ThreadMapB = typename MmaCore::IteratorThreadMapB;
  using IteratorB =
    cutlass::conv::threadblock::Conv2dWgradActivationTileAccessIteratorAnalytic<
      cutlass::MatrixShape<ThreadblockShape::kK, ThreadblockShape::kN>,
      ElementB,
      ThreadMapB
    >;
  
  using SmemIteratorB = typename MmaCore::SmemIteratorB;

  /// Define iterators over tiles from scale/bias vectors
  using IteratorScaleBias =
      cutlass::conv::threadblock::PredicatedScaleBiasVectorIterator<
          cutlass::MatrixShape<1, WarpShape::kN>,
          ElementScaleBias,
          LayoutScaleBias>;

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

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

  // Define the epilogue
  using Epilogue = typename epilogue::threadblock::DefaultEpilogueTensorOp<
    ThreadblockShape,
    WarpMmaTensorOp,
    1,
    EpilogueOutputOp,
    EpilogueOutputOp::kCount
  >::Epilogue;

  // Define the kernel
  using Kernel = cutlass::conv::kernel::ImplicitGemmConvolutionFusion<
    Mma,
    Epilogue,
    ThreadblockSwizzle,
    conv::Operator::kWgrad
  >;
};

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

/// Defines a kernel for Conv2dWgrad specialization for Optimized IteratorAlgorithm and multistage 
// pipeline.
template <
  typename ElementA,
  typename LayoutA,
  typename ElementB,
  typename LayoutB,
  typename ElementScaleBias,
  typename LayoutScaleBias,
  typename ElementC,
  typename LayoutC,
  typename ElementAccumulator,
  typename OperatorClass,
  typename ArchTag,
  typename ThreadblockShape,
  typename WarpShape,
  typename InstructionShape,
  typename EpilogueOutputOp,
  typename ThreadblockSwizzle,
  int Stages,
  typename MathOperatorTag
>
struct DefaultConv2dWgradFusion <
  ElementA,
  LayoutA,
  ElementB,
  LayoutB,
  ElementScaleBias,
  LayoutScaleBias,
  ElementC,
  LayoutC,
  ElementAccumulator,
  OperatorClass,
  ArchTag,
  ThreadblockShape,
  WarpShape,
  InstructionShape,
  EpilogueOutputOp,
  ThreadblockSwizzle,
  Stages,
  MathOperatorTag,
  IteratorAlgorithm::kOptimized
>  {

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

  // Define iterators over tiles from the A operand
  using ThreadMapA = typename MmaCore::IteratorThreadMapA;
  using IteratorA =
    cutlass::conv::threadblock::Conv2dWgradOutputGradientTileAccessIteratorOptimized<
      cutlass::MatrixShape<ThreadblockShape::kM, ThreadblockShape::kK>,
      ElementA,
      ThreadMapA
    >;

  using SmemIteratorA = typename MmaCore::SmemIteratorA;

  // Define iterators over tiles from the B operand
  using ThreadMapB = typename MmaCore::IteratorThreadMapB;
  using IteratorB =
    cutlass::conv::threadblock::Conv2dWgradActivationTileAccessIteratorOptimized<
      cutlass::MatrixShape<ThreadblockShape::kK, ThreadblockShape::kN>,
      ElementB,
      ThreadMapB
    >;
  
  using SmemIteratorB = typename MmaCore::SmemIteratorB;

  /// Define iterators over tiles from scale/bias vectors
  using IteratorScaleBias =
      cutlass::conv::threadblock::PredicatedScaleBiasVectorIterator<
          cutlass::MatrixShape<1, WarpShape::kN>,
          ElementScaleBias,
          LayoutScaleBias>;

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

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

  // Define the epilogue
  using Epilogue = typename epilogue::threadblock::DefaultEpilogueTensorOp<
    ThreadblockShape,
    WarpMmaTensorOp,
    1,
    EpilogueOutputOp,
    EpilogueOutputOp::kCount
  >::Epilogue;

  // Define the kernel
  using Kernel = cutlass::conv::kernel::ImplicitGemmConvolutionFusion<
    Mma,
    Epilogue,
    ThreadblockSwizzle,
    conv::Operator::kWgrad
  >;
};

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

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

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