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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_fprop_activation_tile_access_iterator_analytic.h"
#include "cutlass/conv/threadblock/conv2d_fprop_filter_tile_access_iterator_analytic.h"
#include "cutlass/conv/threadblock/conv2d_fprop_activation_tile_access_iterator_optimized.h"
#include "cutlass/conv/threadblock/conv2d_fprop_filter_tile_access_iterator_optimized.h"

#include "cutlass/transform/threadblock/predicated_vector_access_iterator.h"
#include "cutlass/transform/threadblock/vector_iterator.h"
#include "cutlass/transform/warp/vector_fragment_iterator.h"

#include "cutlass/gemm/warp/mma_tensor_op_fragment_iterator.h"

#include "kernel/default_b2b_conv2d_fprop.h"
#include "kernel/b2b_implicit_gemm_convolution.h"
#include "threadblock/b2b_implicit_gemm_pipelined_smem_accumulator.h"

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

namespace cutlass {
namespace conv {
namespace kernel {

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

/// Defines a kernel for Conv2dFprop specialization for Analytic IteratorAlgorithm
/// and 2 stage pipeline.
/// Accumulator will be staged in shared memory.
template <
  typename ElementA,
  typename LayoutA,
  typename ElementB,
  typename LayoutB,
  typename ElementC,
  typename LayoutC,
  typename ElementAccumulator,
  typename ArchTag,
  typename ThreadblockShape0,
  typename ThreadblockShape1,
  typename WarpShape0,
  typename WarpShape1,
  typename InstructionShape,
  typename EpilogueOutputOp0,
  typename EpilogueOutputOp1,
  typename ThreadblockSwizzle,
  typename MathOperatorTag
>
struct DefaultB2bConv2dFprop <
  ElementA,
  LayoutA,
  ElementB,
  LayoutB,
  ElementC,
  LayoutC,
  ElementAccumulator,
  arch::OpClassTensorOp,
  ArchTag,
  ThreadblockShape0,
  ThreadblockShape1,
  WarpShape0,
  WarpShape1,
  InstructionShape,
  EpilogueOutputOp0,
  EpilogueOutputOp1,
  ThreadblockSwizzle,
  2,
  MathOperatorTag,
  IteratorAlgorithm::kAnalytic,
  true
> {

  // Define the core components from GEMM
  using MmaCore0 = typename cutlass::gemm::threadblock::DefaultMmaCore<
      ThreadblockShape0, WarpShape0, InstructionShape, ElementA, layout::RowMajor,
      ElementB, layout::ColumnMajor, ElementAccumulator, layout::RowMajor, arch::OpClassTensorOp,
      2, MathOperatorTag>;
  using MmaCore1 = typename cutlass::gemm::threadblock::DefaultMmaCore<
      ThreadblockShape1, WarpShape1, InstructionShape, ElementA, layout::RowMajor,
      ElementB, layout::ColumnMajor, ElementAccumulator, layout::RowMajor, arch::OpClassTensorOp,
      2, MathOperatorTag>;

  // Define iterators over tiles from the A operand
  using ThreadMapA0 = typename MmaCore0::IteratorThreadMapA;
  using IteratorA0 =
    cutlass::conv::threadblock::TileIterator<
      cutlass::conv::threadblock::Conv2dFpropActivationTileAccessIteratorAnalytic<
        cutlass::MatrixShape<ThreadblockShape0::kM, ThreadblockShape0::kK>,
        ElementA, LayoutA,
        ThreadMapA0
      >
    >;

  using SmemIteratorA0 = typename MmaCore0::SmemIteratorA;

  // Define iterators over tiles from the B operand
  using ThreadMapB0 = typename MmaCore0::IteratorThreadMapB;
  using IteratorB0 =
    cutlass::conv::threadblock::TileIterator<
      cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorAnalytic<
        cutlass::MatrixShape<ThreadblockShape0::kK, ThreadblockShape0::kN>,
        ElementB, LayoutB,
        ThreadMapB0
      >
    >;
  
  using SmemIteratorB0 = typename MmaCore0::SmemIteratorB;

  /// Define iterators over tiles from scale/bias vectors
  using ElementScaleBias = typename EpilogueOutputOp0::ElementCompute;
  using LayoutScaleBias = layout::RowMajor; //vector layout doesn't really matter
  static int const kElementsPerAccess = 2;
  using IteratorAccumulatorScaleBias =
    cutlass::transform::threadblock::VectorIterator<
      cutlass::transform::threadblock::PredicatedVectorAccessIterator<
          cutlass::MatrixShape<ThreadblockShape0::kM, ThreadblockShape0::kN>, 
          cutlass::MatrixShape<WarpShape0::kM, WarpShape0::kN>, 
          ElementScaleBias, LayoutScaleBias, kElementsPerAccess>
    >;

  // Define iterators over tiles from the B operand
  using ThreadMapB1 = typename MmaCore1::IteratorThreadMapB;
  using IteratorB1 =
    cutlass::conv::threadblock::TileIterator<
      cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorAnalytic<
        cutlass::MatrixShape<ThreadblockShape1::kK, ThreadblockShape1::kN>,
        ElementB, LayoutB,
        ThreadMapB1
      >
    >;
  
  using SmemIteratorB1 = typename MmaCore1::SmemIteratorB;

  // Warp-level GEMM components
  using WarpMmaTensorOp0 = typename MmaCore0::MmaTensorOp;
  using WarpMmaTensorOp1 = typename MmaCore1::MmaTensorOp;
  using MmaPolicy0 = typename MmaCore0::MmaPolicy;
  using MmaPolicy1 = typename MmaCore1::MmaPolicy;

  // Use fragment iterator for the accumulator
  using SmemAccumulatorLayout = cutlass::layout::RowMajor;
  using FragmentIteratorAccumulator = cutlass::epilogue::warp::FragmentIteratorTensorOp<
          WarpShape0, InstructionShape,
          ElementAccumulator,
          typename WarpMmaTensorOp0::Policy::Operator::FragmentC,
          SmemAccumulatorLayout
        >;

  // Store Accumulator tiles to Shared Memory
  using SmemIteratorD0 = 
      cutlass::epilogue::warp::TileIteratorTensorOp<
          WarpShape0,
          InstructionShape,
          ElementC,
          SmemAccumulatorLayout
        >;

  static int const kThreadCount = 32;
  // load warp tile from Shared Memory accumulator
  using WarpIteratorA1 = cutlass::gemm::warp::MmaTensorOpMultiplicandTileIterator<
    MatrixShape<WarpShape1::kM, InstructionShape::kK>, cutlass::gemm::Operand::kA, 
    ElementA, SmemAccumulatorLayout,
    MatrixShape<InstructionShape::kM, InstructionShape::kK>,
    WarpMmaTensorOp1::Policy::OpDelta::kRow, kThreadCount>;
 
  // Define the Mma
  using B2bMma = threadblock::B2bImplicitGemmPipelinedSmemAccumulator<
    ThreadblockShape0,
    IteratorA0,
    SmemIteratorA0,
    IteratorB0,
    SmemIteratorB0,
    IteratorAccumulatorScaleBias,
    FragmentIteratorAccumulator,
    SmemIteratorD0,
    ThreadblockShape1,
    WarpIteratorA1,
    IteratorB1,
    SmemIteratorB1,
    ElementC,
    LayoutC,
    EpilogueOutputOp0,
    MmaPolicy0,
    MmaPolicy1
  >;

  // Define the epilogue
  using Epilogue = typename detail::DefaultConvEpilogue<
    ArchTag,
    ThreadblockShape1,
    WarpMmaTensorOp1,
    1,
    EpilogueOutputOp1
  >::Epilogue;

  // Define the kernel
  using Kernel = cutlass::conv::kernel::B2bImplicitGemmConvolution<
    B2bMma,
    Epilogue,
    ThreadblockSwizzle,
    conv::Operator::kFprop
  >;
};

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

/// Defines a kernel for Conv2dFprop specialization for Analytic IteratorAlgorithm and 2 stage 
/// pipeline with interleaved layout.
/// Accumulator will be staged in shared memory.
template <
  typename ElementA,
  typename ElementB,
  typename ElementC,
  typename LayoutC,
  typename ElementAccumulator,
  typename ArchTag,
  typename ThreadblockShape0,
  typename ThreadblockShape1,
  typename WarpShape0,
  typename WarpShape1,
  typename InstructionShape,
  typename EpilogueOutputOp0,
  typename EpilogueOutputOp1,
  typename ThreadblockSwizzle,
  typename MathOperatorTag,
  int InterleavedK
>
struct DefaultB2bConv2dFprop <
  ElementA,
  layout::TensorNCxHWx<InterleavedK>,
  ElementB,
  layout::TensorCxRSKx<InterleavedK>,
  ElementC,
  LayoutC,
  ElementAccumulator,
  arch::OpClassTensorOp,
  ArchTag,
  ThreadblockShape0,
  ThreadblockShape1,
  WarpShape0,
  WarpShape1,
  InstructionShape,
  EpilogueOutputOp0,
  EpilogueOutputOp1,
  ThreadblockSwizzle,
  2,
  MathOperatorTag,
  IteratorAlgorithm::kAnalytic,
  true
> {

  // Define the core components from GEMM
  using MmaCore0 = typename cutlass::gemm::threadblock::DefaultMmaCore<
      ThreadblockShape0, WarpShape0, InstructionShape, ElementA, layout::ColumnMajorInterleaved<InterleavedK>,
      ElementB, layout::RowMajorInterleaved<InterleavedK>, 
      ElementAccumulator, LayoutC, arch::OpClassTensorOp,
      2, MathOperatorTag, true>;
  using MmaCore1 = typename cutlass::gemm::threadblock::DefaultMmaCore<
      ThreadblockShape1, WarpShape1, InstructionShape, ElementA, layout::ColumnMajorInterleaved<InterleavedK>,
      ElementB, layout::RowMajorInterleaved<InterleavedK>, 
      ElementAccumulator, LayoutC, arch::OpClassTensorOp,
      2, MathOperatorTag, true>;

  // Define iterators over tiles from the A operand
  // Note GEMM shared memory threadmap is used here because conv global memory
  // layout needs to be mapped to fprop which is similar to the crosswise
  // layout which is used by the interleaved GEMM shared memory threadmap.
  // The Interleaved GEMM global memory layout is similar to the congruous
  // layout.
  using ThreadMapA0 = typename MmaCore0::SmemThreadMapA;
  using IteratorA0 =
    cutlass::conv::threadblock::TileIterator<
      cutlass::conv::threadblock::Conv2dFpropActivationTileAccessIteratorAnalytic<
        cutlass::MatrixShape<ThreadblockShape0::kM, ThreadblockShape0::kK>,
        ElementA, layout::TensorNCxHWx<InterleavedK>,
        ThreadMapA0
      >
    >;

  using SmemIteratorA0 = typename MmaCore0::SmemIteratorA;

  // Define iterators over tiles from the B operand
  // Note GEMM shared memory threadmap is used here because conv global memory
  // layout needs to be mapped to fprop which is similar to the crosswise
  // layout which is used by the interleaved GEMM shared memory threadmap.
  // The Interleaved GEMM global memory layout is similar to the congruous
  // layout.
  using ThreadMapB0 = typename MmaCore0::SmemThreadMapB;
  using IteratorB0 =
    cutlass::conv::threadblock::TileIterator<
      cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorAnalytic<
        cutlass::MatrixShape<ThreadblockShape0::kK, ThreadblockShape0::kN>,
        ElementB, layout::TensorCxRSKx<InterleavedK>,
        ThreadMapB0
      >
    >;
  
  using SmemIteratorB0 = typename MmaCore0::SmemIteratorB;

  /// Define iterators over tiles from scale/bias vectors
  using ElementScaleBias = typename EpilogueOutputOp0::ElementCompute;
  using LayoutScaleBias = layout::RowMajor; //vector layout doesn't really matter
  static int const kElementsPerAccess = 4; //For interleaved layout
  using IteratorAccumulatorScaleBias =
    cutlass::transform::threadblock::VectorIterator<
      cutlass::transform::threadblock::PredicatedVectorAccessIterator<
          cutlass::MatrixShape<ThreadblockShape0::kM, ThreadblockShape0::kN>, 
          cutlass::MatrixShape<WarpShape0::kM, WarpShape0::kN>, 
          ElementScaleBias, LayoutScaleBias, kElementsPerAccess>
    >;

 // Define iterators over tiles from the B operand
  using ThreadMapB1 = typename MmaCore1::SmemThreadMapB;
  using IteratorB1 =
    cutlass::conv::threadblock::TileIterator<
      cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorAnalytic<
        cutlass::MatrixShape<ThreadblockShape1::kK, ThreadblockShape1::kN>,
        ElementB, layout::TensorCxRSKx<InterleavedK>,
        ThreadMapB1
      >
    >;
  
  using SmemIteratorB1 = typename MmaCore1::SmemIteratorB;

  // Warp-level GEMM components
  using WarpMmaTensorOp0 = typename MmaCore0::MmaTensorOp;
  using WarpMmaTensorOp1 = typename MmaCore1::MmaTensorOp;
  using MmaPolicy0 = typename MmaCore0::MmaPolicy;
  using MmaPolicy1 = typename MmaCore1::MmaPolicy;

  // Use fragment iterator for the accumulator
  using SmemAccumulatorLayout = cutlass::layout::ColumnMajorInterleaved<16>;
  using FragmentIteratorAccumulator = cutlass::epilogue::warp::FragmentIteratorTensorOp<
          WarpShape0, InstructionShape,
          ElementAccumulator,
          typename WarpMmaTensorOp0::Policy::Operator::FragmentC,
          SmemAccumulatorLayout
        >;


  // Store Accumulator tiles to Shared Memory
  using SmemIteratorD0 = 
      cutlass::epilogue::warp::TileIteratorTensorOp<
          WarpShape0,
          InstructionShape,
          ElementC,
          SmemAccumulatorLayout
        >;

  static int const kThreadCount = 32;
  // load warp tile from Shared Memory accumulator
  using WarpIteratorA1 = cutlass::gemm::warp::MmaTensorOpMultiplicandTileIteratorCanonical<
    MatrixShape<WarpShape1::kM, InstructionShape::kK>, cutlass::gemm::Operand::kA, 
    ElementA, SmemAccumulatorLayout,
    MatrixShape<InstructionShape::kM, InstructionShape::kK>,
    WarpMmaTensorOp1::Policy::OpDelta::kRow, kThreadCount>;
 
  // Define the Mma
  using B2bMma = threadblock::B2bImplicitGemmPipelinedSmemAccumulator<
    ThreadblockShape0,
    IteratorA0,
    SmemIteratorA0,
    IteratorB0,
    SmemIteratorB0,
    IteratorAccumulatorScaleBias,
    FragmentIteratorAccumulator,
    SmemIteratorD0,
    ThreadblockShape1,
    WarpIteratorA1,
    IteratorB1,
    SmemIteratorB1,
    ElementC,
    LayoutC,
    EpilogueOutputOp0,
    MmaPolicy0,
    MmaPolicy1
  >;

  // Define the epilogue
  using Epilogue = typename epilogue::threadblock::DefaultInterleavedConvEpilogue<
    ThreadblockShape1,
    WarpMmaTensorOp1,
    1,
    EpilogueOutputOp1,
    EpilogueOutputOp1::kCount,
    InterleavedK
  >::Epilogue;

  // Define the kernel
  using Kernel = cutlass::conv::kernel::B2bImplicitGemmConvolution<
    B2bMma,
    Epilogue,
    ThreadblockSwizzle,
    conv::Operator::kFprop
  >;
};

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

/// Defines a kernel for Conv2dFprop specialization for Optimized IteratorAlgorithm
/// and 2 stage pipeline.
/// Accumulator will be staged in shared memory.
template <
  typename ElementA,
  typename LayoutA,
  typename ElementB,
  typename LayoutB,
  typename ElementC,
  typename LayoutC,
  typename ElementAccumulator,
  typename ArchTag,
  typename ThreadblockShape0,
  typename ThreadblockShape1,
  typename WarpShape0,
  typename WarpShape1,
  typename InstructionShape,
  typename EpilogueOutputOp0,
  typename EpilogueOutputOp1,
  typename ThreadblockSwizzle,
  typename MathOperatorTag
>
struct DefaultB2bConv2dFprop <
  ElementA,
  LayoutA,
  ElementB,
  LayoutB,
  ElementC,
  LayoutC,
  ElementAccumulator,
  arch::OpClassTensorOp,
  ArchTag,
  ThreadblockShape0,
  ThreadblockShape1,
  WarpShape0,
  WarpShape1,
  InstructionShape,
  EpilogueOutputOp0,
  EpilogueOutputOp1,
  ThreadblockSwizzle,
  2,
  MathOperatorTag,
  IteratorAlgorithm::kOptimized,
  true
> {

  // Define the core components from GEMM
  using MmaCore0 = typename cutlass::gemm::threadblock::DefaultMmaCore<
      ThreadblockShape0, WarpShape0, InstructionShape, ElementA, layout::RowMajor,
      ElementB, layout::ColumnMajor, ElementAccumulator, layout::RowMajor, arch::OpClassTensorOp,
      2, MathOperatorTag>;
  using MmaCore1 = typename cutlass::gemm::threadblock::DefaultMmaCore<
      ThreadblockShape1, WarpShape1, InstructionShape, ElementA, layout::RowMajor,
      ElementB, layout::ColumnMajor, ElementAccumulator, layout::RowMajor, arch::OpClassTensorOp,
      2, MathOperatorTag>;

  // Define iterators over tiles from the A operand
  using ThreadMapA0 = typename MmaCore0::IteratorThreadMapA;
  using IteratorA0 =
    cutlass::conv::threadblock::TileIterator<
      cutlass::conv::threadblock::Conv2dFpropActivationTileAccessIteratorOptimized<
        cutlass::MatrixShape<ThreadblockShape0::kM, ThreadblockShape0::kK>,
        ElementA, LayoutA,
        ThreadMapA0
      >
    >;

  using SmemIteratorA0 = typename MmaCore0::SmemIteratorA;

  // Define iterators over tiles from the B operand
  using ThreadMapB0 = typename MmaCore0::IteratorThreadMapB;
  using IteratorB0 =
    cutlass::conv::threadblock::TileIterator<
      cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorOptimized<
        cutlass::MatrixShape<ThreadblockShape0::kK, ThreadblockShape0::kN>,
        ElementB, LayoutB,
        ThreadMapB0
      >
    >;
  
  using SmemIteratorB0 = typename MmaCore0::SmemIteratorB;

  /// Define iterators over tiles from scale/bias vectors
  using ElementScaleBias = typename EpilogueOutputOp0::ElementCompute;
  using LayoutScaleBias = layout::RowMajor; //vector layout doesn't really matter
  static int const kElementsPerAccess = 2;
  using IteratorAccumulatorScaleBias =
    cutlass::transform::threadblock::VectorIterator<
      cutlass::transform::threadblock::PredicatedVectorAccessIterator<
          cutlass::MatrixShape<ThreadblockShape0::kM, ThreadblockShape0::kN>, 
          cutlass::MatrixShape<WarpShape0::kM, WarpShape0::kN>, 
          ElementScaleBias, LayoutScaleBias, kElementsPerAccess>
    >;

  // Define iterators over tiles from the B operand
  using ThreadMapB1 = typename MmaCore1::IteratorThreadMapB;
  using IteratorB1 =
    cutlass::conv::threadblock::TileIterator<
      cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorOptimized<
        cutlass::MatrixShape<ThreadblockShape1::kK, ThreadblockShape1::kN>,
        ElementB, LayoutB,
        ThreadMapB1
      >
    >;
  
  using SmemIteratorB1 = typename MmaCore1::SmemIteratorB;

  // Warp-level GEMM components
  using WarpMmaTensorOp0 = typename MmaCore0::MmaTensorOp;
  using WarpMmaTensorOp1 = typename MmaCore1::MmaTensorOp;
  using MmaPolicy0 = typename MmaCore0::MmaPolicy;
  using MmaPolicy1 = typename MmaCore1::MmaPolicy;

  // Use fragment iterator for the accumulator
  using SmemAccumulatorLayout = cutlass::layout::RowMajor;
  using FragmentIteratorAccumulator = cutlass::epilogue::warp::FragmentIteratorTensorOp<
          WarpShape0, InstructionShape,
          ElementAccumulator,
          typename WarpMmaTensorOp0::Policy::Operator::FragmentC,
          SmemAccumulatorLayout
        >;

  // Store Accumulator tiles to Shared Memory
  using SmemIteratorD0 = 
      cutlass::epilogue::warp::TileIteratorTensorOp<
          WarpShape0,
          InstructionShape,
          ElementC,
          SmemAccumulatorLayout
        >;

  static int const kThreadCount = 32;
  // load warp tile from Shared Memory accumulator
  using WarpIteratorA1 = cutlass::gemm::warp::MmaTensorOpMultiplicandTileIterator<
    MatrixShape<WarpShape1::kM, InstructionShape::kK>, cutlass::gemm::Operand::kA, 
    ElementA, SmemAccumulatorLayout,
    MatrixShape<InstructionShape::kM, InstructionShape::kK>,
    WarpMmaTensorOp1::Policy::OpDelta::kRow, kThreadCount>;
 
  // Define the Mma
  using B2bMma = threadblock::B2bImplicitGemmPipelinedSmemAccumulator<
    ThreadblockShape0,
    IteratorA0,
    SmemIteratorA0,
    IteratorB0,
    SmemIteratorB0,
    IteratorAccumulatorScaleBias,
    FragmentIteratorAccumulator,
    SmemIteratorD0,
    ThreadblockShape1,
    WarpIteratorA1,
    IteratorB1,
    SmemIteratorB1,
    ElementC,
    LayoutC,
    EpilogueOutputOp0,
    MmaPolicy0,
    MmaPolicy1
  >;

  // Define the epilogue
  using Epilogue = typename detail::DefaultConvEpilogue<
    ArchTag,
    ThreadblockShape1,
    WarpMmaTensorOp1,
    1,
    EpilogueOutputOp1
  >::Epilogue;

  // Define the kernel
  using Kernel = cutlass::conv::kernel::B2bImplicitGemmConvolution<
    B2bMma,
    Epilogue,
    ThreadblockSwizzle,
    conv::Operator::kFprop
  >;
};

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

/// Defines a kernel for Conv2dFprop specialization for Optimized IteratorAlgorithm and 2 stage 
/// pipeline with interleaved layout.
/// Accumulator will be staged in shared memory.
template <
  typename ElementA,
  typename ElementB,
  typename ElementC,
  typename LayoutC,
  typename ElementAccumulator,
  typename ArchTag,
  typename ThreadblockShape0,
  typename ThreadblockShape1,
  typename WarpShape0,
  typename WarpShape1,
  typename InstructionShape,
  typename EpilogueOutputOp0,
  typename EpilogueOutputOp1,
  typename ThreadblockSwizzle,
  typename MathOperatorTag,
  int InterleavedK
>
struct DefaultB2bConv2dFprop <
  ElementA,
  layout::TensorNCxHWx<InterleavedK>,
  ElementB,
  layout::TensorCxRSKx<InterleavedK>,
  ElementC,
  LayoutC,
  ElementAccumulator,
  arch::OpClassTensorOp,
  ArchTag,
  ThreadblockShape0,
  ThreadblockShape1,
  WarpShape0,
  WarpShape1,
  InstructionShape,
  EpilogueOutputOp0,
  EpilogueOutputOp1,
  ThreadblockSwizzle,
  2,
  MathOperatorTag,
  IteratorAlgorithm::kOptimized,
  true
> {

  // Define the core components from GEMM
  using MmaCore0 = typename cutlass::gemm::threadblock::DefaultMmaCore<
      ThreadblockShape0, WarpShape0, InstructionShape, ElementA, layout::ColumnMajorInterleaved<InterleavedK>,
      ElementB, layout::RowMajorInterleaved<InterleavedK>, 
      ElementAccumulator, LayoutC, arch::OpClassTensorOp,
      2, MathOperatorTag, true>;
  using MmaCore1 = typename cutlass::gemm::threadblock::DefaultMmaCore<
      ThreadblockShape1, WarpShape1, InstructionShape, ElementA, layout::ColumnMajorInterleaved<InterleavedK>,
      ElementB, layout::RowMajorInterleaved<InterleavedK>, 
      ElementAccumulator, LayoutC, arch::OpClassTensorOp,
      2, MathOperatorTag, true>;

  // Define iterators over tiles from the A operand
  // Note GEMM shared memory threadmap is used here because conv global memory
  // layout needs to be mapped to fprop which is similar to the crosswise
  // layout which is used by the interleaved GEMM shared memory threadmap.
  // The Interleaved GEMM global memory layout is similar to the congruous
  // layout.

  // Define iterators over tiles from the A operand
  using ThreadMapA0 = typename MmaCore0::SmemThreadMapA;
  using IteratorA0 =
    cutlass::conv::threadblock::TileIterator<
      cutlass::conv::threadblock::Conv2dFpropActivationTileAccessIteratorOptimized<
        cutlass::MatrixShape<ThreadblockShape0::kM, ThreadblockShape0::kK>,
        ElementA, layout::TensorNCxHWx<InterleavedK>,
        ThreadMapA0
      >
    >;

  using SmemIteratorA0 = typename MmaCore0::SmemIteratorA;

  // Define iterators over tiles from the B operand
  // Note GEMM shared memory threadmap is used here because conv global memory
  // layout needs to be mapped to fprop which is similar to the crosswise
  // layout which is used by the interleaved GEMM shared memory threadmap.
  // The Interleaved GEMM global memory layout is similar to the congruous
  // layout.
  using ThreadMapB0 = typename MmaCore0::SmemThreadMapB;
  using IteratorB0 =
    cutlass::conv::threadblock::TileIterator<
      cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorOptimized<
        cutlass::MatrixShape<ThreadblockShape0::kK, ThreadblockShape0::kN>,
        ElementB, layout::TensorCxRSKx<InterleavedK>,
        ThreadMapB0
      >
    >;
  
  using SmemIteratorB0 = typename MmaCore0::SmemIteratorB;

  /// Define iterators over tiles from scale/bias vectors
  using ElementScaleBias = typename EpilogueOutputOp0::ElementCompute;
  using LayoutScaleBias = layout::RowMajor; //vector layout doesn't really matter
  static int const kElementsPerAccess = 4; //For interleaved layout
  using IteratorAccumulatorScaleBias =
    cutlass::transform::threadblock::VectorIterator<
      cutlass::transform::threadblock::PredicatedVectorAccessIterator<
          cutlass::MatrixShape<ThreadblockShape0::kM, ThreadblockShape0::kN>, 
          cutlass::MatrixShape<WarpShape0::kM, WarpShape0::kN>, 
          ElementScaleBias, LayoutScaleBias, kElementsPerAccess>
    >;

  using ThreadMapB1 = typename MmaCore1::SmemThreadMapB;
  using IteratorB1 =
    cutlass::conv::threadblock::TileIterator<
      cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorOptimized<
        cutlass::MatrixShape<ThreadblockShape1::kK, ThreadblockShape1::kN>,
        ElementB, layout::TensorCxRSKx<InterleavedK>,
        ThreadMapB1
      >
    >;
  
  using SmemIteratorB1 = typename MmaCore1::SmemIteratorB;

  // Warp-level GEMM components
  using WarpMmaTensorOp0 = typename MmaCore0::MmaTensorOp;
  using WarpMmaTensorOp1 = typename MmaCore1::MmaTensorOp;
  using MmaPolicy0 = typename MmaCore0::MmaPolicy;
  using MmaPolicy1 = typename MmaCore1::MmaPolicy;

  // Use fragment iterator for the accumulator
  using SmemAccumulatorLayout = cutlass::layout::ColumnMajorInterleaved<16>;
  using FragmentIteratorAccumulator = cutlass::epilogue::warp::FragmentIteratorTensorOp<
          WarpShape0, InstructionShape,
          ElementAccumulator,
          typename WarpMmaTensorOp0::Policy::Operator::FragmentC,
          SmemAccumulatorLayout
        >;


  // Store Accumulator tiles to Shared Memory
  using SmemIteratorD0 = 
      cutlass::epilogue::warp::TileIteratorTensorOp<
          WarpShape0,
          InstructionShape,
          ElementC,
          SmemAccumulatorLayout
        >;

  static int const kThreadCount = 32;
  // load warp tile from Shared Memory accumulator
  using WarpIteratorA1 = cutlass::gemm::warp::MmaTensorOpMultiplicandTileIteratorCanonical<
    MatrixShape<WarpShape1::kM, InstructionShape::kK>, cutlass::gemm::Operand::kA, 
    ElementA, SmemAccumulatorLayout,
    MatrixShape<InstructionShape::kM, InstructionShape::kK>,
    WarpMmaTensorOp1::Policy::OpDelta::kRow, kThreadCount>;
 
  // Define the Mma
  using B2bMma = threadblock::B2bImplicitGemmPipelinedSmemAccumulator<
    ThreadblockShape0,
    IteratorA0,
    SmemIteratorA0,
    IteratorB0,
    SmemIteratorB0,
    IteratorAccumulatorScaleBias,
    FragmentIteratorAccumulator,
    SmemIteratorD0,
    ThreadblockShape1,
    WarpIteratorA1,
    IteratorB1,
    SmemIteratorB1,
    ElementC,
    LayoutC,
    EpilogueOutputOp0,
    MmaPolicy0,
    MmaPolicy1
  >;

  // Define the epilogue
  using Epilogue = typename epilogue::threadblock::DefaultInterleavedConvEpilogue<
    ThreadblockShape1,
    WarpMmaTensorOp1,
    1,
    EpilogueOutputOp1,
    EpilogueOutputOp1::kCount,
    InterleavedK
  >::Epilogue;

  // Define the kernel
  using Kernel = cutlass::conv::kernel::B2bImplicitGemmConvolution<
    B2bMma,
    Epilogue,
    ThreadblockSwizzle,
    conv::Operator::kFprop
  >;
};

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

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

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