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/*! \file
    \brief Implements several possible threadblock-swizzling functions mapping blockIdx to 
      Convolution problems.
*/

#pragma once

#include "cutlass/cutlass.h"
#include "cutlass/layout/matrix.h"
#include "cutlass/platform/platform.h"
#include "cutlass/gemm/gemm.h"
#include "cutlass/gemm/threadblock/threadblock_swizzle.h"
#include "cutlass/conv/convolution.h"
#include "cutlass/conv/conv2d_problem_size.h"

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

namespace cutlass {
namespace conv {
namespace threadblock {

/////////////////////////////////////////////////////////////////////////////////////////////////
CUTLASS_HOST_DEVICE
static int get_strided_dgrad_tile_m(
  cutlass::conv::Conv2dProblemSize const &problem_size,
  int tile_size_m) {

  // CTAs in M dimension per starting filter position
  int tile_m_per_filter = strided_dgrad_tile_m_per_filter(problem_size, tile_size_m);

  // Inflate number of CTAs in M dimension to cover every strating filter position even those that
  // may fall out of valid MMA (Dy * w) but are needed to apply epilogue (beta * Dx_source) 
  // and point-wise fusion
  int tile_m = tile_m_per_filter * int(problem_size.stride().product());

  // There is a possible performance optimization here that leads up to 2x speeds than the current 
  // CUTLASS strided dgrad performance for stride > filter, i.e., stride={2x2} and filter={1x1})
  //
  // * Optimization * 
  // Only launch CTAs in M dimenstion which contribute to a row in Dx output
  // 
  // 
  // * Constraints *
  // (A) stride <= filter, for example, stride={2x2} and filter={3x3}: 
  //       - (A.1): There are no constraints for this case and the optimization does 
  //                affect this case functionality or performance. 
  // (B) stride > filter, for example, stride={2x2} and filter={1x1}: 
  //       - (B.1): Dx output tensor should be zero initialized
  //       - (B.2): The kernel epilogue cannot apply beta. Thus, beta should be zero 

  return tile_m;
}
/////////////////////////////////////////////////////////////////////////////////////////////////

/////////////////////////////////////////////////////////////////////////////////////////////////
/// Threadblock swizzling function for strided dgrad convolution
struct StridedDgradHorizontalThreadblockSwizzle : 
  public gemm::threadblock::GemmHorizontalThreadblockSwizzle {

  using Base = gemm::threadblock::GemmHorizontalThreadblockSwizzle;

  CUTLASS_HOST_DEVICE
  StridedDgradHorizontalThreadblockSwizzle() { }

  /// Returns the shape of the problem in units of logical tiles
  /// For ImplicitGemmConvolution Conv2d problem size: conv_operator(NPQK, NHWC, KRSC)
  CUTLASS_HOST_DEVICE
  gemm::GemmCoord get_tiled_shape(
    cutlass::conv::Operator conv_operator,
    cutlass::conv::Conv2dProblemSize const &problem_size,
    gemm::GemmCoord tile_size,
    int split_k_slices) const {

    gemm::GemmCoord implicit_gemm_problem_size = 
    cutlass::conv::implicit_gemm_problem_size(conv_operator, problem_size);

    // compute number of tiles in m dimension
    int tile_m = get_strided_dgrad_tile_m(problem_size, tile_size.m());

    // compute number of tiles in n dimenstion 
    int tile_n = (implicit_gemm_problem_size.n() + tile_size.n() - 1) / tile_size.n();

    return gemm::GemmCoord(
      tile_m,
      tile_n,
      split_k_slices);
  }

  /// Returns the shape of the problem in units of logical tiles
  /// For GEMM problem size (MxNxK) (Do not use base class get_tiled_shape())
  private:
    using Base::get_tiled_shape;
};

/////////////////////////////////////////////////////////////////////////////////////////////////
/// Threadblock swizzling function for strided dgrad convolution
template <int N = 1>
struct StridedDgradIdentityThreadblockSwizzle : 
  public gemm::threadblock::GemmIdentityThreadblockSwizzle<N> {

  using Base = gemm::threadblock::GemmIdentityThreadblockSwizzle<N>;

  CUTLASS_HOST_DEVICE
  StridedDgradIdentityThreadblockSwizzle() { }

  /// Returns the shape of the problem in units of logical tiles
  /// For ImplicitGemmConvolution Conv2d problem size: conv_operator(NPQK, NHWC, KRSC)
  CUTLASS_HOST_DEVICE
  gemm::GemmCoord get_tiled_shape(
    cutlass::conv::Operator conv_operator,
    cutlass::conv::Conv2dProblemSize const &problem_size,
    gemm::GemmCoord tile_size,
    int split_k_slices) const {

    gemm::GemmCoord implicit_gemm_problem_size = 
    cutlass::conv::implicit_gemm_problem_size(conv_operator, problem_size);

    // compute number of tiles in m dimension
    int tile_m = get_strided_dgrad_tile_m(problem_size, tile_size.m());

    // compute number of tiles in n dimenstion 
    int tile_n = (implicit_gemm_problem_size.n() + tile_size.n() - 1) / tile_size.n();

    return gemm::GemmCoord(
      tile_m,
      tile_n,
      split_k_slices);
  }

  /// Returns the shape of the problem in units of logical tiles
  /// For GEMM problem size (MxNxK) (Do not use base class get_tiled_shape())
  private:
    using Base::get_tiled_shape;
};

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


} // namespace threadblock
} // namespace gemm
} // namespace cutlass
