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/*! \file
    \brief Reference implementation for TRMM in host-side code.

  
*/

#pragma once

#include "cutlass/blas3.h"
#include "cutlass/numeric_conversion.h"
#include "cutlass/tensor_view.h"
#include "cutlass/gemm/gemm.h"
#include "cutlass/arch/mma.h"
#include "cutlass/util/host_tensor.h"

#include "cutlass/util/reference/host/gemm.h"

namespace cutlass {
namespace reference {
namespace host {

/// Computes a Triangular Matrix Multiplication (tensors of rank=2) pointed to by TensorRef
/// objects.
template <
  typename ElementA,
  typename LayoutA,
  SideMode SideModeA,
  FillMode FillModeA,
  DiagType DiagTypeA,
  typename ElementB,
  typename LayoutB,
  typename ElementC,
  typename LayoutC,
  typename ScalarType,
  typename ComputeType,
  typename InnerProductOp = multiply_add<ComputeType>,
  typename ConvertOp = NumericConverter<ElementC, ScalarType>
>
void compute_trmm(
  gemm::GemmCoord problem_size,
  ScalarType alpha,
  TensorRef<ElementA, LayoutA> tensor_a,
  TensorRef<ElementB, LayoutB> tensor_b,
  TensorRef<ElementC, LayoutC> tensor_d,
  ComputeType initial_accum) {

  static_assert(
    LayoutA::kRank == 2 &&
    LayoutC::kRank == 2, "Tensors must be of rank 2");

  static_assert(SideModeA != SideMode::kInvalid
                , "Side Mode can either be Left or Right.");

  static_assert(FillModeA == FillMode::kLower || FillModeA == FillMode::kUpper
                , "Fill Mode can either be Lower or Upper.");

  using CompareOp = typename TrMatrixCompareOp<FillModeA, DiagTypeA>::Type;

  // Note: batch is ignored.
  int const M = problem_size.m();
  int const N = problem_size.n();
  // Assuming correct k-dimension value is passed
  int const K = problem_size.k();
 
  // Blocking necessary to speedup reference implementation
  int const Mblock = 16;
  int const Nblock = 16;

  ConvertOp convert_op;
  InnerProductOp inner_product_op;
  CompareOp compare_op;

  for (int row_block = 0; row_block < M; row_block += Mblock) {
    for (int col_block = 0; col_block < N; col_block += Nblock) {

      ComputeType accum[Mblock][Nblock];

      for (int j = 0; j < Nblock; j++) {
        for (int i = 0; i < Mblock; i++) {
          accum[i][j] = initial_accum;
        }
      }

      for (int k_block = 0; k_block < K; ++k_block) {
        for (int j = 0; j < Nblock; j++) {
          for (int i = 0; i < Mblock; i++) {
            int row = row_block + i;
            int col = col_block + j;

            if (row < M && col < N) {
              ElementA a = ElementA();
              ElementB b = ElementB();

              if (SideModeA == SideMode::kLeft) {
                a = (compare_op(row, k_block)) ? 
                            (tensor_a.at(MatrixCoord(row, k_block))) : ElementA(0);
                if (row == k_block && DiagTypeA == DiagType::kUnit) {
                  a = ElementA(1);
                }
                b = tensor_b.at(MatrixCoord(k_block, col));
              } else if (SideModeA == SideMode::kRight) {
                a = tensor_b.at(MatrixCoord(row, k_block));
                b = (compare_op(k_block, col)) ? 
                      tensor_a.at(MatrixCoord(k_block, col)) : ElementA(0);
                if (k_block == col && DiagTypeA == DiagType::kUnit) {
                  b = ElementA(1);
                }
              }
                            
              ComputeType compute_a(cast_if_scalar<ComputeType>(a));
              ComputeType compute_b(cast_if_scalar<ComputeType>(b));

              accum[i][j] = inner_product_op(compute_a, compute_b, accum[i][j]);
            }
          }
        }
      }

      for (int j = 0; j < Nblock; j++) {
        for (int i = 0; i < Mblock; i++) {
          int row = row_block + i;
          int col = col_block + j;

          MatrixCoord coord = MatrixCoord(row, col);

          if (row < M && col < N) {
            tensor_d.at(coord) = convert_op(
              alpha * ScalarType(accum[i][j]));
          }
        }
      }
    }
  }
}

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

template <
  typename ElementA,
  typename LayoutA,
  SideMode SideModeA,
  FillMode FillModeA,
  DiagType DiagTypeA,
  typename ElementB,
  typename LayoutB,
  typename ElementC,
  typename LayoutC,
  typename ScalarType,
  typename ComputeType,
  typename InnerProductOp = cutlass::arch::OpMultiplyAdd
>
struct Trmm;

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

/// Partial specialization for multiply-add
template <typename ElementA, typename LayoutA, SideMode SideModeA,
           FillMode FillModeA, DiagType DiagTypeA, 
           typename ElementB, typename LayoutB,
           typename ElementC, typename LayoutC,
          typename ScalarType, typename ComputeType>
struct Trmm<ElementA, LayoutA, SideModeA, FillModeA, DiagTypeA, ElementB, LayoutB,
            ElementC, LayoutC, ScalarType,
            ComputeType, arch::OpMultiplyAdd> {

  void operator()(gemm::GemmCoord problem_size, ScalarType alpha,
                  TensorRef<ElementA, LayoutA> tensor_a,
                  TensorRef<ElementB, LayoutB> tensor_b,
                  TensorRef<ElementC, LayoutC> tensor_d,
                  ComputeType initial_accum = ComputeType(0)) {
    static_assert(
        LayoutA::kRank == 2 && LayoutC::kRank == 2,
        "Tensors must be of rank 2");

    compute_trmm<ElementA, LayoutA, SideModeA, FillModeA, DiagTypeA, ElementB, LayoutB,
                 ElementC, LayoutC, ScalarType, ComputeType, multiply_add<ComputeType>>(
                 problem_size, alpha, tensor_a, tensor_b, tensor_d, initial_accum);
  }
};

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

} // namespace host
} // namespace reference
} // namespace cutlass
