tools/util/include/cutlass/util/reference/host/gemm_complex.h

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#pragma once

#include "cutlass/coord.h"
#include "cutlass/complex.h"
#include "cutlass/numeric_types.h"
#include "cutlass/functional.h"
#include "cutlass/numeric_conversion.h"

#include "cutlass/matrix_traits.h"
#include "cutlass/tensor_view.h"
#include "cutlass/gemm/gemm.h"

namespace cutlass {
namespace reference {
namespace host {


template <
  typename ElementA,
  typename LayoutA,
  typename ElementB,
  typename LayoutB,
  typename ElementC,
  typename LayoutC,
  typename ScalarType,
  typename ComputeType,
  typename ConvertOp = NumericConverter<ElementC, ScalarType>,
  typename InnerProductOp = multiply_add<ComputeType>
>
void GemmComplex(
  gemm::GemmCoord problem_size,
  ScalarType alpha,
  TensorRef<ElementA, LayoutA> tensor_a,
  ComplexTransform transform_a,
  TensorRef<ElementB, LayoutB> tensor_b,
  ComplexTransform transform_b,
  ScalarType beta,
  TensorRef<ElementC, LayoutC> tensor_c,
  ComputeType initial_accum) {

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

  // Note: batch is ignored.
  int const M = problem_size.m();
  int const N = problem_size.n();
  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;

  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 = tensor_a.at(MatrixCoord(row, k_block));
              ElementB b = tensor_b.at(MatrixCoord(k_block, col));

              ComputeType a_ik = ComputeType(a);
              ComputeType b_kj = ComputeType(b);

              if (transform_a == ComplexTransform::kConjugate) {
                a_ik = conj(a_ik);
              }

              if (transform_b == ComplexTransform::kConjugate) {
                b_kj = conj(b_kj);
              }

              accum[i][j] = inner_product_op(a_ik, b_kj,  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_c.at(coord) = convert_op(
              alpha * ScalarType(accum[i][j]) + 
              beta * ScalarType(tensor_c.at(coord)));
          }
        }
      }
    }
  }
}


template <
  typename ElementA,
  typename LayoutA,
  typename ElementB,
  typename LayoutB,
  typename ElementC,
  typename LayoutC,
  typename ScalarType
>
void GemmComplex(
  gemm::GemmCoord problem_size,
  ScalarType alpha,
  TensorRef<ElementA, LayoutA> tensor_a,
  ComplexTransform transform_a,
  TensorRef<ElementB, LayoutB> tensor_b,
  ComplexTransform transform_b,
  ScalarType beta,
  TensorRef<ElementC, LayoutC> tensor_c) {

  GemmComplex(problem_size, alpha, tensor_a, transform_a, tensor_b, transform_b, beta, tensor_c, ScalarType(0));
}


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