eigen_gemm_eigen.h

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/*
 * Copyright (c) 2023 Samsung Electronics Co., Ltd. All Rights Reserved
 * Copyright 2019 The TensorFlow Authors. All Rights Reserved.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
 
#ifndef __NNFW_CKER_EGIEN_EIGEN_GEMM_EIGEN_H__
#define __NNFW_CKER_EGIEN_EIGEN_GEMM_EIGEN_H__
 
// See b/131835803: in TFLite code, because eigen_spatial_convolutions.h does
// #define Eigen EigenForTFLite, it is difficult to have any #include of Eigen
// headers in a header file, as that results in name classes (compilation
// errors) depending on the order in which these headers are #included.
// So we have moved the #include of Eigen here, in a .cc file, where we have
// control over the header #include sequence.
// #include "third_party/eigen3/Eigen/Core"
// #include "tensorflow/lite/kernels/cpu_backend_context.h"
// #include "tensorflow/lite/kernels/cpu_backend_gemm_params.h"
// #include "tensorflow/lite/kernels/internal/common.h"
// #include "cker/eigen/eigen_convolution_helpers.h"
#include "cker/operation/Common.h"
#include "cker/Types.h"
 
#include <Eigen/Core>
 
namespace nnfw
{
namespace cker
{
namespace detail
{
 
// tensorflow/tensorflow/lite/kernels/cpu_backend_gemm_eigen.h and cpu_backend_gemm_eigen.cc
struct GemmImplUsingEigen
{
  static void Run(const MatrixParams<float> &lhs_params, const float *lhs_data,
                  const MatrixParams<float> &rhs_params, const float *rhs_data,
                  const MatrixParams<float> &dst_params, float *dst_data,
                  const GemmParams<float, float> &params)
  {
    // This code assumes specific storage orders, encoded in these Eigen types.
    // These assumptions have been checked by TF_LITE_ASSERT's in the public
    // Gemm entry point already, before the implementation gets to this point.
    using EigenMatrixMapRowMajorConst =
      Eigen::Map<const Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>>;
    using EigenMatrixMapColMajorConst =
      Eigen::Map<const Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic, Eigen::ColMajor>>;
    using EigenMatrixMapColMajorMutable =
      Eigen::Map<Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic, Eigen::ColMajor>>;
 
    EigenMatrixMapRowMajorConst eigen_lhs(lhs_data, lhs_params.rows, lhs_params.cols);
    EigenMatrixMapColMajorConst eigen_rhs(rhs_data, rhs_params.rows, rhs_params.cols);
    EigenMatrixMapColMajorMutable eigen_dst(dst_data, dst_params.rows, dst_params.cols);
 
    if (rhs_params.cols == 1)
    {
      eigen_dst.col(0).noalias() = eigen_lhs * eigen_rhs.col(0);
    }
    else if (lhs_params.rows == 1)
    {
      eigen_dst.row(0).noalias() = eigen_lhs.row(0) * eigen_rhs;
    }
    else
    {
      eigen_dst.noalias() = eigen_lhs * eigen_rhs;
    }
 
    if (params.bias)
    {
      BiasAndClamp(params.clamp_min, params.clamp_max, dst_params.rows, params.bias,
                   dst_params.rows * dst_params.cols, dst_data);
    }
    else
    {
      eigen_dst = eigen_dst.cwiseMin(params.clamp_max).cwiseMax(params.clamp_min);
    }
  }
};
 
} // namespace detail
} // namespace cker
} // namespace nnfw
 
#endif // __NNFW_CKER_EGIEN_EIGEN_GEMM_EIGEN_H__