PALBinaryOpCommon.h

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/*
 * Copyright (c) 2024 Samsung Electronics Co., Ltd. All Rights Reserved
 * Copyright 2017 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 ONERT_MICRO_EXECUTE_PAL_BINARYOP_COMMON_H
#define ONERT_MICRO_EXECUTE_PAL_BINARYOP_COMMON_H
 
#include "OMStatus.h"
#include "core/OMRuntimeShape.h"
#include "PALUtils.h"
#include "ProcessBroadcastShapes.h"
#include <cmath>
 
namespace onert_micro
{
namespace execute
{
namespace pal
{
 
template <typename T, std::enable_if_t<std::is_floating_point<T>::value, bool> = true>
struct FloorDivFn
{
  T operator()(T lhs, T rhs)
  {
    return std::floor(static_cast<double>(lhs) / static_cast<double>(rhs));
  }
};
template <typename T, std::enable_if_t<std::is_floating_point<T>::value, bool> = true>
struct FloorModFn
{
  T operator()(T lhs, T rhs)
  {
    T trunc_mod = std::fmod(lhs, rhs);
    return (trunc_mod != 0) && ((rhs < 0) != (trunc_mod < 0)) ? (trunc_mod + rhs) : trunc_mod;
  }
};
template <typename T> struct MaximumFn
{
  T operator()(T lhs, T rhs) { return std::max(lhs, rhs); }
};
template <typename T> struct MinimumFn
{
  T operator()(T lhs, T rhs) { return std::min(lhs, rhs); }
};
 
// TODO: check if there real activation value
template <typename T, typename Fn>
inline OMStatus BinaryOp(const int flat_size, const T *input1_data, const T *input2_data,
                         T *output_data)
{
  Fn func;
  for (int i = 0; i < flat_size; ++i)
  {
    output_data[i] = func(input1_data[i], input2_data[i]);
  }
  return Ok;
}
 
template <typename T, typename Fn>
inline OMStatus
BroadcastBinaryOp4DSlow(const core::OMRuntimeShape &input1_shape, const float *input1_data,
                        const core::OMRuntimeShape &input2_shape, const float *input2_data,
                        const core::OMRuntimeShape &output_shape, float *output_data)
{
  NdArrayDesc<4> desc1;
  NdArrayDesc<4> desc2;
  NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, &desc2);
 
  const core::OMRuntimeShape extended_output_shape =
    core::OMRuntimeShape::extendedShape(4, output_shape);
 
  // In Tensorflow, the dimensions are canonically named (batch_number, row,
  // col, channel), with extents (batches, height, width, depth), with the
  // trailing dimension changing most rapidly (channels has the smallest stride,
  // typically 1 element).
  //
  // In generated C code, we store arrays with the dimensions reversed. The
  // first dimension has smallest stride.
  //
  // We name our variables by their Tensorflow convention, but generate C code
  // nesting loops such that the innermost loop has the smallest stride for the
  // best cache behavior.
 
  Fn func;
  for (int b = 0; b < extended_output_shape.dims(0); ++b)
  {
    for (int y = 0; y < extended_output_shape.dims(1); ++y)
    {
      for (int x = 0; x < extended_output_shape.dims(2); ++x)
      {
        for (int c = 0; c < extended_output_shape.dims(3); ++c)
        {
          const int output_data_offset =
            ((b * extended_output_shape.dims(1) + y) * extended_output_shape.dims(2) + x) *
              extended_output_shape.dims(3) +
            c;
 
          output_data[output_data_offset] = func(input1_data[subscriptToIndex(desc1, b, y, x, c)],
                                                 input2_data[subscriptToIndex(desc2, b, y, x, c)]);
        }
      }
    }
  }
  return Ok;
}
 
} // namespace pal
} // namespace execute
} // namespace onert_micro
 
#endif // ONERT_MICRO_EXECUTE_PAL_BINARYOP_COMMON_H