OMUtils.h

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/*
 * Copyright (c) 2024 Samsung Electronics Co., Ltd. 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_UTILS_H
#define ONERT_MICRO_EXECUTE_UTILS_H
 
#include <cmath>
#include "OMStatus.h"
#include "core/reader/OMCircleReader.h"
#include "core/OMRuntimeShape.h"
#include "core/OMKernelData.h"
 
#include "execute/OMKernelExecutionBuilder.h"
#include "execute/OMRuntimeKernel.h"
 
namespace onert_micro
{
namespace execute
{
 
void readQuantParams(const circle::Tensor *tensor, long &zero_point, float &scale);
template <typename T>
OMStatus calculateActivationRange(circle::ActivationFunctionType activation, T *activation_min,
                                  T *activation_max)
{
  switch (activation)
  {
    case circle::ActivationFunctionType::ActivationFunctionType_NONE:
      *activation_min = std::numeric_limits<T>::lowest();
      *activation_max = std::numeric_limits<T>::max();
      break;
    case circle::ActivationFunctionType::ActivationFunctionType_RELU:
      *activation_min = 0;
      *activation_max = std::numeric_limits<T>::max();
      break;
    case circle::ActivationFunctionType::ActivationFunctionType_RELU_N1_TO_1:
      *activation_min = -1;
      *activation_max = 1;
      break;
    case circle::ActivationFunctionType::ActivationFunctionType_RELU6:
      *activation_min = 0;
      *activation_max = 6;
      break;
    default:
      assert(false && "Unsupported activation.");
      return UnsupportedActivation;
  }
 
  return Ok;
}
 
inline double getQuantizedConvolutionMultipler(float input_scale, float filter_scale,
                                               float output_scale)
{
  const double input_product_scale = static_cast<double>(input_scale * filter_scale);
 
  assert(input_product_scale >= 0);
 
  assert(output_scale != 0.f);
 
  return input_product_scale / static_cast<double>(output_scale);
}
 
// Decompose a double multiplier into a Q0.31 int32 representation of its
// significand, and shift representation of its exponent.
//
// Handles an arbitrary positive multiplier. The 'shift' output-value is
// basically the 'floating-point exponent' of the multiplier:
// Negative for a right-shift (when the multiplier is <1), positive for a
// left-shift (when the multiplier is >1)
void quantizeMultiplier(double double_multiplier, int32_t *quantized_multiplier, int *shift);
 
// Decompose a double multiplier into a Q0.31 int32 representation of its
// significand, and shift representation of NEGATIVE its exponent ---
// this is intended as a RIGHT-shift.
//
// Restricted to the case where the multiplier < 1 (and non-negative).
void quantizeMultiplierSmallerThanOneExp(double double_multiplier, int32_t *quantized_multiplier,
                                         int *left_shift);
 
inline std::vector<double>
getQuantizedConvolutionMultiplers(float input_scale, const flatbuffers::Vector<float> *filter_scale,
                                  float output_scale)
{
  std::vector<double> effective_output_scales;
  size_t n = filter_scale->size();
  effective_output_scales.reserve(n);
  for (size_t i = 0; i < n; ++i)
  {
    effective_output_scales.push_back(
      getQuantizedConvolutionMultipler(input_scale, filter_scale->operator[](i), output_scale));
  }
  return effective_output_scales;
}
 
OMStatus calculateActivationRangeQuantized(circle::ActivationFunctionType activation,
                                           int32_t output_zero_point, float output_scale,
                                           circle::TensorType data_type, int32_t *activation_min,
                                           int32_t *activation_max);
 
inline int computeOutSize(circle::Padding padding, int image_size, int filter_size, int stride,
                          int dilation_rate = 1)
{
  int effective_filter_size = (filter_size - 1) * dilation_rate + 1;
 
  if (stride == 0)
    return 0;
 
  switch (padding)
  {
    case circle::Padding_SAME:
      return (image_size + stride - 1) / stride;
    case circle::Padding_VALID:
      return (image_size + stride - effective_filter_size) / stride;
    default:
      return 0;
  }
}
 
inline int computePadding(int32_t stride, int32_t dilation_rate, int32_t in_size,
                          int32_t filter_size, int32_t out_size)
{
  int32_t effective_filter_size = (filter_size - 1) * dilation_rate + 1;
  int32_t padding = ((out_size - 1) * stride + effective_filter_size - in_size) / 2;
  return padding > 0 ? padding : 0;
}
 
inline void computePaddingHeightWidth(int32_t stride_height, int32_t stride_width,
                                      int32_t dilation_rate_height, int32_t dilation_rate_width,
                                      int32_t in_height, int32_t in_width, int32_t filter_height,
                                      int32_t filter_width, circle::Padding padding,
                                      int32_t *padding_h, int32_t *padding_w)
{
 
  int out_width =
    computeOutSize(padding, in_width, filter_width, stride_width, dilation_rate_width);
  int out_height =
    computeOutSize(padding, in_height, filter_height, stride_height, dilation_rate_height);
 
  *padding_h =
    computePadding(stride_height, dilation_rate_height, in_height, filter_height, out_height);
 
  *padding_w = computePadding(stride_width, dilation_rate_width, in_width, filter_width, out_width);
}
 
void calculateQuantParams(core::ArithmeticQuantParams &params, const circle::Tensor *input1,
                          const circle::Tensor *input2, const circle::Tensor *output,
                          circle::ActivationFunctionType act);
 
OMStatus SISOHeader(const OMExecuteArgs &execute_args, const circle::Tensor **input,
                    const circle::Tensor **output, uint8_t **input_data, uint8_t **output_data);
 
OMStatus TISOHeader(const OMExecuteArgs &execute_args, const circle::Tensor **input1,
                    const circle::Tensor **input2, const circle::Tensor **output,
                    OMRuntimeKernel *runtime_kernel);
 
inline int calculateInputRadius(int input_integer_bits, int input_left_shift, int total_signed_bits)
{
  const double max_input_rescaled = 1.0 * ((1 << input_integer_bits) - 1) *
                                    (1LL << (total_signed_bits - input_integer_bits)) /
                                    (1LL << input_left_shift);
  // Tighten bound using floor.  Suppose that we could use the exact value.
  // After scaling the difference, the result would be at the maximum.  Thus we
  // must ensure that our value has lower magnitude.
  return static_cast<int>(std::floor(max_input_rescaled));
}
 
} // namespace execute
} // namespace onert_micro
 
#endif // ONERT_MICRO_EXECUTE_UTILS_H