ConvolutionCommon.cpp

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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.
 */
 
#include "execute/kernels/ConvolutionCommon.h"
#include "execute/OMUtils.h"
 
using namespace onert_micro;
using namespace onert_micro::core;
 
namespace onert_micro
{
namespace execute
{
 
OMStatus createConvParams(core::ConvQuant &params, const circle::Tensor *input,
                          const circle::Tensor *filter, const circle::Tensor *output,
                          circle::ActivationFunctionType act_type)
{
  assert(input->quantization() != nullptr);  // Fix caller
  assert(filter->quantization() != nullptr); // Fix caller
  assert(output->quantization() != nullptr); // Fix caller
 
  const auto *input_scales = input->quantization()->scale();
  const auto *filter_scales = filter->quantization()->scale();
  const auto *output_scales = output->quantization()->scale();
 
  assert(input_scales != nullptr);  // Fix caller
  assert(filter_scales != nullptr); // Fix caller
  assert(output_scales != nullptr); // Fix caller
 
  assert(input_scales->size() != 0);  // Fix caller
  assert(filter_scales->size() != 0); // Fix caller
  assert(output_scales->size() != 0); // Fix caller
 
  const auto input_zero_points = input->quantization()->zero_point();
  const auto filter_zero_points = filter->quantization()->zero_point();
  const auto output_zero_points = output->quantization()->zero_point();
 
  assert(input_zero_points != nullptr);  // Fix caller
  assert(filter_zero_points != nullptr); // Fix caller
  assert(output_zero_points != nullptr); // Fix caller
 
  assert(input_zero_points->size() != 0);  // Fix caller
  assert(filter_zero_points->size() != 0); // Fix caller
  assert(output_zero_points->size() != 0); // Fix caller
 
  const auto input_zp = input_zero_points->operator[](0);
  const auto filter_zp = filter_zero_points->operator[](0);
  const auto output_zp = output_zero_points->operator[](0);
 
  const auto output_scale = output_scales->operator[](0);
 
  int32_t activation_min{};
  int32_t activation_max{};
  OMStatus status = execute::calculateActivationRangeQuantized(
    act_type, static_cast<int32_t>(output_zp), output_scale, output->type(), &activation_min,
    &activation_max);
  assert(status == Ok);
  if (status != Ok)
    return status;
 
  // The kernel expects input and filter zero points to be negated.
  params.input_offset = -static_cast<int32_t>(input_zp);    // Note the '-'.
  params.weights_offset = -static_cast<int32_t>(filter_zp); // Note the '-'.
  params.output_offset = static_cast<int32_t>(output_zp);
  params.quantized_activation_min = activation_min;
  params.quantized_activation_max = activation_max;
 
  assert(filter_scales->size() > 1); // Support only channel-wise quantization
  // Channel-wise quantization
  const auto input_scale = input_scales->operator[](0);
  const std::vector<double> effective_output_scale =
    execute::getQuantizedConvolutionMultiplers(input_scale, filter_scales, output_scale);
 
  size_t n = effective_output_scale.size();
  params.per_channel_output_shift.resize(n);
  params.per_channel_output_multiplier.resize(n);
  for (size_t i = 0; i < n; ++i)
  {
    execute::quantizeMultiplier(effective_output_scale[i], &params.per_channel_output_multiplier[i],
                                &params.per_channel_output_shift[i]);
  }
 
  return Ok;
}
OMStatus createConvParams(core::ConvQuant &params, const circle::Tensor *input, {…}
 
} // namespace execute
} // namespace onert_micro