PALConv2D.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_CONV_2D_H
#define ONERT_MICRO_EXECUTE_PAL_CONV_2D_H
 
#include "PALConv2DCommon.h"
#include "core/OMKernelData.h"
#include "core/OMRuntimeShape.h"
#include "PALUtils.h"
 
namespace onert_micro
{
namespace execute
{
namespace pal
{
 
// Fixed-point per-channel-quantization convolution reference kernel.
OMStatus ConvPerChannel(const core::ConvQuant &params, const core::OMRuntimeShape &input_shape,
                        const int8_t *input_data, const core::OMRuntimeShape &filter_shape,
                        const int8_t *filter_data, const int32_t *bias_data,
                        const core::OMRuntimeShape &output_shape, int8_t *output_data)
{
  // Get parameters.
  const int32_t input_offset = params.input_offset; // r = s(q - Z)
  const int stride_width = params.stride_width;
  const int stride_height = params.stride_height;
  const int dilation_width_factor = params.dilation_width_factor;
  const int dilation_height_factor = params.dilation_height_factor;
  const int pad_width = params.pad_w;
  const int pad_height = params.pad_h;
  const int32_t output_offset = params.output_offset;
 
  const auto &output_multiplier = params.per_channel_output_multiplier;
  const auto &output_shift = params.per_channel_output_shift;
 
  // Set min and max value of the output.
  const int32_t output_activation_min = params.quantized_activation_min;
  const int32_t output_activation_max = params.quantized_activation_max;
 
  // Consistency check.
  assert(output_activation_max >= output_activation_min);
  assert(input_shape.dimensionsCount() == 4);
  assert(filter_shape.dimensionsCount() == 4);
  assert(output_shape.dimensionsCount() == 4);
 
  const int batches = MatchingDim(input_shape, 0, output_shape, 0);
  const int input_depth = input_shape.dims(3);
  const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3);
 
  // Check dimensions of the tensors.
  const int input_height = input_shape.dims(1);
  const int input_width = input_shape.dims(2);
  const int filter_height = filter_shape.dims(1);
  const int filter_width = filter_shape.dims(2);
  const int filter_input_depth = filter_shape.dims(3);
  const int groups = input_depth / filter_input_depth;
  assert(groups != 0);
  assert(input_depth % filter_input_depth == 0);
  const int filters_per_group = output_depth / groups;
  assert(filters_per_group != 0);
  const int output_height = output_shape.dims(1);
  const int output_width = output_shape.dims(2);
  for (int batch = 0; batch < batches; ++batch)
  {
    for (int out_y = 0; out_y < output_height; ++out_y)
    {
      const int in_y_origin = (out_y * stride_height) - pad_height;
      for (int out_x = 0; out_x < output_width; ++out_x)
      {
        const int in_x_origin = (out_x * stride_width) - pad_width;
        for (int out_channel = 0; out_channel < output_depth; ++out_channel)
        {
          auto group = out_channel / filters_per_group;
          int32_t acc = 0;
          for (int filter_y = 0; filter_y < filter_height; ++filter_y)
          {
            const int in_y = in_y_origin + dilation_height_factor * filter_y;
            for (int filter_x = 0; filter_x < filter_width; ++filter_x)
            {
              const int in_x = in_x_origin + dilation_width_factor * filter_x;
 
              // Zero padding by omitting the areas outside the image.
              const bool is_point_inside_image =
                (in_x >= 0) && (in_x < input_width) && (in_y >= 0) && (in_y < input_height);
 
              if (!is_point_inside_image)
              {
                continue;
              }
 
              for (int in_channel = 0; in_channel < filter_input_depth; ++in_channel)
              {
                int32_t input_val = input_data[offset(input_shape.dimsData(), batch, in_y, in_x,
                                                      in_channel + group * filter_input_depth)];
                int32_t filter_val = filter_data[offset(filter_shape.dimsData(), out_channel,
                                                        filter_y, filter_x, in_channel)];
                // Accumulate with 32 bits accumulator.
                // In the nudging process during model quantization, we force
                // real value of 0.0 be represented by a quantized value. This
                // guarantees that the input_offset is a int8_t, even though
                // it is represented using int32_t. int32_t += int8_t *
                // (int8_t - int8_t) so the highest value we can get from each
                // accumulation is [-127, 127] * ([-128, 127] -
                // [-128, 127]), which is [-32512, 32512]. log2(32512)
                // = 14.98, which means we can accumulate at least 2^16
                // multiplications without overflow. The accumulator is
                // applied to a filter so the accumulation logic will hold as
                // long as the filter size (filter_y * filter_x * in_channel)
                // does not exceed 2^16, which is the case in all the models
                // we have seen so far.
                // accumulator depth is smaller than 2^16.
                acc += filter_val * (input_val + input_offset);
              }
            }
          }
 
          if (bias_data)
          {
            acc += bias_data[out_channel];
          }
          acc = multiplyByQuantizedMultiplier(acc, output_multiplier[out_channel],
                                              output_shift[out_channel]);
          acc += output_offset;
          acc = std::max(acc, output_activation_min);
          acc = std::min(acc, output_activation_max);
          output_data[offset(output_shape.dimsData(), batch, out_y, out_x, out_channel)] =
            static_cast<int8_t>(acc);
        }
      }
    }
  }
  return Ok;
}
 
} // namespace pal
} // namespace execute
} // namespace onert_micro
 
#endif // ONERT_MICRO_EXECUTE_PAL_CONV_2D_H