RmsNorm.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 __NNFW_CKER_RMS_NORM_H__
#define __NNFW_CKER_RMS_NORM_H__
 
#include "cker/Shape.h"
#include "cker/Types.h"
#include "cker/Utils.h"
 
#include <cmath>
#include <stdexcept>
 
namespace nnfw
{
namespace cker
{
 
inline void RmsNorm(const RmsNormParams &params, const Shape &input_shape, const float *input_data,
                    const Shape &gamma_shape, const float *gamma_data, const Shape &output_shape,
                    float *output_data)
{
  bool single_gamma = gamma_shape.DimensionsCount() == 1 && gamma_shape.Dims(0) == 1;
 
  if (input_shape.DimensionsCount() == 4)
  {
    const int32_t batches = MatchingDim(input_shape, 0, output_shape, 0);
    const int32_t heights = MatchingDim(input_shape, 1, output_shape, 1);
    const int32_t widths = MatchingDim(input_shape, 2, output_shape, 2);
    const int32_t channels = MatchingDim(input_shape, 3, output_shape, 3);
 
    for (int32_t batch = 0; batch < batches; batch++)
    {
      for (int32_t height = 0; height < heights; height++)
      {
        for (int32_t width = 0; width < widths; width++)
        {
          // normalize over last-axis
          double square_sum = 0.0f;
          for (int32_t channel = 0; channel < channels; channel++)
          {
            double input_val = input_data[Offset(input_shape, batch, height, width, channel)];
            square_sum += (input_val * input_val);
          }
          double rms = std::sqrt((square_sum / channels) + params.epsilon);
          for (int32_t channel = 0; channel < channels; channel++)
          {
            double gamma = (single_gamma ? gamma_data[0] : gamma_data[channel]);
            output_data[Offset(output_shape, batch, height, width, channel)] =
              gamma * (input_data[Offset(input_shape, batch, height, width, channel)] / rms);
          }
        }
      }
    }
  }
  else if (input_shape.DimensionsCount() == 3)
  {
    const int32_t heights = MatchingDim(input_shape, 0, output_shape, 0);
    const int32_t widths = MatchingDim(input_shape, 1, output_shape, 1);
    const int32_t channels = MatchingDim(input_shape, 2, output_shape, 2);
 
    for (int32_t height = 0; height < heights; height++)
    {
      for (int32_t width = 0; width < widths; width++)
      {
        // normalize over last-axis
        double square_sum = 0.0f;
        for (int32_t channel = 0; channel < channels; channel++)
        {
          double input_val = input_data[(height * widths + width) * channels + channel];
          square_sum += (input_val * input_val);
        }
        double rms = std::sqrt((square_sum / channels) + params.epsilon);
        for (int32_t channel = 0; channel < channels; channel++)
        {
          double gamma = (single_gamma ? gamma_data[0] : gamma_data[channel]);
          output_data[(height * widths + width) * channels + channel] =
            gamma * (input_data[(height * widths + width) * channels + channel] / rms);
        }
      }
    }
  }
  else
  {
    throw std::runtime_error("cker::RmsNorm: Unsupported input shape");
  }
}
 
} // namespace cker
} // namespace nnfw
 
#endif // __NNFW_CKER_RMS_NORM_H__