ElementwiseActivationLayer.cc

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/*
 * Copyright (c) 2023 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 "ElementwiseActivationLayer.h"
 
#include "OperationUtils.h"
 
#include <cker/train/operation/ReLU.h>
#include <cker/train/operation/ReLU6.h>
 
namespace onert::backend::train::ops
{
 
ElementwiseActivationLayer::ElementwiseActivationLayer() : cpu::ops::ElementwiseActivationLayer()
{
  // DO NOTHING
}
ElementwiseActivationLayer::ElementwiseActivationLayer() : cpu::ops::ElementwiseActivationLayer() {…}
 
void ElementwiseActivationLayer::configureBackward(const IPortableTensor *input,
                                                   IPortableTensor *back_prop_input,
                                                   const IPortableTensor *back_prop_output,
                                                   float alpha, float beta,
                                                   ElementwiseActivationType op_type)
{
  assert(input != nullptr);
  assert(back_prop_input != nullptr);
  assert(back_prop_output != nullptr);
 
  _back_prop_input = back_prop_input;
  _back_prop_output = back_prop_output;
 
  _op_type = op_type;
 
  switch (op_type)
  {
    case ElementwiseActivationType::kReLU:
      if (input->data_type() == OperandType::FLOAT32)
      {
        if ((alpha == std::numeric_limits<float>::infinity() || alpha == 6.0f) && beta == 0.f)
        {
          auto relu_cker = [&alpha]() {
            if (alpha == std::numeric_limits<float>::infinity())
              return nnfw::cker::train::ReLUGrad;
            else if (alpha == 6.0f)
              return nnfw::cker::train::ReLU6Grad;
            else
              throw std::runtime_error{"no supported relu kernel"};
          }();
 
          _backward_kernel = [relu_cker](const IPortableTensor *output,
                                         const IPortableTensor *incoming,
                                         IPortableTensor *outgoing) {
            relu_cker(getShape(output), getBuffer<float>(output), getShape(incoming),
                      getBuffer<float>(incoming), getShape(outgoing), getBuffer<float>(outgoing));
          };
        }
        else
        {
          throw std::runtime_error(
            "train ElementwiseActivationLayer : Unsupported ReLU activation type");
        }
      }
      else
      {
        throw std::runtime_error("train ElementwiseActivationLayer: Unsupported datatype");
      }
      break;
    default:
      throw std::runtime_error("train ElementwiseActivationLayer: Unsupported activation type yet");
  }
}
void ElementwiseActivationLayer::configureBackward(const IPortableTensor *input, {…}
 
void ElementwiseActivationLayer::forward(bool) { cpu::ops::ElementwiseActivationLayer::run(); }
 
void ElementwiseActivationLayer::backward()
{
  _backward_kernel(_output, _back_prop_output, _back_prop_input);
}
void ElementwiseActivationLayer::backward() {…}
 
} // namespace onert::backend::train::ops