PoolLayer.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 "PoolLayer.h"
#include "OperationUtils.h"
#include "../Tensor.h"
 
#include <cker/Utils.h>
#include <cker/operation/AveragePool.h>
#include <cker/train/operation/AveragePool.h>
#include <cker/train/operation/MaxPool.h>
#include <cker/train/operation/ReLU.h>
 
namespace onert::backend::train::ops
{
 
namespace
{
 
class MaxPool2D final : public TrainingKernelRegistry
{
private:
  const ir::Activation _activation;
  const IPortableTensor *_output;
  nnfw::cker::PoolParams _op_params;
 
  std::unique_ptr<Tensor> _act_back_prop_output;
  std::unique_ptr<Tensor> _arg_max_index;
 
public:
  MaxPool2D(const uint32_t paddingLeft, const uint32_t, const uint32_t paddingTop, const uint32_t,
            const uint32_t strideWidth, const uint32_t strideHeight, const uint32_t kernelWidth,
            const uint32_t kernelHeight, const ir::Activation activation,
            const IPortableTensor *output)
    : _activation(activation), _output(output)
  {
    {
      _op_params.stride_height = strideHeight;
      _op_params.stride_width = strideWidth;
      _op_params.filter_height = kernelHeight;
      _op_params.filter_width = kernelWidth;
      assert(paddingTop < (1 << 15));
      assert(paddingLeft < (1 << 15));
      _op_params.padding_values.height = static_cast<int16_t>(paddingTop);
      _op_params.padding_values.width = static_cast<int16_t>(paddingLeft);
      CalculateActivationRange<float>(activation, &_op_params.float_activation_min,
                                      &_op_params.float_activation_max);
    }
 
    _arg_max_index = std::make_unique<Tensor>(_output->get_info());
    _arg_max_index->setBuffer(std::make_shared<basic::Allocator>(_output->total_size()));
 
    if (activation != ir::Activation::NONE)
    {
      _act_back_prop_output = std::make_unique<Tensor>(_output->get_info());
      _act_back_prop_output->setBuffer(std::make_shared<basic::Allocator>(_output->total_size()));
    }
  };
 
  ~MaxPool2D() {}
 
public:
  void forward(const IPortableTensor *in, IPortableTensor *out)
  {
    auto out_shape = getShape(out);
    auto out_data = getBuffer<float>(out);
    auto arg_max_index = _arg_max_index.get();
 
    // maxpool forward
    nnfw::cker::train::MaxPool2D(_op_params, getShape(in), getBuffer<float>(in), out_shape,
                                 out_data, getBuffer<int>(arg_max_index));
  }
 
  void backward(const IPortableTensor *back_prop_out, IPortableTensor *back_prop_in)
  {
    // activation backward
    try
    {
      back_prop_out =
        backpropActivation(_activation, _output, back_prop_out, _act_back_prop_output.get());
    }
    catch (const std::exception &e)
    {
      throw std::runtime_error{"train PoolLayer: " + std::string(e.what())};
    }
    assert(back_prop_out != nullptr);
 
    // maxpool baackward
    auto arg_max_index = _arg_max_index.get();
    nnfw::cker::train::MaxPool2DGrad(getShape(back_prop_out), getBuffer<float>(back_prop_out),
                                     getBuffer<int>(arg_max_index), getShape(back_prop_in),
                                     getBuffer<float>(back_prop_in));
  }
};
 
class AveragePool2D final : public TrainingKernelRegistry
{
private:
  const ir::Activation _activation;
  const IPortableTensor *_output;
  nnfw::cker::PoolParams _op_params;
 
  std::unique_ptr<Tensor> _act_back_prop_output;
  std::unique_ptr<Tensor> _arg_avg_index;
 
public:
  AveragePool2D(const uint32_t paddingLeft, const uint32_t, const uint32_t paddingTop,
                const uint32_t, const uint32_t strideWidth, const uint32_t strideHeight,
                const uint32_t kernelWidth, const uint32_t kernelHeight,
                const ir::Activation activation, const IPortableTensor *output)
    : _activation(activation), _output(output)
  {
    {
      _op_params.stride_height = strideHeight;
      _op_params.stride_width = strideWidth;
      _op_params.filter_height = kernelHeight;
      _op_params.filter_width = kernelWidth;
      assert(paddingTop < (1 << 15));
      assert(paddingLeft < (1 << 15));
      _op_params.padding_values.height = static_cast<int16_t>(paddingTop);
      _op_params.padding_values.width = static_cast<int16_t>(paddingLeft);
      CalculateActivationRange<float>(activation, &_op_params.float_activation_min,
                                      &_op_params.float_activation_max);
    }
 
    if (activation != ir::Activation::NONE)
    {
      _act_back_prop_output = std::make_unique<Tensor>(_output->get_info());
      _act_back_prop_output->setBuffer(std::make_shared<basic::Allocator>(_output->total_size()));
    }
  };
 
  ~AveragePool2D() {}
 
public:
  void forward(const IPortableTensor *in, IPortableTensor *out)
  {
    auto out_shape = getShape(out);
    auto out_data = getBuffer<float>(out);
 
    // avgpool forward
    nnfw::cker::AveragePool<float>(_op_params, getShape(in), getBuffer<float>(in), out_shape,
                                   out_data);
  }
 
  void backward(const IPortableTensor *back_prop_out, IPortableTensor *back_prop_in)
  {
    // activation backward
    try
    {
      back_prop_out =
        backpropActivation(_activation, _output, back_prop_out, _act_back_prop_output.get());
    }
    catch (const std::exception &e)
    {
      throw std::runtime_error{"train PoolLayer: " + std::string(e.what())};
    }
    assert(back_prop_out != nullptr);
 
    // averagepool baackward
    nnfw::cker::train::AveragePool2DGrad(_op_params, getShape(back_prop_out),
                                         getBuffer<float>(back_prop_out), getShape(back_prop_in),
                                         getBuffer<float>(back_prop_in));
  }
};
 
} // namespace
 
PoolLayer::PoolLayer()
  : cpu::ops::PoolLayer(), _back_prop_input(nullptr), _back_prop_output(nullptr), _kernel(nullptr)
{
  // DO NOTHING
}
PoolLayer::PoolLayer() {…}
 
void PoolLayer::configureBackward(const uint32_t paddingLeft, const uint32_t paddingRight,
                                  const uint32_t paddingTop, const uint32_t paddingBottom,
                                  const uint32_t strideWidth, const uint32_t strideHeight,
                                  const uint32_t kernelWidth, const uint32_t kernelHeight,
                                  const ir::Activation activation, const PoolType op_type,
                                  IPortableTensor *output, IPortableTensor *back_prop_input,
                                  const IPortableTensor *back_prop_output)
{
  _back_prop_output = back_prop_output;
  _back_prop_input = back_prop_input;
 
  if (output->data_type() != OperandType::FLOAT32)
  {
    throw std::runtime_error("PoolLayer : Unsupported data type for training");
  }
 
  // ready training kernel
  switch (op_type)
  {
    case PoolType::kMax:
      _kernel = std::make_unique<MaxPool2D>(paddingLeft, paddingRight, paddingTop, paddingBottom,
                                            strideWidth, strideHeight, kernelWidth, kernelHeight,
                                            activation, output);
      break;
    case PoolType::kAvg:
      _kernel = std::make_unique<AveragePool2D>(paddingLeft, paddingRight, paddingTop,
                                                paddingBottom, strideWidth, strideHeight,
                                                kernelWidth, kernelHeight, activation, output);
      break;
    default:
      throw std::runtime_error("PoolLayer: Unsupported pool type");
  }
}
void PoolLayer::configureBackward(const uint32_t paddingLeft, const uint32_t paddingRight, {…}
 
void PoolLayer::forward(bool training)
{
  if (training)
  {
    _kernel->forward(_input, _output);
  }
  else
  {
    cpu::ops::PoolLayer::run();
  }
}
void PoolLayer::forward(bool training) {…}
 
void PoolLayer::backward() { _kernel->backward(_back_prop_output, _back_prop_input); }
 
} // namespace onert::backend::train::ops