////////////////////////////////////////////////////////////////////////////////
// Copyright (c) 2014-2023, Lawrence Livermore National Security, LLC.
// Produced at the Lawrence Livermore National Laboratory.
// Written by the LBANN Research Team (B. Van Essen, et al.) listed in
// the CONTRIBUTORS file. <lbann-dev@llnl.gov>
//
// LLNL-CODE-697807.
// All rights reserved.
//
// This file is part of LBANN: Livermore Big Artificial Neural Network
// Toolkit. For details, see http://software.llnl.gov/LBANN or
// https://github.com/LLNL/LBANN.
//
// Licensed under the Apache License, Version 2.0 (the "Licensee"); 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 LBANN_LAYERS_ACTIVATIONS_LEAKY_RELU_HPP_INCLUDED
#define LBANN_LAYERS_ACTIVATIONS_LEAKY_RELU_HPP_INCLUDED

#include "lbann/layers/data_type_layer.hpp"
#include "lbann/layers/layer.hpp"
#include "lbann/proto/datatype_helpers.hpp"
#include "lbann/proto/layers.pb.h"
#include "lbann/utils/distconv.hpp"

#ifdef LBANN_HAS_DISTCONV
#include "distconv/dnn_backend/leaky_relu.hpp"
#include "lbann/utils/distconv.hpp"
#endif

namespace lbann {

#ifdef LBANN_HAS_DISTCONV
namespace dc {
using Backend = ::distconv::BackendDNNLib;
using LeakyReLU = ::distconv::LeakyReLU<Backend>;
} // namespace dc

template <typename TensorDataType, data_layout T_layout, El::Device Dev>
class leaky_relu_distconv_adapter
  : public data_type_distconv_adapter<TensorDataType>
{
public:
  using TensorDevType =
    typename data_type_distconv_adapter<TensorDataType>::TensorDevType;

  leaky_relu_distconv_adapter(Layer& layer)
    : data_type_distconv_adapter<TensorDataType>(layer)
  {}
  virtual ~leaky_relu_distconv_adapter() = default;

  void setup_distributions(tensor_overlap_constraints& constraints) override;
  void setup_layer(size_t workspace_capacity) override;

  std::unique_ptr<dc::LeakyReLU> m_leaky_relu;
};
#endif // LBANN_HAS_DISTCONV

/**
 *  @f[
 *    \text{LeakyReLU}(x; \alpha) =
 *      \begin{cases}
 *        x        & x > 0 \\
 *        \alpha x & x \leq 0
 *      \end{cases}
 *  @f]
 *  See:
 *
 *  Andrew L. Maas, Awni Y. Hannun, and Andrew Y. Ng. "Rectifier
 *  nonlinearities improve neural network acoustic models." In
 *  Proc. ICML, vol. 30, no. 1, p. 3. 2013.
 */
template <typename TensorDataType, data_layout Layout, El::Device Device>
class leaky_relu_layer : public data_type_layer<TensorDataType>
{
public:
  leaky_relu_layer() : leaky_relu_layer(nullptr, El::To<TensorDataType>(0.01))
  {}
  leaky_relu_layer(lbann_comm* comm,
                   TensorDataType negative_slope = El::To<TensorDataType>(0.01))
    : data_type_layer<TensorDataType>(comm), m_negative_slope(negative_slope)
  {}
  leaky_relu_layer* copy() const override
  {
    return new leaky_relu_layer(*this);
  }
  std::string get_type() const override { return "leaky ReLU"; }
  data_layout get_data_layout() const override { return Layout; }
  El::Device get_device_allocation() const override { return Device; }

  description get_description() const override
  {
    auto desc = data_type_layer<TensorDataType>::get_description();
    desc.add("Negative slope", m_negative_slope);
    return desc;
  }

  /** @name Serialization */
  ///@{

  template <typename ArchiveT>
  void serialize(ArchiveT& ar);

  ///@}

protected:
  /** Add layer specific data to prototext */
  void write_specific_proto(lbann_data::Layer& proto) const final;

  void setup_dims(DataReaderMetaData& dr_metadata) override
  {
    data_type_layer<TensorDataType>::setup_dims(dr_metadata);
    this->set_output_dims(this->get_input_dims());
  }
  void fp_compute() override;
  void bp_compute() override;

private:
  /** Function slope in negative region. */
  TensorDataType m_negative_slope;

#ifdef LBANN_HAS_DISTCONV
protected:
  bool is_distconv_supported() const override
  {
    return Device == El::Device::GPU && Layout == data_layout::DATA_PARALLEL;
  }
  void setup_distconv_adapter(const DataReaderMetaData& dr_metadata) override
  {
    this->get_distconv_adapter_ptr() = std::make_unique<
      leaky_relu_distconv_adapter<TensorDataType, Layout, Device>>(*this);
  }
  leaky_relu_distconv_adapter<TensorDataType, Layout, Device>&
  get_distconv_adapter() override;
  const leaky_relu_distconv_adapter<TensorDataType, Layout, Device>&
  get_distconv_adapter() const override;
#endif // LBANN_HAS_DISTCONV
};

#ifndef LBANN_LEAKY_RELU_LAYER_INSTANTIATE
#define PROTO_DEVICE(T, Device)                                                \
  extern template class leaky_relu_layer<T,                                    \
                                         data_layout::DATA_PARALLEL,           \
                                         Device>;                              \
  extern template class leaky_relu_layer<T, data_layout::MODEL_PARALLEL, Device>

#include "lbann/macros/instantiate_device.hpp"
#undef PROTO_DEVICE
#endif // LBANN_LEAKY_RELU_LAYER_INSTANTIATE

} // namespace lbann

#endif // LBANN_LAYERS_ACTIVATIONS_LEAKY_RELU_HPP_INCLUDED
