////////////////////////////////////////////////////////////////////////////////
// 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_LOSS_MEAN_SQUARED_ERROR_HPP_INCLUDED
#define LBANN_LAYERS_LOSS_MEAN_SQUARED_ERROR_HPP_INCLUDED

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

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

namespace lbann {

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

template <typename TensorDataType, data_layout T_layout, El::Device Dev>
class mean_squared_error_distconv_adapter
  : public data_type_distconv_adapter<TensorDataType>
{
public:
  using TensorDevType =
    typename data_type_distconv_adapter<TensorDataType>::TensorDevType;
  mean_squared_error_distconv_adapter(Layer& layer)
    : data_type_distconv_adapter<TensorDataType>(layer)
  {}
  virtual ~mean_squared_error_distconv_adapter() = default;
  void setup_distributions(tensor_overlap_constraints& constraints) override;
  dc::Shape get_prev_activations_shape(int index) const override;
  dc::Shape get_activations_shape(int index) const override;
  dc::Shape get_activations_local_shape(int index) const override;
  void setup_layer(size_t workspace_capacity) override;
  std::unique_ptr<dc::MeanSquaredError> m_mean_squared_error;
};
#endif // LBANN_HAS_DISTCONV

/** @brief Mean squared error
 *
 *  Given a prediction @f$y@f$ and ground truth @f$\hat{y}@f$,
 *  @f[
 *    MSE(y,\hat{y})
 *      = \frac{1}{n} \sum\limits_{i=1}^{n} (y_i - \hat{y}_i)^2
 *  @f]
 */
template <typename TensorDataType, data_layout T_layout, El::Device Dev>
class mean_squared_error_layer : public data_type_layer<TensorDataType>
{
public:
  /** @name Public Types */
  ///@{

  /** @brief The tensor type expected in this object. */
  using AbsDistMatrixType = El::AbstractDistMatrix<TensorDataType>;

  ///@}

public:
  mean_squared_error_layer(lbann_comm* comm)
    : data_type_layer<TensorDataType>(comm)
  {
    this->m_expected_num_parent_layers = 2;
  }

  mean_squared_error_layer(const mean_squared_error_layer& other)
    : data_type_layer<TensorDataType>(other)
  {
    m_workspace.reset(other.m_workspace ? other.m_workspace->Copy() : nullptr);
  }

  mean_squared_error_layer& operator=(const mean_squared_error_layer& other)
  {
    data_type_layer<TensorDataType>::operator=(other);
    m_workspace.reset(other.m_workspace ? other.m_workspace->Copy() : nullptr);
    return *this;
  }

  mean_squared_error_layer* copy() const override
  {
    return new mean_squared_error_layer(*this);
  }

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

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

  ///@}

  std::string get_type() const override { return "mean squared error"; }
  data_layout get_data_layout() const override { return T_layout; }
  El::Device get_device_allocation() const override { return Dev; }

#ifdef LBANN_HAS_ONNX
  void fill_onnx_node(onnx::GraphProto& graph) const override;
#endif // LBANN_HAS_ONNX

  void setup_dims(DataReaderMetaData& dr_metadata) override;

  void setup_data(size_t max_mini_batch_size) override;

  void fp_compute() override;

  void bp_compute() override;

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

  friend class cereal::access;
  mean_squared_error_layer() : mean_squared_error_layer(nullptr) {}

private:
  /** Compute local contributions to mean squared error loss. */
  void local_fp_compute();
  /** Compute local gradients. */
  void local_bp_compute();

  /** Workspace matrix. */
  std::unique_ptr<AbsDistMatrixType> m_workspace;

#ifdef LBANN_HAS_DISTCONV
  friend class mean_squared_error_distconv_adapter<TensorDataType,
                                                   T_layout,
                                                   Dev>;

protected:
  bool is_distconv_supported() const override
  {
    return Dev == El::Device::GPU && T_layout == data_layout::DATA_PARALLEL;
  }

  void setup_distconv_adapter(const DataReaderMetaData& dr_metadata) override
  {
    this->get_distconv_adapter_ptr() = std::make_unique<
      mean_squared_error_distconv_adapter<TensorDataType, T_layout, Dev>>(
      *this);
  }

  mean_squared_error_distconv_adapter<TensorDataType, T_layout, Dev>&
  get_distconv_adapter() override;
  const mean_squared_error_distconv_adapter<TensorDataType, T_layout, Dev>&
  get_distconv_adapter() const override;

  void fp_compute_distconv()
  {
    assert_always(this->distconv_enabled());
    get_distconv_adapter().m_mean_squared_error->forward(
      this->get_distconv_adapter().get_prev_activations(0),
      this->get_distconv_adapter().get_prev_activations(1),
      this->get_distconv_adapter().get_activations());
  }

  void bp_compute_distconv()
  {
    assert_always(this->distconv_enabled());
    get_distconv_adapter().m_mean_squared_error->backward(
      this->get_distconv_adapter().get_prev_activations(0),
      this->get_distconv_adapter().get_prev_activations(1),
      this->get_distconv_adapter().get_prev_error_signals(0),
      this->get_distconv_adapter().get_error_signals(0),
      this->get_distconv_adapter().get_error_signals(1));
  }
#endif // LBANN_HAS_DISTCONV
};

#ifndef LBANN_MEAN_SQUARED_ERROR_LAYER_INSTANTIATE

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

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

#endif // LBANN_MEAN_SQUARED_ERROR_LAYER_INSTANTIATE

} // namespace lbann

#endif // LBANN_LAYERS_LOSS_MEAN_SQUARED_ERROR_HPP_INCLUDED
