weighted_sum.hpp

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// 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_LAYER_WEIGHTED_SUM_HPP_INCLUDED
#define LBANN_LAYER_WEIGHTED_SUM_HPP_INCLUDED

#include "lbann/layers/data_type_layer.hpp"
#include "lbann/utils/exception.hpp"
#include <vector>

namespace lbann {

template <typename TensorDataType,
          data_layout T_layout = data_layout::DATA_PARALLEL,
          El::Device Dev = El::Device::CPU>
class weighted_sum_layer : public data_type_layer<TensorDataType>
{
private:
  std::vector<DataType> m_scaling_factors;

public:
  weighted_sum_layer(lbann_comm* comm, std::vector<DataType> scaling_factors)
    : data_type_layer<TensorDataType>(comm), m_scaling_factors(scaling_factors)
  {
    this->m_expected_num_parent_layers = -1; // No limit on parents
  }

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


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


  std::string get_type() const override { return "weighted sum"; }
  data_layout get_data_layout() const override { return T_layout; }
  El::Device get_device_allocation() const override { return Dev; }
  bool can_run_inplace() const override { return true; }
  int get_backprop_requirements() const override { return ERROR_SIGNALS; }

  description get_description() const override
  {
    auto desc = data_type_layer<TensorDataType>::get_description();
    std::stringstream ss;
    for (size_t i = 0; i < m_scaling_factors.size(); ++i) {
      ss << (i > 0 ? ", " : "") << m_scaling_factors[i];
    }
    desc.add("Scaling factors", ss.str());
    return desc;
  }

protected:
  void write_specific_proto(lbann_data::Layer& proto) const final;

  friend class cereal::access;
  weighted_sum_layer() : weighted_sum_layer(nullptr, {1}) {}

  void setup_pointers() override
  {
    data_type_layer<TensorDataType>::setup_pointers();
    std::stringstream err;
    if (this->get_num_parents() < 1) {
      err << get_type() << " layer \"" << this->get_name() << "\" "
          << "has no parent layers";
      LBANN_ERROR(err.str());
    }
    if ((int)m_scaling_factors.size() != this->get_num_parents()) {
      err << get_type() << " layer \"" << this->get_name() << "\" "
          << "has an invalid number of scaling factors "
          << "(found " << m_scaling_factors.size() << ", "
          << "but there are " << this->get_num_parents() << " parent layers)";
      LBANN_ERROR(err.str());
    }
  }

  void setup_dims() override
  {
    data_type_layer<TensorDataType>::setup_dims();
    this->set_output_dims(this->get_input_dims());

    // Check that input dimensions match
    const auto& output_dims = this->get_output_dims();
    for (int i = 0; i < this->get_num_parents(); ++i) {
      if (this->get_input_dims(i) != output_dims) {
        const auto& parents = this->get_parent_layers();
        std::stringstream err;
        err << get_type() << " layer \"" << this->get_name() << "\" "
            << "has input tensors with incompatible dimensions (";
        for (int j = 0; j < this->get_num_parents(); ++j) {
          const auto& dims = this->get_input_dims(j);
          err << (j > 0 ? ", " : "") << "layer \"" << parents[j]->get_name()
              << "\" outputs ";
          for (size_t k = 0; k < dims.size(); ++k) {
            err << (k > 0 ? " x " : "") << dims[k];
          }
        }
        err << ")";
        LBANN_ERROR(err.str());
      }
    }
  }

  void fp_compute() override
  {
    auto& output = this->get_activations();

    // Special case for the first input so that in-place operation works
    if (!this->m_runs_inplace)
      El::Copy(this->get_prev_activations(0), output);

    El::Scale(m_scaling_factors[0], output);
    for (int i = 1; i < this->get_num_parents(); ++i) {
      El::Axpy(m_scaling_factors[i], this->get_prev_activations(i), output);
    }
  }

  void bp_compute() override
  {
    const auto& gradient_wrt_output = this->get_prev_error_signals();

    for (int i = 1; i < this->get_num_parents(); ++i) {
      auto& gradient_wrt_input = this->get_error_signals(i);
      El::Zero(gradient_wrt_input);
      El::Axpy(m_scaling_factors[i], gradient_wrt_output, gradient_wrt_input);
    }

    // Special case for the first input so that in-place operation works
    if (!this->m_runs_inplace)
      El::Copy(gradient_wrt_output, this->get_error_signals(0));
    El::Scale(m_scaling_factors[0], this->get_error_signals(0));
  }
};

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

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

} // namespace lbann

#endif // LBANN_LAYER_WEIGHTED_SUM_HPP_INCLUDED