distconv_layers.hpp

Go to the documentation of this file.

// 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_LEARNING_DISTCONV_LAYERS
#define LBANN_LAYERS_LEARNING_DISTCONV_LAYERS
#include "distconv/base.hpp"
#include "distconv/tensor/tensor.hpp"
#include "distconv/tensor/tensor_mpi.hpp"
#include "lbann/utils/distconv.hpp"

#ifdef LBANN_HAS_DISTCONV
namespace distconv {
template <typename Backend, typename DataType>
class ChannelwiseFullyConnected
{
  using LocaleMPI = tensor::LocaleMPI;

public:
  ChannelwiseFullyConnected(Backend& backend) : m_be(backend){};

  template <typename Allocator>
  int forward(
    bool transpose_A,
    const tensor::Tensor<DataType, tensor::LocaleMPI, Allocator>& input,
    const tensor::Tensor<DataType, tensor::LocaleMPI, Allocator>& linearity,
    tensor::Tensor<DataType, tensor::LocaleMPI, Allocator>& output);

  template <typename Allocator>
  int apply_bias(
    const tensor::Tensor<DataType, tensor::LocaleMPI, Allocator>& bias,
    tensor::Tensor<DataType, tensor::LocaleMPI, Allocator>& output);

  template <typename Allocator>
  int backward_wrt_input(
    bool transpose_A,
    const tensor::Tensor<DataType, tensor::LocaleMPI, Allocator>& output_grad,
    const tensor::Tensor<DataType, tensor::LocaleMPI, Allocator>& linearity,
    tensor::Tensor<DataType, tensor::LocaleMPI, Allocator>& input_grad);

  template <typename Allocator>
  int backward_wrt_weight(
    bool transpose,
    DataType dst_scale,
    DataType gradient_scale,
    const tensor::Tensor<DataType, tensor::LocaleMPI, Allocator>& input,
    const tensor::Tensor<DataType, tensor::LocaleMPI, Allocator>& output_grad,
    tensor::Tensor<DataType, tensor::LocaleMPI, Allocator>& linearity_grad);

  template <typename Allocator>
  int backward_wrt_bias(
    DataType gradient_scale,
    DataType dst_scale,
    const tensor::Tensor<DataType, tensor::LocaleMPI, Allocator>& output_grad,
    tensor::Tensor<DataType, tensor::LocaleMPI, Allocator>& bias_grad);

protected:
  Backend& m_be;
}; // class definition ChannelwiseFullyConnected

template <typename DataType, typename locale, typename Allocator>
tensor::Shape get_fc_output_local_tensor_shape(
  const tensor::Tensor<DataType, locale, Allocator>& input,
  const int_vector& linearity_dims,
  bool transpose)
{

  // https://github.com/LLNL/DiHydrogen/blob/7f86db1f9701ac3afb5e16aefdd57563d57a1698/legacy/include/distconv/distconv.hpp#L173

  // Get the input layer local tensor shape

  auto output_local_shape = input.get_local_shape();
  output_local_shape[0] = transpose ? linearity_dims[1] : linearity_dims[0];
  return output_local_shape;
}
extern template class ChannelwiseFullyConnected<::distconv::BackendDNNLib,
                                                float>;
extern template class ChannelwiseFullyConnected<::distconv::BackendDNNLib,
                                                double>;
} // namespace distconv

#endif // LBANN_HAS_DISTCONV
#endif // LBANN_LAYERS_LEARNING_DISTCONV_LAYERS