lstm_layer.cpp

/*
All modification made by Intel Corporation: © 2016 Intel Corporation

All contributions by the University of California:
Copyright (c) 2014, 2015, The Regents of the University of California (Regents)
All rights reserved.

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Copyright (c) 2014, 2015, the respective contributors
All rights reserved.
For the list of contributors go to https://github.com/BVLC/caffe/blob/master/CONTRIBUTORS.md


Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

    * Redistributions of source code must retain the above copyright notice,
      this list of conditions and the following disclaimer.
    * Redistributions in binary form must reproduce the above copyright
      notice, this list of conditions and the following disclaimer in the
      documentation and/or other materials provided with the distribution.
    * Neither the name of Intel Corporation nor the names of its contributors
      may be used to endorse or promote products derived from this software
      without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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*/

#include <string>
#include <vector>

#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/filler.hpp"
#include "caffe/layer.hpp"
#include "caffe/layers/lstm_layer.hpp"
#include "caffe/util/math_functions.hpp"

namespace caffe {

template <typename Dtype>
void LSTMLayer<Dtype>::RecurrentInputBlobNames(vector<string>* names) const {
  names->resize(2);
  (*names)[0] = "h_0";
  (*names)[1] = "c_0";
}

template <typename Dtype>
void LSTMLayer<Dtype>::RecurrentOutputBlobNames(vector<string>* names) const {
  names->resize(2);
  (*names)[0] = "h_" + format_int(this->T_);
  (*names)[1] = "c_T";
}

template <typename Dtype>
void LSTMLayer<Dtype>::RecurrentInputShapes(vector<BlobShape>* shapes) const {
  const int num_output = this->layer_param_.recurrent_param().num_output();
  const int num_blobs = 2;
  shapes->resize(num_blobs);
  for (int i = 0; i < num_blobs; ++i) {
    (*shapes)[i].Clear();
    (*shapes)[i].add_dim(1);  // a single timestep
    (*shapes)[i].add_dim(this->N_);
    (*shapes)[i].add_dim(num_output);
  }
}

template <typename Dtype>
void LSTMLayer<Dtype>::OutputBlobNames(vector<string>* names) const {
  names->resize(1);
  (*names)[0] = "h";
}

template <typename Dtype>
void LSTMLayer<Dtype>::FillUnrolledNet(NetParameter* net_param) const {
  const int num_output = this->layer_param_.recurrent_param().num_output();
  CHECK_GT(num_output, 0) << "num_output must be positive";
  const FillerParameter& weight_filler =
      this->layer_param_.recurrent_param().weight_filler();
  const FillerParameter& bias_filler =
      this->layer_param_.recurrent_param().bias_filler();

  // Add generic LayerParameter's (without bottoms/tops) of layer types we'll
  // use to save redundant code.
  LayerParameter hidden_param;
  hidden_param.set_type("InnerProduct");
  hidden_param.mutable_inner_product_param()->set_num_output(num_output * 4);
  hidden_param.mutable_inner_product_param()->set_bias_term(false);
  hidden_param.mutable_inner_product_param()->set_axis(2);
  hidden_param.mutable_inner_product_param()->
      mutable_weight_filler()->CopyFrom(weight_filler);

  LayerParameter biased_hidden_param(hidden_param);
  biased_hidden_param.mutable_inner_product_param()->set_bias_term(true);
  biased_hidden_param.mutable_inner_product_param()->
      mutable_bias_filler()->CopyFrom(bias_filler);

  LayerParameter sum_param;
  sum_param.set_type("Eltwise");
  sum_param.mutable_eltwise_param()->set_operation(
      EltwiseParameter_EltwiseOp_SUM);

  LayerParameter scale_param;
  scale_param.set_type("Scale");
  scale_param.mutable_scale_param()->set_axis(0);

  LayerParameter slice_param;
  slice_param.set_type("Slice");
  slice_param.mutable_slice_param()->set_axis(0);

  LayerParameter split_param;
  split_param.set_type("Split");

  vector<BlobShape> input_shapes;
  RecurrentInputShapes(&input_shapes);
  CHECK_EQ(2, input_shapes.size());

  LayerParameter* input_layer_param = net_param->add_layer();
  input_layer_param->set_type("Input");
  InputParameter* input_param = input_layer_param->mutable_input_param();

  input_layer_param->add_top("c_0");
  input_param->add_shape()->CopyFrom(input_shapes[0]);

  input_layer_param->add_top("h_0");
  input_param->add_shape()->CopyFrom(input_shapes[1]);

  LayerParameter* cont_slice_param = net_param->add_layer();
  cont_slice_param->CopyFrom(slice_param);
  cont_slice_param->set_name("cont_slice");
  cont_slice_param->add_bottom("cont");
  cont_slice_param->mutable_slice_param()->set_axis(0);

  // Add layer to transform all timesteps of x to the hidden state dimension.
  //     W_xc_x = W_xc * x + b_c
  {
    LayerParameter* x_transform_param = net_param->add_layer();
    x_transform_param->CopyFrom(biased_hidden_param);
    x_transform_param->set_name("x_transform");
    x_transform_param->add_param()->set_name("W_xc");
    x_transform_param->add_param()->set_name("b_c");
    x_transform_param->add_bottom("x");
    x_transform_param->add_top("W_xc_x");
    x_transform_param->add_propagate_down(true);
  }

  if (this->static_input_) {
    // Add layer to transform x_static to the gate dimension.
    //     W_xc_x_static = W_xc_static * x_static
    LayerParameter* x_static_transform_param = net_param->add_layer();
    x_static_transform_param->CopyFrom(hidden_param);
    x_static_transform_param->mutable_inner_product_param()->set_axis(1);
    x_static_transform_param->set_name("W_xc_x_static");
    x_static_transform_param->add_param()->set_name("W_xc_static");
    x_static_transform_param->add_bottom("x_static");
    x_static_transform_param->add_top("W_xc_x_static_preshape");
    x_static_transform_param->add_propagate_down(true);

    LayerParameter* reshape_param = net_param->add_layer();
    reshape_param->set_type("Reshape");
    BlobShape* new_shape =
         reshape_param->mutable_reshape_param()->mutable_shape();
    new_shape->add_dim(1);  // One timestep.
    // Should infer this->N as the dimension so we can reshape on batch size.
    new_shape->add_dim(-1);
    new_shape->add_dim(
        x_static_transform_param->inner_product_param().num_output());
    reshape_param->set_name("W_xc_x_static_reshape");
    reshape_param->add_bottom("W_xc_x_static_preshape");
    reshape_param->add_top("W_xc_x_static");
  }

  LayerParameter* x_slice_param = net_param->add_layer();
  x_slice_param->CopyFrom(slice_param);
  x_slice_param->add_bottom("W_xc_x");
  x_slice_param->set_name("W_xc_x_slice");

  LayerParameter output_concat_layer;
  output_concat_layer.set_name("h_concat");
  output_concat_layer.set_type("Concat");
  output_concat_layer.add_top("h");
  output_concat_layer.mutable_concat_param()->set_axis(0);

  for (int t = 1; t <= this->T_; ++t) {
    string tm1s = format_int(t - 1);
    string ts = format_int(t);

    cont_slice_param->add_top("cont_" + ts);
    x_slice_param->add_top("W_xc_x_" + ts);

    // Add layers to flush the hidden state when beginning a new
    // sequence, as indicated by cont_t.
    //     h_conted_{t-1} := cont_t * h_{t-1}
    //
    // Normally, cont_t is binary (i.e., 0 or 1), so:
    //     h_conted_{t-1} := h_{t-1} if cont_t == 1
    //                       0   otherwise
    {
      LayerParameter* cont_h_param = net_param->add_layer();
      cont_h_param->CopyFrom(scale_param);
      cont_h_param->set_name("h_conted_" + tm1s);
      cont_h_param->add_bottom("h_" + tm1s);
      cont_h_param->add_bottom("cont_" + ts);
      cont_h_param->add_top("h_conted_" + tm1s);
    }

    // Add layer to compute
    //     W_hc_h_{t-1} := W_hc * h_conted_{t-1}
    {
      LayerParameter* w_param = net_param->add_layer();
      w_param->CopyFrom(hidden_param);
      w_param->set_name("transform_" + ts);
      w_param->add_param()->set_name("W_hc");
      w_param->add_bottom("h_conted_" + tm1s);
      w_param->add_top("W_hc_h_" + tm1s);
      w_param->mutable_inner_product_param()->set_axis(2);
    }

    // Add the outputs of the linear transformations to compute the gate input.
    //     gate_input_t := W_hc * h_conted_{t-1} + W_xc * x_t + b_c
    //                   = W_hc_h_{t-1} + W_xc_x_t + b_c
    {
      LayerParameter* input_sum_layer = net_param->add_layer();
      input_sum_layer->CopyFrom(sum_param);
      input_sum_layer->set_name("gate_input_" + ts);
      input_sum_layer->add_bottom("W_hc_h_" + tm1s);
      input_sum_layer->add_bottom("W_xc_x_" + ts);
      if (this->static_input_) {
        input_sum_layer->add_bottom("W_xc_x_static");
      }
      input_sum_layer->add_top("gate_input_" + ts);
    }

    // Add LSTMUnit layer to compute the cell & hidden vectors c_t and h_t.
    // Inputs: c_{t-1}, gate_input_t = (i_t, f_t, o_t, g_t), cont_t
    // Outputs: c_t, h_t
    //     [ i_t' ]
    //     [ f_t' ] := gate_input_t
    //     [ o_t' ]
    //     [ g_t' ]
    //         i_t := \sigmoid[i_t']
    //         f_t := \sigmoid[f_t']
    //         o_t := \sigmoid[o_t']
    //         g_t := \tanh[g_t']
    //         c_t := cont_t * (f_t .* c_{t-1}) + (i_t .* g_t)
    //         h_t := o_t .* \tanh[c_t]
    {
      LayerParameter* lstm_unit_param = net_param->add_layer();
      lstm_unit_param->set_type("LSTMUnit");
      lstm_unit_param->add_bottom("c_" + tm1s);
      lstm_unit_param->add_bottom("gate_input_" + ts);
      lstm_unit_param->add_bottom("cont_" + ts);
      lstm_unit_param->add_top("c_" + ts);
      lstm_unit_param->add_top("h_" + ts);
      lstm_unit_param->set_name("unit_" + ts);
    }
    output_concat_layer.add_bottom("h_" + ts);
  }  // for (int t = 1; t <= this->T_; ++t)

  {
    LayerParameter* c_T_copy_param = net_param->add_layer();
    c_T_copy_param->CopyFrom(split_param);
    c_T_copy_param->add_bottom("c_" + format_int(this->T_));
    c_T_copy_param->add_top("c_T");
  }
  net_param->add_layer()->CopyFrom(output_concat_layer);
}

INSTANTIATE_CLASS(LSTMLayer);
REGISTER_LAYER_CLASS(LSTM);

}  // namespace caffe