summaryrefslogtreecommitdiff
path: root/decoder/incremental.cc
blob: 85647a444c800dbbe9fd604e73f97b88587b3cd6 (plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
#include "incremental.h"

#include "hg.h"
#include "fdict.h"
#include "tdict.h"

#include "lm/enumerate_vocab.hh"
#include "lm/model.hh"
#include "search/applied.hh"
#include "search/config.hh"
#include "search/context.hh"
#include "search/edge.hh"
#include "search/edge_generator.hh"
#include "search/rule.hh"
#include "search/vertex.hh"
#include "search/vertex_generator.hh"
#include "util/exception.hh"

#include <boost/scoped_ptr.hpp>
#include <boost/scoped_array.hpp>

#include <iostream>
#include <vector>

namespace {

struct MapVocab : public lm::EnumerateVocab {
  public:
    MapVocab() {}

    // Do not call after Lookup.  
    void Add(lm::WordIndex index, const StringPiece &str) {
      const WordID cdec_id = TD::Convert(str.as_string());
      if (cdec_id >= out_.size()) out_.resize(cdec_id + 1);
      out_[cdec_id] = index;
    }

    // Assumes Add has been called and will never be called again.  
    lm::WordIndex FromCDec(WordID id) const {
      return out_[out_.size() > id ? id : 0];
    }

  private:
    std::vector<lm::WordIndex> out_;
};

template <class Model> class Incremental : public IncrementalBase {
  public:
    Incremental(const char *model_file, const std::vector<weight_t> &weights) :
      IncrementalBase(weights), 
      m_(model_file, GetConfig()),
      lm_(weights[FD::Convert("KLanguageModel")]),
      oov_(weights[FD::Convert("KLanguageModel_OOV")]),
      word_penalty_(weights[FD::Convert("WordPenalty")]) {
      std::cerr << "Weights KLanguageModel " << lm_ << " KLanguageModel_OOV " << oov_ << " WordPenalty " << word_penalty_ << std::endl;
    }

    void Search(unsigned int pop_limit, const Hypergraph &hg) const;

  private:
    void ConvertEdge(const search::Context<Model> &context, search::Vertex *vertices, const Hypergraph::Edge &in, search::EdgeGenerator &gen) const;

    lm::ngram::Config GetConfig() {
      lm::ngram::Config ret;
      ret.enumerate_vocab = &vocab_;
      return ret;
    }

    MapVocab vocab_;

    const Model m_;

    const float lm_, oov_, word_penalty_;
};

void PrintApplied(const Hypergraph &hg, const search::Applied final) {
  const std::vector<WordID> &words = static_cast<const Hypergraph::Edge*>(final.GetNote().vp)->rule_->e();
  const search::Applied *child(final.Children());
  for (std::vector<WordID>::const_iterator i = words.begin(); i != words.end(); ++i) {
    if (*i > 0) {
      std::cout << TD::Convert(*i) << ' ';
    } else {
      PrintApplied(hg, *child++);
    }
  }
}

template <class Model> void Incremental<Model>::Search(unsigned int pop_limit, const Hypergraph &hg) const {
  boost::scoped_array<search::Vertex> out_vertices(new search::Vertex[hg.nodes_.size()]);
  search::Config config(lm_, pop_limit, search::NBestConfig(1));
  search::Context<Model> context(config, m_);
  search::SingleBest best;

  for (unsigned int i = 0; i < hg.nodes_.size() - 1; ++i) {
    search::EdgeGenerator gen;
    const Hypergraph::EdgesVector &down_edges = hg.nodes_[i].in_edges_;
    for (unsigned int j = 0; j < down_edges.size(); ++j) {
      unsigned int edge_index = down_edges[j];
      ConvertEdge(context, out_vertices.get(), hg.edges_[edge_index], gen);
    }
    search::VertexGenerator<search::SingleBest> vertex_gen(context, out_vertices[i], best);
    gen.Search(context, vertex_gen);
  }
  const search::Applied top = out_vertices[hg.nodes_.size() - 2].BestChild();
  if (!top.Valid()) {
    std::cout << "NO PATH FOUND" << std::endl;
  } else {
    PrintApplied(hg, top);
    std::cout << "||| " << top.GetScore() << std::endl;
  }
}

template <class Model> void Incremental<Model>::ConvertEdge(const search::Context<Model> &context, search::Vertex *vertices, const Hypergraph::Edge &in, search::EdgeGenerator &gen) const {
  const std::vector<WordID> &e = in.rule_->e();
  std::vector<lm::WordIndex> words;
  words.reserve(e.size());
  std::vector<search::PartialVertex> nts;
  unsigned int terminals = 0;
  float score = 0.0;
  for (std::vector<WordID>::const_iterator word = e.begin(); word != e.end(); ++word) {
    if (*word <= 0) {
      nts.push_back(vertices[in.tail_nodes_[-*word]].RootPartial());
      if (nts.back().Empty()) return;
      score += nts.back().Bound();
      words.push_back(lm::kMaxWordIndex);
    } else {
      ++terminals;
      words.push_back(vocab_.FromCDec(*word));
    }
  }

  search::PartialEdge out(gen.AllocateEdge(nts.size()));

  memcpy(out.NT(), &nts[0], sizeof(search::PartialVertex) * nts.size());

  search::Note note;
  note.vp = &in;
  out.SetNote(note);

  score += in.rule_->GetFeatureValues().dot(cdec_weights_);
  score -= static_cast<float>(terminals) * word_penalty_ / M_LN10;
  search::ScoreRuleRet res(search::ScoreRule(context.LanguageModel(), words, out.Between()));
  score += res.prob * lm_ + static_cast<float>(res.oov) * oov_;

  out.SetScore(score);

  gen.AddEdge(out);
}

} // namespace

IncrementalBase *IncrementalBase::Load(const char *model_file, const std::vector<weight_t> &weights) {
  lm::ngram::ModelType model_type;
  if (!lm::ngram::RecognizeBinary(model_file, model_type)) model_type = lm::ngram::PROBING;
  switch (model_type) {
    case lm::ngram::PROBING:
      return new Incremental<lm::ngram::ProbingModel>(model_file, weights);
    case lm::ngram::REST_PROBING:
      return new Incremental<lm::ngram::RestProbingModel>(model_file, weights);
    default:
      UTIL_THROW(util::Exception, "Sorry this lm type isn't supported yet.");
  }
}

IncrementalBase::~IncrementalBase() {}

IncrementalBase::IncrementalBase(const std::vector<weight_t> &weights) : cdec_weights_(weights) {}