path: root/moses/LM/Ken.cpp

// $Id$

/***********************************************************************
Moses - factored phrase-based language decoder
Copyright (C) 2006 University of Edinburgh

This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.

This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
Lesser General Public License for more details.

You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301  USA
***********************************************************************/

#include <cstring>
#include <iostream>
#include <memory>
#include <stdlib.h>
#include <boost/shared_ptr.hpp>

#include "lm/binary_format.hh"
#include "lm/enumerate_vocab.hh"
#include "lm/left.hh"
#include "lm/model.hh"
#include "util/exception.hh"

#include "Ken.h"
#include "Base.h"
#include "moses/FF/FFState.h"
#include "moses/TypeDef.h"
#include "moses/Util.h"
#include "moses/FactorCollection.h"
#include "moses/Phrase.h"
#include "moses/InputFileStream.h"
#include "moses/StaticData.h"
#include "moses/ChartHypothesis.h"
#include "moses/Incremental.h"
#include "moses/Syntax/SVertex.h"

using namespace std;

namespace Moses
{
namespace
{

struct KenLMState : public FFState {
  lm::ngram::State state;
  int Compare(const FFState &o) const {
    const KenLMState &other = static_cast<const KenLMState &>(o);
    if (state.length < other.state.length) return -1;
    if (state.length > other.state.length) return 1;
    return std::memcmp(state.words, other.state.words, sizeof(lm::WordIndex) * state.length);
  }
};

///*
// * An implementation of single factor LM using Ken's code.
// */
//template <class Model> class LanguageModelKen : public LanguageModel
//{
//public:
//  LanguageModelKen(const std::string &line, const std::string &file, FactorType factorType, bool lazy);
//
//  const FFState *EmptyHypothesisState(const InputType &/*input*/) const {
//    KenLMState *ret = new KenLMState();
//    ret->state = m_ngram->BeginSentenceState();
//    return ret;
//  }
//
//  void CalcScore(const Phrase &phrase, float &fullScore, float &ngramScore, size_t &oovCount) const;
//
//  FFState *Evaluate(const Hypothesis &hypo, const FFState *ps, ScoreComponentCollection *out) const;
//
//  FFState *EvaluateWhenApplied(const ChartHypothesis& cur_hypo, int featureID, ScoreComponentCollection *accumulator) const;
//
//  void IncrementalCallback(Incremental::Manager &manager) const {
//    manager.LMCallback(*m_ngram, m_lmIdLookup);
//  }
//
//  bool IsUseable(const FactorMask &mask) const;
//private:
//  LanguageModelKen(const LanguageModelKen<Model> &copy_from);
//
//  lm::WordIndex TranslateID(const Word &word) const {
//    std::size_t factor = word.GetFactor(m_factorType)->GetId();
//    return (factor >= m_lmIdLookup.size() ? 0 : m_lmIdLookup[factor]);
//  }
//
//  // Convert last words of hypothesis into vocab ids, returning an end pointer.
//  lm::WordIndex *LastIDs(const Hypothesis &hypo, lm::WordIndex *indices) const {
//    lm::WordIndex *index = indices;
//    lm::WordIndex *end = indices + m_ngram->Order() - 1;
//    int position = hypo.GetCurrTargetWordsRange().GetEndPos();
//    for (; ; ++index, --position) {
//      if (index == end) return index;
//      if (position == -1) {
//        *index = m_ngram->GetVocabulary().BeginSentence();
//        return index + 1;
//      }
//      *index = TranslateID(hypo.GetWord(position));
//    }
//  }
//
//  boost::shared_ptr<Model> m_ngram;
//
//  std::vector<lm::WordIndex> m_lmIdLookup;
//
//  FactorType m_factorType;
//
//  const Factor *m_beginSentenceFactor;
//};

class MappingBuilder : public lm::EnumerateVocab
{
public:
  MappingBuilder(FactorCollection &factorCollection, std::vector<lm::WordIndex> &mapping)
    : m_factorCollection(factorCollection), m_mapping(mapping) {}

  void Add(lm::WordIndex index, const StringPiece &str) {
    std::size_t factorId = m_factorCollection.AddFactor(str)->GetId();
    if (m_mapping.size() <= factorId) {
      // 0 is <unk> :-)
      m_mapping.resize(factorId + 1);
    }
    m_mapping[factorId] = index;
  }

private:
  FactorCollection &m_factorCollection;
  std::vector<lm::WordIndex> &m_mapping;
};

} // namespace

template <class Model> LanguageModelKen<Model>::LanguageModelKen(const std::string &line, const std::string &file, FactorType factorType, bool lazy)
  :LanguageModel(line)
  ,m_factorType(factorType)
{
  lm::ngram::Config config;
  IFVERBOSE(1) {
    config.messages = &std::cerr;
  }
  else {
    config.messages = NULL;
  }
  FactorCollection &collection = FactorCollection::Instance();
  MappingBuilder builder(collection, m_lmIdLookup);
  config.enumerate_vocab = &builder;
  config.load_method = lazy ? util::LAZY : util::POPULATE_OR_READ;

  m_ngram.reset(new Model(file.c_str(), config));

  m_beginSentenceFactor = collection.AddFactor(BOS_);
}

template <class Model> LanguageModelKen<Model>::LanguageModelKen(const LanguageModelKen<Model> &copy_from)
  :LanguageModel(copy_from.GetArgLine()),
   m_ngram(copy_from.m_ngram),
// TODO: don't copy this.
   m_lmIdLookup(copy_from.m_lmIdLookup),
   m_factorType(copy_from.m_factorType),
   m_beginSentenceFactor(copy_from.m_beginSentenceFactor)
{
}

template <class Model> const FFState * LanguageModelKen<Model>::EmptyHypothesisState(const InputType &/*input*/) const
{
  KenLMState *ret = new KenLMState();
  ret->state = m_ngram->BeginSentenceState();
  return ret;
}

template <class Model> void LanguageModelKen<Model>::CalcScore(const Phrase &phrase, float &fullScore, float &ngramScore, size_t &oovCount) const
{
  fullScore = 0;
  ngramScore = 0;
  oovCount = 0;

  if (!phrase.GetSize()) return;

  lm::ngram::ChartState discarded_sadly;
  lm::ngram::RuleScore<Model> scorer(*m_ngram, discarded_sadly);

  size_t position;
  if (m_beginSentenceFactor == phrase.GetWord(0).GetFactor(m_factorType)) {
    scorer.BeginSentence();
    position = 1;
  } else {
    position = 0;
  }

  size_t ngramBoundary = m_ngram->Order() - 1;

  size_t end_loop = std::min(ngramBoundary, phrase.GetSize());
  for (; position < end_loop; ++position) {
    const Word &word = phrase.GetWord(position);
    if (word.IsNonTerminal()) {
      fullScore += scorer.Finish();
      scorer.Reset();
    } else {
      lm::WordIndex index = TranslateID(word);
      scorer.Terminal(index);
      if (!index) ++oovCount;
    }
  }
  float before_boundary = fullScore + scorer.Finish();
  for (; position < phrase.GetSize(); ++position) {
    const Word &word = phrase.GetWord(position);
    if (word.IsNonTerminal()) {
      fullScore += scorer.Finish();
      scorer.Reset();
    } else {
      lm::WordIndex index = TranslateID(word);
      scorer.Terminal(index);
      if (!index) ++oovCount;
    }
  }
  fullScore += scorer.Finish();

  ngramScore = TransformLMScore(fullScore - before_boundary);
  fullScore = TransformLMScore(fullScore);
}

template <class Model> FFState *LanguageModelKen<Model>::EvaluateWhenApplied(const Hypothesis &hypo, const FFState *ps, ScoreComponentCollection *out) const
{
  const lm::ngram::State &in_state = static_cast<const KenLMState&>(*ps).state;

  std::auto_ptr<KenLMState> ret(new KenLMState());

  if (!hypo.GetCurrTargetLength()) {
    ret->state = in_state;
    return ret.release();
  }

  const std::size_t begin = hypo.GetCurrTargetWordsRange().GetStartPos();
  //[begin, end) in STL-like fashion.
  const std::size_t end = hypo.GetCurrTargetWordsRange().GetEndPos() + 1;
  const std::size_t adjust_end = std::min(end, begin + m_ngram->Order() - 1);

  std::size_t position = begin;
  typename Model::State aux_state;
  typename Model::State *state0 = &ret->state, *state1 = &aux_state;

  float score = m_ngram->Score(in_state, TranslateID(hypo.GetWord(position)), *state0);
  ++position;
  for (; position < adjust_end; ++position) {
    score += m_ngram->Score(*state0, TranslateID(hypo.GetWord(position)), *state1);
    std::swap(state0, state1);
  }

  if (hypo.IsSourceCompleted()) {
    // Score end of sentence.
    std::vector<lm::WordIndex> indices(m_ngram->Order() - 1);
    const lm::WordIndex *last = LastIDs(hypo, &indices.front());
    score += m_ngram->FullScoreForgotState(&indices.front(), last, m_ngram->GetVocabulary().EndSentence(), ret->state).prob;
  } else if (adjust_end < end) {
    // Get state after adding a long phrase.
    std::vector<lm::WordIndex> indices(m_ngram->Order() - 1);
    const lm::WordIndex *last = LastIDs(hypo, &indices.front());
    m_ngram->GetState(&indices.front(), last, ret->state);
  } else if (state0 != &ret->state) {
    // Short enough phrase that we can just reuse the state.
    ret->state = *state0;
  }

  score = TransformLMScore(score);

  if (OOVFeatureEnabled()) {
    std::vector<float> scores(2);
    scores[0] = score;
    scores[1] = 0.0;
    out->PlusEquals(this, scores);
  } else {
    out->PlusEquals(this, score);
  }

  return ret.release();
}

class LanguageModelChartStateKenLM : public FFState
{
public:
  LanguageModelChartStateKenLM() {}

  const lm::ngram::ChartState &GetChartState() const {
    return m_state;
  }
  lm::ngram::ChartState &GetChartState() {
    return m_state;
  }

  int Compare(const FFState& o) const {
    const LanguageModelChartStateKenLM &other = static_cast<const LanguageModelChartStateKenLM&>(o);
    int ret = m_state.Compare(other.m_state);
    return ret;
  }

private:
  lm::ngram::ChartState m_state;
};

template <class Model> FFState *LanguageModelKen<Model>::EvaluateWhenApplied(const ChartHypothesis& hypo, int featureID, ScoreComponentCollection *accumulator) const
{
  LanguageModelChartStateKenLM *newState = new LanguageModelChartStateKenLM();
  lm::ngram::RuleScore<Model> ruleScore(*m_ngram, newState->GetChartState());
  const TargetPhrase &target = hypo.GetCurrTargetPhrase();
  const AlignmentInfo::NonTermIndexMap &nonTermIndexMap =
    target.GetAlignNonTerm().GetNonTermIndexMap();

  const size_t size = hypo.GetCurrTargetPhrase().GetSize();
  size_t phrasePos = 0;
  // Special cases for first word.
  if (size) {
    const Word &word = hypo.GetCurrTargetPhrase().GetWord(0);
    if (word.GetFactor(m_factorType) == m_beginSentenceFactor) {
      // Begin of sentence
      ruleScore.BeginSentence();
      phrasePos++;
    } else if (word.IsNonTerminal()) {
      // Non-terminal is first so we can copy instead of rescoring.
      const ChartHypothesis *prevHypo = hypo.GetPrevHypo(nonTermIndexMap[phrasePos]);
      const lm::ngram::ChartState &prevState = static_cast<const LanguageModelChartStateKenLM*>(prevHypo->GetFFState(featureID))->GetChartState();
      ruleScore.BeginNonTerminal(prevState);
      phrasePos++;
    }
  }

  for (; phrasePos < size; phrasePos++) {
    const Word &word = hypo.GetCurrTargetPhrase().GetWord(phrasePos);
    if (word.IsNonTerminal()) {
      const ChartHypothesis *prevHypo = hypo.GetPrevHypo(nonTermIndexMap[phrasePos]);
      const lm::ngram::ChartState &prevState = static_cast<const LanguageModelChartStateKenLM*>(prevHypo->GetFFState(featureID))->GetChartState();
      ruleScore.NonTerminal(prevState);
    } else {
      ruleScore.Terminal(TranslateID(word));
    }
  }

  float score = ruleScore.Finish();
  score = TransformLMScore(score);
  score -= hypo.GetTranslationOption().GetScores().GetScoresForProducer(this)[0];

  if (OOVFeatureEnabled()) {
    std::vector<float> scores(2);
    scores[0] = score;
    scores[1] = 0.0;
    accumulator->PlusEquals(this, scores);
  }
  else {
    accumulator->PlusEquals(this, score);
  }
  return newState;
}

template <class Model> FFState *LanguageModelKen<Model>::EvaluateWhenApplied(const Syntax::SHyperedge& hyperedge, int featureID, ScoreComponentCollection *accumulator) const
{
  LanguageModelChartStateKenLM *newState = new LanguageModelChartStateKenLM();
  lm::ngram::RuleScore<Model> ruleScore(*m_ngram, newState->GetChartState());
  const TargetPhrase &target = *hyperedge.translation;
  const AlignmentInfo::NonTermIndexMap &nonTermIndexMap =
    target.GetAlignNonTerm().GetNonTermIndexMap2();

  const size_t size = target.GetSize();
  size_t phrasePos = 0;
  // Special cases for first word.
  if (size) {
    const Word &word = target.GetWord(0);
    if (word.GetFactor(m_factorType) == m_beginSentenceFactor) {
      // Begin of sentence
      ruleScore.BeginSentence();
      phrasePos++;
    } else if (word.IsNonTerminal()) {
      // Non-terminal is first so we can copy instead of rescoring.
      const Syntax::SVertex *pred = hyperedge.tail[nonTermIndexMap[phrasePos]];
      const lm::ngram::ChartState &prevState = static_cast<const LanguageModelChartStateKenLM*>(pred->state[featureID])->GetChartState();
      float prob = UntransformLMScore(pred->best->scoreBreakdown.GetScoresForProducer(this)[0]);
      ruleScore.BeginNonTerminal(prevState, prob);
      phrasePos++;
    }
  }

  for (; phrasePos < size; phrasePos++) {
    const Word &word = target.GetWord(phrasePos);
    if (word.IsNonTerminal()) {
      const Syntax::SVertex *pred = hyperedge.tail[nonTermIndexMap[phrasePos]];
      const lm::ngram::ChartState &prevState = static_cast<const LanguageModelChartStateKenLM*>(pred->state[featureID])->GetChartState();
      float prob = UntransformLMScore(pred->best->scoreBreakdown.GetScoresForProducer(this)[0]);
      ruleScore.NonTerminal(prevState, prob);
    } else {
      ruleScore.Terminal(TranslateID(word));
    }
  }

  float score = ruleScore.Finish();
  score = TransformLMScore(score);
  accumulator->Assign(this, score);
  return newState;
}

template <class Model> void LanguageModelKen<Model>::IncrementalCallback(Incremental::Manager &manager) const
{
  manager.LMCallback(*m_ngram, m_lmIdLookup);
}

template <class Model> void LanguageModelKen<Model>::ReportHistoryOrder(std::ostream &out, const Phrase &phrase) const
{
  out << "|lm=(";
  if (!phrase.GetSize()) return;

  typename Model::State aux_state;
  typename Model::State start_of_sentence_state = m_ngram->BeginSentenceState();
  typename Model::State *state0 = &start_of_sentence_state;
  typename Model::State *state1 = &aux_state;

  for (std::size_t position=0; position<phrase.GetSize(); position++) {
    const lm::WordIndex idx = TranslateID(phrase.GetWord(position));
    lm::FullScoreReturn ret(m_ngram->FullScore(*state0, idx, *state1));
    if (position) out << ",";
    out << (int) ret.ngram_length << ":" << TransformLMScore(ret.prob);
    if (idx == 0) out << ":unk";
    std::swap(state0, state1);
  }
  out << ")| ";
}

template <class Model>
bool LanguageModelKen<Model>::IsUseable(const FactorMask &mask) const
{
  bool ret = mask[m_factorType];
  return ret;
}


LanguageModel *ConstructKenLM(const std::string &line)
{
  FactorType factorType = 0;
  string filePath;
  bool lazy = false;

  vector<string> toks = Tokenize(line);
  for (size_t i = 1; i < toks.size(); ++i) {
    vector<string> args = Tokenize(toks[i], "=");
    UTIL_THROW_IF2(args.size() != 2,
                   "Incorrect format of KenLM property: " << toks[i]);

    if (args[0] == "factor") {
      factorType = Scan<FactorType>(args[1]);
    } else if (args[0] == "order") {
      //nGramOrder = Scan<size_t>(args[1]);
    } else if (args[0] == "path") {
      filePath = args[1];
    } else if (args[0] == "lazyken") {
      lazy = Scan<bool>(args[1]);
    } else if (args[0] == "name") {
      // that's ok. do nothing, passes onto LM constructor
    }
  }

  return ConstructKenLM(line, filePath, factorType, lazy);
}

LanguageModel *ConstructKenLM(const std::string &line, const std::string &file, FactorType factorType, bool lazy)
{
  lm::ngram::ModelType model_type;
  if (lm::ngram::RecognizeBinary(file.c_str(), model_type)) {

    switch(model_type) {
    case lm::ngram::PROBING:
      return new LanguageModelKen<lm::ngram::ProbingModel>(line, file, factorType, lazy);
    case lm::ngram::REST_PROBING:
      return new LanguageModelKen<lm::ngram::RestProbingModel>(line, file, factorType, lazy);
    case lm::ngram::TRIE:
      return new LanguageModelKen<lm::ngram::TrieModel>(line, file, factorType, lazy);
    case lm::ngram::QUANT_TRIE:
      return new LanguageModelKen<lm::ngram::QuantTrieModel>(line, file, factorType, lazy);
    case lm::ngram::ARRAY_TRIE:
      return new LanguageModelKen<lm::ngram::ArrayTrieModel>(line, file, factorType, lazy);
    case lm::ngram::QUANT_ARRAY_TRIE:
      return new LanguageModelKen<lm::ngram::QuantArrayTrieModel>(line, file, factorType, lazy);
    default:
      UTIL_THROW2("Unrecognized kenlm model type " << model_type);
    }
  } else {
    return new LanguageModelKen<lm::ngram::ProbingModel>(line, file, factorType, lazy);
  }
}

}