incorporate kenneth's fixes

1 files changed, 0 insertions, 464 deletions

diff --git a/decoder/ff_klm.cc b/decoder/ff_klm.cc
index 3c941fbf..ed6f731e 100644
--- a/decoder/ff_klm.cc
+++ b/decoder/ff_klm.cc
@@ -12,9 +12,6 @@
 #include "lm/model.hh"
 #include "lm/enumerate_vocab.hh"
 
-#define NEW_KENLM
-#undef NEW_KENLM
-
 #include "lm/left.hh"
 
 using namespace std;
@@ -395,464 +392,3 @@ std::string  KLanguageModelFactory::usage(bool params,bool verbose) const {
   return KLanguageModel<lm::ngram::Model>::usage(params, verbose);
 }
 
-#else
-
-using namespace std;
-
-static const unsigned char HAS_FULL_CONTEXT = 1;
-static const unsigned char HAS_EOS_ON_RIGHT = 2;
-static const unsigned char MASK             = 7;
-
-// -x : rules include <s> and </s>
-// -n NAME : feature id is NAME
-bool ParseLMArgs(string const& in, string* filename, string* mapfile, bool* explicit_markers, string* featname) {
-  vector<string> const& argv=SplitOnWhitespace(in);
-  *explicit_markers = false;
-  *featname="LanguageModel";
-  *mapfile = "";
-#define LMSPEC_NEXTARG if (i==argv.end()) {            \
-    cerr << "Missing argument for "<<*last<<". "; goto usage; \
-    } else { ++i; }
-
-  for (vector<string>::const_iterator last,i=argv.begin(),e=argv.end();i!=e;++i) {
-    string const& s=*i;
-    if (s[0]=='-') {
-      if (s.size()>2) goto fail;
-      switch (s[1]) {
-      case 'x':
-        *explicit_markers = true;
-        break;
-      case 'm':
-        LMSPEC_NEXTARG; *mapfile=*i;
-        break;
-      case 'n':
-        LMSPEC_NEXTARG; *featname=*i;
-        break;
-#undef LMSPEC_NEXTARG
-      default:
-      fail:
-        cerr<<"Unknown KLanguageModel option "<<s<<" ; ";
-        goto usage;
-      }
-    } else {
-      if (filename->empty())
-        *filename=s;
-      else {
-        cerr<<"More than one filename provided. ";
-        goto usage;
-      }
-    }
-  }
-  if (!filename->empty())
-    return true;
-usage:
-  cerr << "KLanguageModel is incorrect!\n";
-  return false;
-}
-
-template <class Model>
-string KLanguageModel<Model>::usage(bool /*param*/,bool /*verbose*/) {
-  return "KLanguageModel";
-}
-
-struct VMapper : public lm::ngram::EnumerateVocab {
-  VMapper(vector<lm::WordIndex>* out) : out_(out), kLM_UNKNOWN_TOKEN(0) { out_->clear(); }
-  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, kLM_UNKNOWN_TOKEN);
-    (*out_)[cdec_id] = index;
-  }
-  vector<lm::WordIndex>* out_;
-  const lm::WordIndex kLM_UNKNOWN_TOKEN;
-};
-
-template <class Model>
-class KLanguageModelImpl {
-
-  // returns the number of unscored words at the left edge of a span
-  inline int UnscoredSize(const void* state) const {
-    return *(static_cast<const char*>(state) + unscored_size_offset_);
-  }
-
-  inline void SetUnscoredSize(int size, void* state) const {
-    *(static_cast<char*>(state) + unscored_size_offset_) = size;
-  }
-
-  static inline const lm::ngram::State& RemnantLMState(const void* state) {
-    return *static_cast<const lm::ngram::State*>(state);
-  }
-
-  inline void SetRemnantLMState(const lm::ngram::State& lmstate, void* state) const {
-    // if we were clever, we could use the memory pointed to by state to do all
-    // the work, avoiding this copy
-    memcpy(state, &lmstate, ngram_->StateSize());
-  }
-
-  lm::WordIndex IthUnscoredWord(int i, const void* state) const {
-    const lm::WordIndex* const mem = reinterpret_cast<const lm::WordIndex*>(static_cast<const char*>(state) + unscored_words_offset_);
-    return mem[i];
-  }
-
-  void SetIthUnscoredWord(int i, lm::WordIndex index, void *state) const {
-    lm::WordIndex* mem = reinterpret_cast<lm::WordIndex*>(static_cast<char*>(state) + unscored_words_offset_);
-    mem[i] = index;
-  }
-
-  inline bool GetFlag(const void *state, unsigned char flag) const {
-    return (*(static_cast<const char*>(state) + is_complete_offset_) & flag);
-  }
-
-  inline void SetFlag(bool on, unsigned char flag, void *state) const {
-    if (on) {
-      *(static_cast<char*>(state) + is_complete_offset_) |= flag;
-    } else {
-      *(static_cast<char*>(state) + is_complete_offset_) &= (MASK ^ flag);
-    }
-  }
-
-  inline bool HasFullContext(const void *state) const {
-    return GetFlag(state, HAS_FULL_CONTEXT);
-  }
-
-  inline void SetHasFullContext(bool flag, void *state) const {
-    SetFlag(flag, HAS_FULL_CONTEXT, state);
-  }
-
- public:
-  double LookupWords(const TRule& rule, const vector<const void*>& ant_states, double* pest_sum, double* oovs, double* est_oovs, void* remnant) {
-    double sum = 0.0;
-    double est_sum = 0.0;
-    int num_scored = 0;
-    int num_estimated = 0;
-    if (oovs) *oovs = 0;
-    if (est_oovs) *est_oovs = 0;
-    bool saw_eos = false;
-    bool has_some_history = false;
-    lm::ngram::State state = ngram_->NullContextState();
-    const vector<WordID>& e = rule.e();
-    bool context_complete = false;
-    for (int j = 0; j < e.size(); ++j) {
-      if (e[j] < 1) {   // handle non-terminal substitution
-        const void* astate = (ant_states[-e[j]]);
-        int unscored_ant_len = UnscoredSize(astate);
-        for (int k = 0; k < unscored_ant_len; ++k) {
-          const lm::WordIndex cur_word = IthUnscoredWord(k, astate);
-          const bool is_oov = (cur_word == 0);
-          double p = 0;
-          if (cur_word == kSOS_) {
-            state = ngram_->BeginSentenceState();
-            if (has_some_history) {  // this is immediately fully scored, and bad
-              p = -100;
-              context_complete = true;
-            } else {  // this might be a real <s>
-              num_scored = max(0, order_ - 2);
-            }
-          } else {
-            const lm::ngram::State scopy(state);
-            p = ngram_->Score(scopy, cur_word, state);
-            if (saw_eos) { p = -100; }
-            saw_eos = (cur_word == kEOS_);
-          }
-          has_some_history = true;
-          ++num_scored;
-          if (!context_complete) {
-            if (num_scored >= order_) context_complete = true;
-          }
-          if (context_complete) {
-            sum += p;
-            if (oovs && is_oov) (*oovs)++;
-          } else {
-            if (remnant)
-              SetIthUnscoredWord(num_estimated, cur_word, remnant);
-            ++num_estimated;
-            est_sum += p;
-            if (est_oovs && is_oov) (*est_oovs)++;
-          }
-        }
-        saw_eos = GetFlag(astate, HAS_EOS_ON_RIGHT);
-        if (HasFullContext(astate)) { // this is equivalent to the "star" in Chiang 2007
-          state = RemnantLMState(astate);
-          context_complete = true;
-        }
-      } else {   // handle terminal
-        const WordID cdec_word_or_class = ClassifyWordIfNecessary(e[j]);  // in future,
-                                                                          // maybe handle emission
-        const lm::WordIndex cur_word = MapWord(cdec_word_or_class); // map to LM's id
-        double p = 0;
-        const bool is_oov = (cur_word == 0);
-        if (cur_word == kSOS_) {
-          state = ngram_->BeginSentenceState();
-          if (has_some_history) {  // this is immediately fully scored, and bad
-            p = -100;
-            context_complete = true;
-          } else {  // this might be a real <s>
-            num_scored = max(0, order_ - 2);
-          }
-        } else {
-          const lm::ngram::State scopy(state);
-          p = ngram_->Score(scopy, cur_word, state);
-          if (saw_eos) { p = -100; }
-          saw_eos = (cur_word == kEOS_);
-        }
-        has_some_history = true;
-        ++num_scored;
-        if (!context_complete) {
-          if (num_scored >= order_) context_complete = true;
-        }
-        if (context_complete) {
-          sum += p;
-          if (oovs && is_oov) (*oovs)++;
-        } else {
-          if (remnant)
-            SetIthUnscoredWord(num_estimated, cur_word, remnant);
-          ++num_estimated;
-          est_sum += p;
-          if (est_oovs && is_oov) (*est_oovs)++;
-        }
-      }
-    }
-    if (pest_sum) *pest_sum = est_sum;
-    if (remnant) {
-      state.ZeroRemaining();
-      SetFlag(saw_eos, HAS_EOS_ON_RIGHT, remnant);
-      SetRemnantLMState(state, remnant);
-      SetUnscoredSize(num_estimated, remnant);
-      SetHasFullContext(context_complete || (num_scored >= order_), remnant);
-    }
-    return sum;
-  }
-
-  // this assumes no target words on final unary -> goal rule.  is that ok?
-  // for <s> (n-1 left words) and (n-1 right words) </s>
-  double FinalTraversalCost(const void* state, double* oovs) {
-    if (add_sos_eos_) {  // rules do not produce <s> </s>, so do it here
-      SetRemnantLMState(ngram_->BeginSentenceState(), dummy_state_);
-      SetHasFullContext(1, dummy_state_);
-      SetUnscoredSize(0, dummy_state_);
-      dummy_ants_[1] = state;
-      *oovs = 0;
-      return LookupWords(*dummy_rule_, dummy_ants_, NULL, oovs, NULL, NULL);
-    } else {  // rules DO produce <s> ... </s>
-      double p = 0;
-      if (!GetFlag(state, HAS_EOS_ON_RIGHT)) { p -= 100; }
-      if (UnscoredSize(state) > 0) {  // are there unscored words
-        if (kSOS_ != IthUnscoredWord(0, state)) {
-          p -= 100 * UnscoredSize(state);
-        }
-      }
-      return p;
-    }
-  }
-
-  // if this is not a class-based LM, returns w untransformed,
-  // otherwise returns a word class mapping of w,
-  // returns TD::Convert("<unk>") if there is no mapping for w
-  WordID ClassifyWordIfNecessary(WordID w) const {
-    if (word2class_map_.empty()) return w;
-    if (w >= word2class_map_.size())
-      return kCDEC_UNK;
-    else
-      return word2class_map_[w];
-  }
-
-  // converts to cdec word id's to KenLM's id space, OOVs and <unk> end up at 0
-  lm::WordIndex MapWord(WordID w) const {
-    if (w >= cdec2klm_map_.size())
-      return 0;
-    else
-      return cdec2klm_map_[w];
-  }
-
- public:
-  KLanguageModelImpl(const string& filename, const string& mapfile, bool explicit_markers) :
-      kCDEC_UNK(TD::Convert("<unk>")) ,
-      add_sos_eos_(!explicit_markers) {
-    {
-      VMapper vm(&cdec2klm_map_);
-      lm::ngram::Config conf;
-      conf.enumerate_vocab = &vm;
-      ngram_ = new Model(filename.c_str(), conf);
-    }
-    order_ = ngram_->Order();
-    cerr << "Loaded " << order_ << "-gram KLM from " << filename << " (MapSize=" << cdec2klm_map_.size() << ")\n";
-    state_size_ = ngram_->StateSize() + 2 + (order_ - 1) * sizeof(lm::WordIndex);
-    unscored_size_offset_ = ngram_->StateSize();
-    is_complete_offset_ = unscored_size_offset_ + 1;
-    unscored_words_offset_ = is_complete_offset_ + 1;
-
-    // special handling of beginning / ending sentence markers
-    dummy_state_ = new char[state_size_];
-    memset(dummy_state_, 0, state_size_);
-    dummy_ants_.push_back(dummy_state_);
-    dummy_ants_.push_back(NULL);
-    dummy_rule_.reset(new TRule("[DUMMY] ||| [BOS] [DUMMY] ||| [1] [2] </s> ||| X=0"));
-    kSOS_ = MapWord(TD::Convert("<s>"));
-    assert(kSOS_ > 0);
-    kEOS_ = MapWord(TD::Convert("</s>"));
-    assert(kEOS_ > 0);
-    assert(MapWord(kCDEC_UNK) == 0); // KenLM invariant
-
-    // handle class-based LMs (unambiguous word->class mapping reqd.)
-    if (mapfile.size())
-      LoadWordClasses(mapfile);
-  }
-
-  void LoadWordClasses(const string& file) {
-    ReadFile rf(file);
-    istream& in = *rf.stream();
-    string line;
-    vector<WordID> dummy;
-    int lc = 0;
-    cerr << "  Loading word classes from " << file << " ...\n";
-    AddWordToClassMapping_(TD::Convert("<s>"), TD::Convert("<s>"));
-    AddWordToClassMapping_(TD::Convert("</s>"), TD::Convert("</s>"));
-    while(in) {
-      getline(in, line);
-      if (!in) continue;
-      dummy.clear();
-      TD::ConvertSentence(line, &dummy);
-      ++lc;
-      if (dummy.size() != 2) {
-        cerr << "    Format error in " << file << ", line " << lc << ": " << line << endl;
-        abort();
-      }
-      AddWordToClassMapping_(dummy[0], dummy[1]);
-    }
-  }
-
-  void AddWordToClassMapping_(WordID word, WordID cls) {
-    if (word2class_map_.size() <= word) {
-      word2class_map_.resize((word + 10) * 1.1, kCDEC_UNK);
-      assert(word2class_map_.size() > word);
-    }
-    if(word2class_map_[word] != kCDEC_UNK) {
-      cerr << "Multiple classes for symbol " << TD::Convert(word) << endl;
-      abort();
-    }
-    word2class_map_[word] = cls;
-  }
-
-  ~KLanguageModelImpl() {
-    delete ngram_;
-    delete[] dummy_state_;
-  }
-
-  int ReserveStateSize() const { return state_size_; }
-
- private:
-  const WordID kCDEC_UNK;
-  lm::WordIndex kSOS_;  // <s> - requires special handling.
-  lm::WordIndex kEOS_;  // </s>
-  Model* ngram_;
-  const bool add_sos_eos_; // flag indicating whether the hypergraph produces <s> and </s>
-                     // if this is true, FinalTransitionFeatures will "add" <s> and </s>
-                     // if false, FinalTransitionFeatures will score anything with the
-                     // markers in the right place (i.e., the beginning and end of
-                     // the sentence) with 0, and anything else with -100
-
-  int order_;
-  int state_size_;
-  int unscored_size_offset_;
-  int is_complete_offset_;
-  int unscored_words_offset_;
-  char* dummy_state_;
-  vector<const void*> dummy_ants_;
-  vector<lm::WordIndex> cdec2klm_map_;
-  vector<WordID> word2class_map_;        // if this is a class-based LM, this is the word->class mapping
-  TRulePtr dummy_rule_;
-};
-
-template <class Model>
-KLanguageModel<Model>::KLanguageModel(const string& param) {
-  string filename, mapfile, featname;
-  bool explicit_markers;
-  if (!ParseLMArgs(param, &filename, &mapfile, &explicit_markers, &featname)) {
-    abort();
-  }
-  try {
-    pimpl_ = new KLanguageModelImpl<Model>(filename, mapfile, explicit_markers);
-  } catch (std::exception &e) {
-    std::cerr << e.what() << std::endl;
-    abort();
-  }
-  fid_ = FD::Convert(featname);
-  oov_fid_ = FD::Convert(featname+"_OOV");
-  cerr << "FID: " << oov_fid_ << endl;
-  SetStateSize(pimpl_->ReserveStateSize());
-}
-
-template <class Model>
-Features KLanguageModel<Model>::features() const {
-  return single_feature(fid_);
-}
-
-template <class Model>
-KLanguageModel<Model>::~KLanguageModel() {
-  delete pimpl_;
-}
-
-template <class Model>
-void KLanguageModel<Model>::TraversalFeaturesImpl(const SentenceMetadata& /* smeta */,
-                                          const Hypergraph::Edge& edge,
-                                          const vector<const void*>& ant_states,
-                                          SparseVector<double>* features,
-                                          SparseVector<double>* estimated_features,
-                                          void* state) const {
-  double est = 0;
-  double oovs = 0;
-  double est_oovs = 0;
-  features->set_value(fid_, pimpl_->LookupWords(*edge.rule_, ant_states, &est, &oovs, &est_oovs, state));
-  estimated_features->set_value(fid_, est);
-  if (oov_fid_) {
-    if (oovs) features->set_value(oov_fid_, oovs);
-    if (est_oovs) estimated_features->set_value(oov_fid_, est_oovs);
-  }
-}
-
-template <class Model>
-void KLanguageModel<Model>::FinalTraversalFeatures(const void* ant_state,
-                                           SparseVector<double>* features) const {
-  double oovs = 0;
-  double lm = pimpl_->FinalTraversalCost(ant_state, &oovs);
-  features->set_value(fid_, lm);
-  if (oov_fid_ && oovs)
-    features->set_value(oov_fid_, oovs);
-}
-
-template <class Model> boost::shared_ptr<FeatureFunction> CreateModel(const std::string &param) {
-  KLanguageModel<Model> *ret = new KLanguageModel<Model>(param);
-  ret->Init();
-  return boost::shared_ptr<FeatureFunction>(ret);
-}
-
-boost::shared_ptr<FeatureFunction> KLanguageModelFactory::Create(std::string param) const {
-  using namespace lm::ngram;
-  std::string filename, ignored_map;
-  bool ignored_markers;
-  std::string ignored_featname;
-  ParseLMArgs(param, &filename, &ignored_map, &ignored_markers, &ignored_featname);
-  ModelType m;
-  if (!RecognizeBinary(filename.c_str(), m)) m = HASH_PROBING;
-
-  switch (m) {
-    case HASH_PROBING:
-      return CreateModel<ProbingModel>(param);
-    case TRIE_SORTED:
-      return CreateModel<TrieModel>(param);
-    case ARRAY_TRIE_SORTED:
-      return CreateModel<ArrayTrieModel>(param);
-    case QUANT_TRIE_SORTED:
-      return CreateModel<QuantTrieModel>(param);
-    case QUANT_ARRAY_TRIE_SORTED:
-      return CreateModel<QuantArrayTrieModel>(param);
-    default:
-      UTIL_THROW(util::Exception, "Unrecognized kenlm binary file type " << (unsigned)m);
-  }
-}
-
-std::string  KLanguageModelFactory::usage(bool params,bool verbose) const {
-  return KLanguageModel<lm::ngram::Model>::usage(params, verbose);
-}
-
-#endif