support different types in kenlm

1 files changed, 27 insertions, 17 deletions

diff --git a/decoder/ff_klm.cc b/decoder/ff_klm.cc
index 092c07b0..5049f156 100644
--- a/decoder/ff_klm.cc
+++ b/decoder/ff_klm.cc
@@ -4,12 +4,12 @@
 
 #include "hg.h"
 #include "tdict.h"
-#include "lm/model.hh"
 #include "lm/enumerate_vocab.hh"
 
 using namespace std;
 
-string KLanguageModel::usage(bool param,bool verbose) {
+template <class Model>
+string KLanguageModel<Model>::usage(bool /*param*/,bool /*verbose*/) {
   return "KLanguageModel";
 }
 
@@ -25,6 +25,7 @@ struct VMapper : public lm::ngram::EnumerateVocab {
   const lm::WordIndex kLM_UNKNOWN_TOKEN;
 };
 
+template <class Model>
 class KLanguageModelImpl {
 
   // returns the number of unscored words at the left edge of a span
@@ -36,11 +37,11 @@ class KLanguageModelImpl {
     *(static_cast<char*>(state) + unscored_size_offset_) = size;
   }
 
-  static inline const lm::ngram::Model::State& RemnantLMState(const void* state) {
-    return *static_cast<const lm::ngram::Model::State*>(state);
+  static inline const lm::ngram::State& RemnantLMState(const void* state) {
+    return *static_cast<const lm::ngram::State*>(state);
   }
 
-  inline void SetRemnantLMState(const lm::ngram::Model::State& lmstate, void* state) const {
+  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());
@@ -68,10 +69,9 @@ class KLanguageModelImpl {
   double LookupWords(const TRule& rule, const vector<const void*>& ant_states, double* pest_sum, void* remnant) {
     double sum = 0.0;
     double est_sum = 0.0;
-    int len = rule.ELength() - rule.Arity();
     int num_scored = 0;
     int num_estimated = 0;
-    lm::ngram::Model::State state = ngram_->NullContextState();
+    lm::ngram::State state = ngram_->NullContextState();
     const vector<WordID>& e = rule.e();
     bool context_complete = false;
     for (int j = 0; j < e.size(); ++j) {
@@ -80,7 +80,7 @@ class KLanguageModelImpl {
         int unscored_ant_len = UnscoredSize(astate);
         for (int k = 0; k < unscored_ant_len; ++k) {
           const lm::WordIndex cur_word = IthUnscoredWord(k, astate);
-          const lm::ngram::Model::State scopy(state);
+          const lm::ngram::State scopy(state);
           const double p = ngram_->Score(scopy, cur_word, state);
           ++num_scored;
           if (!context_complete) {
@@ -101,7 +101,7 @@ class KLanguageModelImpl {
         }
       } else {
         const lm::WordIndex cur_word = MapWord(e[j]);
-        const lm::ngram::Model::State scopy(state);
+        const lm::ngram::State scopy(state);
         const double p = ngram_->Score(scopy, cur_word, state);
         ++num_scored;
         if (!context_complete) {
@@ -149,7 +149,7 @@ class KLanguageModelImpl {
     lm::ngram::Config conf;
     VMapper vm(&map_);
     conf.enumerate_vocab = &vm; 
-    ngram_ = new lm::ngram::Model(param.c_str(), conf);
+    ngram_ = new Model(param.c_str(), conf);
     order_ = ngram_->Order();
     cerr << "Loaded " << order_ << "-gram KLM from " << param << " (MapSize=" << map_.size() << ")\n";
     state_size_ = ngram_->StateSize() + 2 + (order_ - 1) * sizeof(lm::WordIndex);
@@ -172,7 +172,7 @@ class KLanguageModelImpl {
   int ReserveStateSize() const { return state_size_; }
 
  private:
-  lm::ngram::Model* ngram_;
+  Model* ngram_;
   int order_;
   int state_size_;
   int unscored_size_offset_;
@@ -184,21 +184,25 @@ class KLanguageModelImpl {
   TRulePtr dummy_rule_;
 };
 
-KLanguageModel::KLanguageModel(const string& param) {
-  pimpl_ = new KLanguageModelImpl(param);
+template <class Model>
+KLanguageModel<Model>::KLanguageModel(const string& param) {
+  pimpl_ = new KLanguageModelImpl<Model>(param);
   fid_ = FD::Convert("LanguageModel");
   SetStateSize(pimpl_->ReserveStateSize());
 }
 
-Features KLanguageModel::features() const {
+template <class Model>
+Features KLanguageModel<Model>::features() const {
   return single_feature(fid_);
 }
 
-KLanguageModel::~KLanguageModel() {
+template <class Model>
+KLanguageModel<Model>::~KLanguageModel() {
   delete pimpl_;
 }
 
-void KLanguageModel::TraversalFeaturesImpl(const SentenceMetadata& /* smeta */,
+template <class Model>
+void KLanguageModel<Model>::TraversalFeaturesImpl(const SentenceMetadata& /* smeta */,
                                           const Hypergraph::Edge& edge,
                                           const vector<const void*>& ant_states,
                                           SparseVector<double>* features,
@@ -209,8 +213,14 @@ void KLanguageModel::TraversalFeaturesImpl(const SentenceMetadata& /* smeta */,
   estimated_features->set_value(fid_, est);
 }
 
-void KLanguageModel::FinalTraversalFeatures(const void* ant_state,
+template <class Model>
+void KLanguageModel<Model>::FinalTraversalFeatures(const void* ant_state,
                                            SparseVector<double>* features) const {
   features->set_value(fid_, pimpl_->FinalTraversalCost(ant_state));
 }
 
+// instantiate templates
+template class KLanguageModel<lm::ngram::ProbingModel>;
+template class KLanguageModel<lm::ngram::SortedModel>;
+template class KLanguageModel<lm::ngram::TrieModel>;
+