merged, compiles but not working

10 files changed, 63 insertions, 188 deletions

diff --git a/.gitignore b/.gitignore
index 7e63c4ef..43b48a97 100644
--- a/.gitignore
+++ b/.gitignore
@@ -155,3 +155,6 @@ training/compute_cllh
 dtrain/dtrain
 weights.gz
 dtrain/test/eval/
+phrasinator/gibbs_train_plm_notables
+training/mpi_flex_optimize
+utils/phmt
diff --git a/decoder/ff_klm.cc b/decoder/ff_klm.cc
index 28bcb6b9..ed6f731e 100644
--- a/decoder/ff_klm.cc
+++ b/decoder/ff_klm.cc
@@ -392,22 +392,3 @@ std::string  KLanguageModelFactory::usage(bool params,bool verbose) const {
   return KLanguageModel<lm::ngram::Model>::usage(params, verbose);
 }
 
-  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);
-}
diff --git a/dtrain/dtrain.cc b/dtrain/dtrain.cc
index 0a94f7aa..e96b65aa 100644
--- a/dtrain/dtrain.cc
+++ b/dtrain/dtrain.cc
@@ -20,8 +20,8 @@ dtrain_init(int argc, char** argv, po::variables_map* cfg)
     ("stop_after",     po::value<unsigned>()->default_value(0),                              "stop after X input sentences")
     ("print_weights",  po::value<string>(),                                            "weights to print on each iteration")
     ("hstreaming",     po::value<bool>()->zero_tokens(),                                     "run in hadoop streaming mode")
-    ("learning_rate",  po::value<double>()->default_value(0.0005),                                          "learning rate")
-    ("gamma",          po::value<double>()->default_value(0),                            "gamma for SVM (0 for perceptron)")
+    ("learning_rate",  po::value<weight_t>()->default_value(0.0005),                                          "learning rate")
+    ("gamma",          po::value<weight_t>()->default_value(0),                            "gamma for SVM (0 for perceptron)")
     ("tmp",            po::value<string>()->default_value("/tmp"),                                        "temp dir to use")
     ("select_weights", po::value<string>()->default_value("last"), "output 'best' or 'last' weights ('VOID' to throw away)")
     ("noup",           po::value<bool>()->zero_tokens(),                                            "do not update weights");
@@ -134,15 +134,14 @@ main(int argc, char** argv)
   observer->SetScorer(scorer);
 
   // init weights
-  Weights weights;
-  if (cfg.count("input_weights")) weights.InitFromFile(cfg["input_weights"].as<string>());
-  SparseVector<double> lambdas;
-  weights.InitSparseVector(&lambdas);
-  vector<double> dense_weights;
+  vector<weight_t>& dense_weights = decoder.CurrentWeightVector();
+  SparseVector<weight_t> lambdas;
+  if (cfg.count("input_weights")) Weights::InitFromFile(cfg["input_weights"].as<string>(), &dense_weights);
+  Weights::InitSparseVector(dense_weights, &lambdas);
 
   // meta params for perceptron, SVM
-  double eta = cfg["learning_rate"].as<double>();
-  double gamma = cfg["gamma"].as<double>();
+  weight_t eta = cfg["learning_rate"].as<weight_t>();
+  weight_t gamma = cfg["gamma"].as<weight_t>();
   WordID __bias = FD::Convert("__bias");
   lambdas.add_value(__bias, 0);
 
@@ -160,7 +159,7 @@ main(int argc, char** argv)
   grammar_buf_out.open(grammar_buf_fn.c_str());
   
   unsigned in_sz = 999999999; // input index, input size
-  vector<pair<score_t,score_t> > all_scores;
+  vector<pair<score_t, score_t> > all_scores;
   score_t max_score = 0.;
   unsigned best_it = 0;
   float overall_time = 0.;
@@ -189,6 +188,15 @@ main(int argc, char** argv)
   }
 
 
+  //LogVal<double> a(2.2);
+  //LogVal<double> b(2.1);
+  //cout << a << endl;
+  //cout << log(a) << endl;
+  //LogVal<double> c = a - b;
+  //cout << log(c) << endl;
+  //exit(0);
+
+
   for (unsigned t = 0; t < T; t++) // T epochs
   {
 
@@ -196,7 +204,8 @@ main(int argc, char** argv)
   time(&start);
   igzstream grammar_buf_in;
   if (t > 0) grammar_buf_in.open(grammar_buf_fn.c_str());
-  score_t score_sum = 0., model_sum = 0.;
+  score_t score_sum = 0.;
+  score_t model_sum(0);
   unsigned ii = 0, nup = 0, npairs = 0;
   if (!quiet) cerr << "Iteration #" << t+1 << " of " << T << "." << endl;
 
@@ -238,10 +247,7 @@ main(int argc, char** argv)
     if (next || stop) break;
 
     // weights
-    dense_weights.clear();
-    weights.InitFromVector(lambdas);
-    weights.InitVector(&dense_weights);
-    decoder.SetWeights(dense_weights);
+    lambdas.init_vector(&dense_weights);
 
     // getting input
     vector<string> in_split; // input: sid\tsrc\tref\tpsg
@@ -289,7 +295,8 @@ main(int argc, char** argv)
     // get (scored) samples 
     vector<ScoredHyp>* samples = observer->GetSamples();
 
-    if (verbose) {
+    // FIXME
+    /*if (verbose) {
       cout << "[ref: '";
       if (t > 0) cout << ref_ids_buf[ii];
       else cout << ref_ids;
@@ -297,7 +304,15 @@ main(int argc, char** argv)
       cout << _p5 << _np << "1best: " << "'" << (*samples)[0].w << "'" << endl;
       cout << "SCORE=" << (*samples)[0].score << ",model="<< (*samples)[0].model << endl;
       cout << "F{" << (*samples)[0].f << "} ]" << endl << endl;
-    }
+    }*/
+    /*cout << lambdas.get(FD::Convert("PhraseModel_0")) << endl;
+    cout << (*samples)[0].model << endl;
+    cout << "1best: ";
+    for (unsigned u = 0; u < (*samples)[0].w.size(); u++) cout << TD::Convert((*samples)[0].w[u]) << " ";
+    cout << endl;
+    cout << (*samples)[0].f << endl;
+    cout << "___" << endl;*/
+
     score_sum += (*samples)[0].score;
     model_sum += (*samples)[0].model;
 
@@ -317,21 +332,21 @@ main(int argc, char** argv)
         if (!gamma) {
           // perceptron
           if (it->first.score - it->second.score < 0) { // rank error
-            SparseVector<double> dv = it->second.f - it->first.f;
+            SparseVector<weight_t> dv = it->second.f - it->first.f;
             dv.add_value(__bias, -1);
             lambdas.plus_eq_v_times_s(dv, eta);
             nup++;
           }
         } else {
           // SVM
-          double rank_error = it->second.score - it->first.score;
+          score_t rank_error = it->second.score - it->first.score;
           if (rank_error > 0) {
-            SparseVector<double> dv = it->second.f - it->first.f;
+            SparseVector<weight_t> dv = it->second.f - it->first.f;
             dv.add_value(__bias, -1);
             lambdas.plus_eq_v_times_s(dv, eta);
           }
           // regularization
-          double margin = it->first.model - it->second.model;
+          score_t margin = it->first.model - it->second.model;
           if (rank_error || margin < 1) {
             lambdas.plus_eq_v_times_s(lambdas, -2*gamma*eta); // reg /= #EXAMPLES or #UPDATES ?
             nup++;
@@ -339,6 +354,15 @@ main(int argc, char** argv)
         }
       }
     }
+    
+
+    vector<weight_t> x;
+    lambdas.init_vector(&x);
+    for (int q = 0; q < x.size(); q++) {
+      if (x[q] < -10 && x[q] != 0)
+        cout << FD::Convert(q) << " " << x[q] << endl; 
+    }
+    cout << " --- " << endl;
 
     ++ii;
 
@@ -358,7 +382,8 @@ main(int argc, char** argv)
   // print some stats
   score_t score_avg = score_sum/(score_t)in_sz;
   score_t model_avg = model_sum/(score_t)in_sz;
-  score_t score_diff, model_diff;
+  score_t score_diff;
+  score_t model_diff;
   if (t > 0) {
     score_diff = score_avg - all_scores[t-1].first;
     model_diff = model_avg - all_scores[t-1].second;
@@ -402,10 +427,10 @@ main(int argc, char** argv)
 
   // write weights to file
   if (select_weights == "best") {
-    weights.InitFromVector(lambdas);
     string infix = "dtrain-weights-" + boost::lexical_cast<string>(t);
+    lambdas.init_vector(&dense_weights);
     string w_fn = gettmpf(tmp_path, infix, "gz");
-    weights.WriteToFile(w_fn, true); 
+    Weights::WriteToFile(w_fn, dense_weights, true); 
     weights_files.push_back(w_fn);
   }
 
@@ -420,7 +445,7 @@ main(int argc, char** argv)
       ostream& o = *of.stream();
       o.precision(17);
       o << _np;
-      for (SparseVector<double>::const_iterator it = lambdas.begin(); it != lambdas.end(); ++it) {
+      for (SparseVector<weight_t>::const_iterator it = lambdas.begin(); it != lambdas.end(); ++it) {
 	    if (it->second == 0) continue;
         o << FD::Convert(it->first) << '\t' << it->second << endl;
       }
diff --git a/dtrain/dtrain.h b/dtrain/dtrain.h
index e98ef470..7c1509e4 100644
--- a/dtrain/dtrain.h
+++ b/dtrain/dtrain.h
@@ -11,6 +11,8 @@
 #include "ksampler.h"
 #include "pairsampling.h"
 
+#include "filelib.h"
+
 #define DTRAIN_DOTS 100 // when to display a '.'
 #define DTRAIN_GRAMMAR_DELIM "########EOS########"
 
diff --git a/dtrain/kbestget.h b/dtrain/kbestget.h
index d141da60..4aadee7a 100644
--- a/dtrain/kbestget.h
+++ b/dtrain/kbestget.h
@@ -7,6 +7,7 @@
 #include "ff_register.h"
 #include "decoder.h"
 #include "weights.h"
+#include "logval.h"
 
 using namespace std;
 
@@ -106,7 +107,8 @@ struct KBestGetter : public HypSampler
       ScoredHyp h;
       h.w = d->yield;
       h.f = d->feature_values;
-      h.model = log(d->score);
+      h.model = d->score;
+      cout << i << ". "<< h.model << endl;
       h.rank = i;
       h.score = scorer_->Score(h.w, *ref_, i);
       s_.push_back(h);
@@ -125,7 +127,7 @@ struct KBestGetter : public HypSampler
       ScoredHyp h;
       h.w = d->yield;
       h.f = d->feature_values;
-      h.model = log(d->score);
+      h.model = -1*log(d->score);
       h.rank = i;
       h.score = scorer_->Score(h.w, *ref_, i);
       s_.push_back(h);
diff --git a/dtrain/test/example/dtrain.ini b/dtrain/test/example/dtrain.ini
index 9b83193a..96bdbf8e 100644
--- a/dtrain/test/example/dtrain.ini
+++ b/dtrain/test/example/dtrain.ini
@@ -1,14 +1,14 @@
 decoder_config=test/example/cdec.ini
 k=100
 N=3
-gamma=0.00001
+gamma=0 #.00001
 epochs=2
 input=test/example/nc-1k-tabs.gz
 scorer=stupid_bleu
 output=-
-stop_after=10
+stop_after=5
 sample_from=kbest
-pair_sampling=108010
-select_weights=best
+pair_sampling=all #108010
+select_weights=VOID
 print_weights=Glue WordPenalty LanguageModel LanguageModel_OOV PhraseModel_0 PhraseModel_1 PhraseModel_2 PhraseModel_3 PhraseModel_4 PassThrough
 tmp=/tmp
diff --git a/klm/lm/binary_format.cc b/klm/lm/binary_format.cc
index eac8aa85..27cada13 100644
--- a/klm/lm/binary_format.cc
+++ b/klm/lm/binary_format.cc
@@ -182,10 +182,6 @@ void SeekPastHeader(int fd, const Parameters &params) {
   SeekOrThrow(fd, TotalHeaderSize(params.counts.size()));
 }
 
-void SeekPastHeader(int fd, const Parameters &params) {
-  SeekOrThrow(fd, TotalHeaderSize(params.counts.size()));
-}
-
 uint8_t *SetupBinary(const Config &config, const Parameters &params, std::size_t memory_size, Backing &backing) {
   const off_t file_size = util::SizeFile(backing.file.get());
   // The header is smaller than a page, so we have to map the whole header as well.  
diff --git a/klm/lm/search_trie.cc b/klm/lm/search_trie.cc
index 1bcfe27d..5d8c70db 100644
--- a/klm/lm/search_trie.cc
+++ b/klm/lm/search_trie.cc
@@ -234,19 +234,8 @@ class FindBlanks {
       return unigrams_[index].prob;
     }
 
-<<<<<<< HEAD
-// Phase to count n-grams, including blanks inserted because they were pruned but have extensions
-class JustCount {
-  public:
-    template <class Middle, class Longest> JustCount(ContextReader * /*contexts*/, UnigramValue * /*unigrams*/, Middle * /*middle*/, Longest &/*longest*/, uint64_t *counts, unsigned char order)
-      : counts_(counts), longest_counts_(counts + order - 1) {}
-
-    void Unigrams(WordIndex begin, WordIndex end) {
-      counts_[0] += end - begin;
-=======
     void Unigram(WordIndex /*index*/) {
       ++counts_[0];
->>>>>>> upstream/master
     }
 
     void MiddleBlank(const unsigned char order, const WordIndex *indices, unsigned char lower, float prob_basis) {
@@ -278,11 +267,7 @@ class JustCount {
 // Phase to actually write n-grams to the trie.  
 template <class Quant, class Bhiksha> class WriteEntries {
   public:
-<<<<<<< HEAD
-    WriteEntries(ContextReader *contexts, UnigramValue *unigrams, BitPackedMiddle<typename Quant::Middle, Bhiksha> *middle, BitPackedLongest<typename Quant::Longest> &longest, const uint64_t * /*counts*/, unsigned char order) : 
-=======
     WriteEntries(RecordReader *contexts, UnigramValue *unigrams, BitPackedMiddle<typename Quant::Middle, Bhiksha> *middle, BitPackedLongest<typename Quant::Longest> &longest, unsigned char order, SRISucks &sri) : 
->>>>>>> upstream/master
       contexts_(contexts),
       unigrams_(unigrams),
       middle_(middle),
@@ -330,16 +315,8 @@ template <class Quant, class Bhiksha> class WriteEntries {
     SRISucks &sri_;
 };
 
-<<<<<<< HEAD
-template <class Doing> class RecursiveInsert {
-  public:
-    template <class MiddleT, class LongestT> RecursiveInsert(SortedFileReader *inputs, ContextReader *contexts, UnigramValue *unigrams, MiddleT *middle, LongestT &longest, uint64_t *counts, unsigned char order) : 
-      doing_(contexts, unigrams, middle, longest, counts, order), inputs_(inputs), inputs_end_(inputs + order - 1), order_minus_2_(order - 2) {
-    }
-=======
 struct Gram {
   Gram(const WordIndex *in_begin, unsigned char order) : begin(in_begin), end(in_begin + order) {}
->>>>>>> upstream/master
 
   const WordIndex *begin, *end;
 
@@ -440,29 +417,6 @@ void SanityCheckCounts(const std::vector<uint64_t> &initial, const std::vector<u
   }
 }
 
-<<<<<<< HEAD
-bool IsDirectory(const char *path) {
-  struct stat info;
-  if (0 != stat(path, &info)) return false;
-  return S_ISDIR(info.st_mode);
-}
-
-template <class Quant> void TrainQuantizer(uint8_t order, uint64_t count, SortedFileReader &reader, util::ErsatzProgress &progress, Quant &quant) {
-  ProbBackoff weights;
-  std::vector<float> probs, backoffs;
-  probs.reserve(count);
-  backoffs.reserve(count);
-  for (reader.Rewind(); !reader.Ended(); reader.NextHeader()) {
-    uint64_t entries = reader.ReadCount();
-    for (uint64_t c = 0; c < entries; ++c) {
-      reader.ReadWord();
-      reader.ReadWeights(weights);
-      // kBlankProb isn't added yet.  
-      probs.push_back(weights.prob);
-      if (weights.backoff != 0.0) backoffs.push_back(weights.backoff);
-      ++progress;
-    }
-=======
 template <class Quant> void TrainQuantizer(uint8_t order, uint64_t count, const std::vector<float> &additional, RecordReader &reader, util::ErsatzProgress &progress, Quant &quant) {
   std::vector<float> probs(additional), backoffs;
   probs.reserve(count + additional.size());
@@ -472,26 +426,10 @@ template <class Quant> void TrainQuantizer(uint8_t order, uint64_t count, const
     probs.push_back(weights.prob);
     if (weights.backoff != 0.0) backoffs.push_back(weights.backoff);
     ++progress;
->>>>>>> upstream/master
   }
   quant.Train(order, probs, backoffs);
 }
 
-<<<<<<< HEAD
-template <class Quant> void TrainProbQuantizer(uint8_t order, uint64_t count, SortedFileReader &reader, util::ErsatzProgress &progress, Quant &quant) {
-  Prob weights;
-  std::vector<float> probs, backoffs;
-  probs.reserve(count);
-  for (reader.Rewind(); !reader.Ended(); reader.NextHeader()) {
-    uint64_t entries = reader.ReadCount();
-    for (uint64_t c = 0; c < entries; ++c) {
-      reader.ReadWord();
-      reader.ReadWeights(weights);
-      // kBlankProb isn't added yet.  
-      probs.push_back(weights.prob);
-      ++progress;
-    }
-=======
 template <class Quant> void TrainProbQuantizer(uint8_t order, uint64_t count, RecordReader &reader, util::ErsatzProgress &progress, Quant &quant) {
   std::vector<float> probs, backoffs;
   probs.reserve(count);
@@ -499,18 +437,10 @@ template <class Quant> void TrainProbQuantizer(uint8_t order, uint64_t count, Re
     const Prob &weights = *reinterpret_cast<const Prob*>(reinterpret_cast<const uint8_t*>(reader.Data()) + sizeof(WordIndex) * order);
     probs.push_back(weights.prob);
     ++progress;
->>>>>>> upstream/master
   }
   quant.TrainProb(order, probs);
 }
 
-<<<<<<< HEAD
-} // namespace
-
-template <class Quant, class Bhiksha> void BuildTrie(const std::string &file_prefix, std::vector<uint64_t> &counts, const Config &config, TrieSearch<Quant, Bhiksha> &out, Quant &quant, const SortedVocabulary &vocab, Backing &backing) {
-  std::vector<SortedFileReader> inputs(counts.size() - 1);
-  std::vector<ContextReader> contexts(counts.size() - 1);
-=======
 void PopulateUnigramWeights(FILE *file, WordIndex unigram_count, RecordReader &contexts, UnigramValue *unigrams) {
   // Fill unigram probabilities.  
   try {
@@ -533,7 +463,6 @@ void PopulateUnigramWeights(FILE *file, WordIndex unigram_count, RecordReader &c
 template <class Quant, class Bhiksha> void BuildTrie(const std::string &file_prefix, std::vector<uint64_t> &counts, const Config &config, TrieSearch<Quant, Bhiksha> &out, Quant &quant, const SortedVocabulary &vocab, Backing &backing) {
   RecordReader inputs[kMaxOrder - 1];
   RecordReader contexts[kMaxOrder - 1];
->>>>>>> upstream/master
 
   for (unsigned char i = 2; i <= counts.size(); ++i) {
     std::stringstream assembled;
@@ -548,17 +477,12 @@ template <class Quant, class Bhiksha> void BuildTrie(const std::string &file_pre
   SRISucks sri;
   std::vector<uint64_t> fixed_counts(counts.size());
   {
-<<<<<<< HEAD
-    RecursiveInsert<JustCount> counter(&*inputs.begin(), &*contexts.begin(), NULL, out.middle_begin_, out.longest, &*fixed_counts.begin(), counts.size());
-    counter.Apply(config.messages, "Counting n-grams that should not have been pruned", counts[0]);
-=======
     std::string temp(file_prefix); temp += "unigrams";
     util::scoped_fd unigram_file(util::OpenReadOrThrow(temp.c_str()));
     util::scoped_memory unigrams;
     MapRead(util::POPULATE_OR_READ, unigram_file.get(), 0, counts[0] * sizeof(ProbBackoff), unigrams);
     FindBlanks finder(&*fixed_counts.begin(), counts.size(), reinterpret_cast<const ProbBackoff*>(unigrams.get()), sri);
     RecursiveInsert(counts.size(), counts[0], inputs, config.messages, "Identifying n-grams omitted by SRI", finder);
->>>>>>> upstream/master
   }
   for (const RecordReader *i = inputs; i != inputs + counts.size() - 2; ++i) {
     if (*i) UTIL_THROW(FormatLoadException, "There's a bug in the trie implementation: the " << (i - inputs + 2) << "-gram table did not complete reading");
@@ -566,18 +490,6 @@ template <class Quant, class Bhiksha> void BuildTrie(const std::string &file_pre
   SanityCheckCounts(counts, fixed_counts);
   counts = fixed_counts;
 
-<<<<<<< HEAD
-  out.SetupMemory(GrowForSearch(config, vocab.UnkCountChangePadding(), TrieSearch<Quant, Bhiksha>::Size(fixed_counts, config), backing), fixed_counts, config);
-
-  if (Quant::kTrain) {
-    util::ErsatzProgress progress(config.messages, "Quantizing", std::accumulate(counts.begin() + 1, counts.end(), 0));
-    for (unsigned char i = 2; i < counts.size(); ++i) {
-      TrainQuantizer(i, counts[i-1], inputs[i-2], progress, quant);
-    }
-    TrainProbQuantizer(counts.size(), counts.back(), inputs[counts.size() - 2], progress, quant);
-    quant.FinishedLoading(config);
-  }
-=======
   util::scoped_FILE unigram_file;
   {
     std::string name(file_prefix + "unigrams");
@@ -587,7 +499,6 @@ template <class Quant, class Bhiksha> void BuildTrie(const std::string &file_pre
   sri.ObtainBackoffs(counts.size(), unigram_file.get(), inputs);
 
   out.SetupMemory(GrowForSearch(config, vocab.UnkCountChangePadding(), TrieSearch<Quant, Bhiksha>::Size(fixed_counts, config), backing), fixed_counts, config);
->>>>>>> upstream/master
 
   for (unsigned char i = 2; i <= counts.size(); ++i) {
     inputs[i-2].Rewind();
@@ -610,30 +521,8 @@ template <class Quant, class Bhiksha> void BuildTrie(const std::string &file_pre
   }
   // Fill entries except unigram probabilities.  
   {
-<<<<<<< HEAD
-    RecursiveInsert<WriteEntries<Quant, Bhiksha> > inserter(&*inputs.begin(), &*contexts.begin(), unigrams, out.middle_begin_, out.longest, &*fixed_counts.begin(), counts.size());
-    inserter.Apply(config.messages, "Building trie", fixed_counts[0]);
-  }
-
-  // Fill unigram probabilities.  
-  try {
-    std::string name(file_prefix + "unigrams");
-    util::scoped_FILE file(OpenOrThrow(name.c_str(), "r"));
-    for (WordIndex i = 0; i < counts[0]; ++i) {
-      ReadOrThrow(file.get(), &unigrams[i].weights, sizeof(ProbBackoff));
-      if (contexts[0] && **contexts[0] == i) {
-        SetExtension(unigrams[i].weights.backoff);
-        ++contexts[0];
-      }
-    }
-    RemoveOrThrow(name.c_str());
-  } catch (util::Exception &e) {
-    e << " while re-reading unigram probabilities";
-    throw;
-=======
     WriteEntries<Quant, Bhiksha> writer(contexts, unigrams, out.middle_begin_, out.longest, counts.size(), sri);
     RecursiveInsert(counts.size(), counts[0], inputs, config.messages, "Writing trie", writer);
->>>>>>> upstream/master
   }
 
   // Do not disable this error message or else too little state will be returned.  Both WriteEntries::Middle and returning state based on found n-grams will need to be fixed to handle this situation.   
@@ -687,17 +576,6 @@ template <class Quant, class Bhiksha> uint8_t *TrieSearch<Quant, Bhiksha>::Setup
   }
   longest.Init(start, quant_.Long(counts.size()), counts[0]);
   return start + Longest::Size(Quant::LongestBits(config), counts.back(), counts[0]);
-<<<<<<< HEAD
-}
-
-template <class Quant, class Bhiksha> void TrieSearch<Quant, Bhiksha>::LoadedBinary() {
-  unigram.LoadedBinary();
-  for (Middle *i = middle_begin_; i != middle_end_; ++i) {
-    i->LoadedBinary();
-  }
-  longest.LoadedBinary();
-}
-=======
 }
 
 template <class Quant, class Bhiksha> void TrieSearch<Quant, Bhiksha>::LoadedBinary() {
@@ -715,7 +593,6 @@ bool IsDirectory(const char *path) {
   return S_ISDIR(info.st_mode);
 }
 } // namespace
->>>>>>> upstream/master
 
 template <class Quant, class Bhiksha> void TrieSearch<Quant, Bhiksha>::InitializeFromARPA(const char *file, util::FilePiece &f, std::vector<uint64_t> &counts, const Config &config, SortedVocabulary &vocab, Backing &backing) {
   std::string temporary_directory;
diff --git a/klm/lm/trie.cc b/klm/lm/trie.cc
index a1136b6f..20075bb8 100644
--- a/klm/lm/trie.cc
+++ b/klm/lm/trie.cc
@@ -91,15 +91,6 @@ template <class Quant, class Bhiksha> bool BitPackedMiddle<Quant, Bhiksha>::Find
   if (!FindBitPacked(base_, word_mask_, word_bits_, total_bits_, range.begin, range.end, max_vocab_, word, at_pointer)) {
     return false;
   }
-<<<<<<< HEAD
-  uint64_t index = at_pointer;
-  at_pointer *= total_bits_;
-  at_pointer += word_bits_;
-  quant_.Read(base_, at_pointer, prob, backoff);
-  at_pointer += quant_.TotalBits();
-
-  bhiksha_.ReadNext(base_, at_pointer, index, total_bits_, range);
-=======
   pointer = at_pointer;
   at_pointer *= total_bits_;
   at_pointer += word_bits_;
@@ -108,7 +99,6 @@ template <class Quant, class Bhiksha> bool BitPackedMiddle<Quant, Bhiksha>::Find
   at_pointer += quant_.TotalBits();
 
   bhiksha_.ReadNext(base_, at_pointer, pointer, total_bits_, range);
->>>>>>> upstream/master
 
   return true;
 }
diff --git a/utils/fdict.h b/utils/fdict.h
index 9c8d7cde..f0871b9a 100644
--- a/utils/fdict.h
+++ b/utils/fdict.h
@@ -33,7 +33,6 @@ struct FD {
     hash_ = new PerfectHashFunction(cmph_file);
 #endif
   }
->>>>>>> upstream/master
   static inline int NumFeats() {
 #ifdef HAVE_CMPH
     if (hash_) return hash_->number_of_keys();