Baum Welch training for HMMs

7 files changed, 2368 insertions, 1 deletions

diff --git a/training/crf/Makefile.am b/training/crf/Makefile.am
index 621ca803..4a8c30fd 100644
--- a/training/crf/Makefile.am
+++ b/training/crf/Makefile.am
@@ -4,7 +4,11 @@ bin_PROGRAMS = \
   mpi_extract_features \
   mpi_extract_reachable \
   mpi_flex_optimize \
-  mpi_online_optimize
+  mpi_online_optimize \
+  mpi_baum_welch
+
+mpi_baum_welch_SOURCES = mpi_baum_welch.cc
+mpi_baum_welch_LDADD = ../../decoder/libcdec.a ../../klm/search/libksearch.a ../../mteval/libmteval.a ../../utils/libutils.a ../../klm/lm/libklm.a ../../klm/util/libklm_util.a ../../klm/util/double-conversion/libklm_util_double.a -lz
 
 mpi_online_optimize_SOURCES = mpi_online_optimize.cc
 mpi_online_optimize_LDADD = ../../training/utils/libtraining_utils.a ../../decoder/libcdec.a ../../klm/search/libksearch.a ../../mteval/libmteval.a ../../utils/libutils.a ../../klm/lm/libklm.a ../../klm/util/libklm_util.a ../../klm/util/double-conversion/libklm_util_double.a -lz
diff --git a/training/crf/baum_welch_example/README.md b/training/crf/baum_welch_example/README.md
new file mode 100644
index 00000000..97525da5
--- /dev/null
+++ b/training/crf/baum_welch_example/README.md
@@ -0,0 +1,32 @@
+Here's how to do Baum-Welch training with `cdec`.
+
+## Set the tags you want.
+
+First, set the number of tags you want in tagset.txt (these
+can be any symbols, listed one after another, separated
+by whitespace), e.g.:
+
+    C1 C2 C3 C4
+
+## Extract the parameter feature names
+
+    ../mpi_extract_features -c cdec.ini -t train.txt
+
+If you have compiled with MPI, you can use `mpirun`:
+
+    mpirun -np 8 ../mpi_extract_features -c cdec.ini -t train.txt
+
+## Randomly initialize the weights file
+
+    sort -u features.* | ./random_init.pl > weights.init
+
+## Run training
+
+    ../mpi_baum_welch -c cdec.ini -t train.txt -w weights.init -n 50
+
+Again, if you have compiled with MPI, you can use `mpirun`:
+
+    mpirun -np 8 ../mpi_baum_welch -c cdec.ini -t train.txt -w weights.init -n 50
+
+The `-n` flag indicates how many iterations to run for.
+
diff --git a/training/crf/baum_welch_example/cdec.ini b/training/crf/baum_welch_example/cdec.ini
new file mode 100644
index 00000000..61203da7
--- /dev/null
+++ b/training/crf/baum_welch_example/cdec.ini
@@ -0,0 +1,5 @@
+feature_function=Tagger_BigramIndicator
+feature_function=LexicalPairIndicator
+formalism=tagger
+tagger_tagset=tagset.txt
+intersection_strategy=full
diff --git a/training/crf/baum_welch_example/random_init.pl b/training/crf/baum_welch_example/random_init.pl
new file mode 100755
index 00000000..98467ed1
--- /dev/null
+++ b/training/crf/baum_welch_example/random_init.pl
@@ -0,0 +1,9 @@
+#!/usr/bin/perl -w
+while(<>) {
+  chomp;
+  my ($a,$b,@d) =split /\s+/;
+  die "Bad input" if scalar @d > 0;
+  $r = -rand() * rand() - 0.5;
+  $r = 0 if $a =~ /^Uni:/;
+  print "$a $r\n";
+}
diff --git a/training/crf/baum_welch_example/tagset.txt b/training/crf/baum_welch_example/tagset.txt
new file mode 100644
index 00000000..93a48451
--- /dev/null
+++ b/training/crf/baum_welch_example/tagset.txt
@@ -0,0 +1 @@
+1 2 3 4
diff --git a/training/crf/baum_welch_example/train.txt b/training/crf/baum_welch_example/train.txt
new file mode 100644
index 00000000..e9c3455e
--- /dev/null
+++ b/training/crf/baum_welch_example/train.txt
@@ -0,0 +1,2000 @@
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+r o m a n i a
+h e l p i n g
+f a s t
+h a p p e n e d
+d e f e n d i n g
+n a v y
+w i t n e s s e s
+f u l l y
+s u s p e c t
+i s l a n d s
+m a i n t a i n
+p r e s e n c e
+j a k a r t a
+p a c k a g e
+y a r d s
+g a i n
+a c c o u n t
+s q u a d
+s h a r o n
+w i n g
+a c t i o n s
+a t h e n s
+s t r a t e g i c
+s t r e n g t h e n
+f r i e n d l y
+d e s t r o y e d
+a p p a r e n t l y
+c o n s e r v a t i v e
+g a i n s
+f a i l u r e
+f u t u r e s
+s h o t s
+r e l a t i o n s h i p
+c o m m i s s i o n e r
+m a l a y s i a n
+r e q u i r e d
+a t l a n t a
+a g r e e
+d e f e a t
+s t r i k e r
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+a s s e t s
+h o u s e s
+s u p p l i e s
+s a f e
+m i l l i o n s
+s o u g h t
+f r e s h
+v i d e o
+p r o s e c u t o r
+p u l l e d
+v e h i c l e
+t o l l
+p a r e n t s
+c e a s e
+a c t i v i s t s
+o r g a n i z e d
+e n t e r e d
+s h i i t e
+l a n g u a g e
+a b b a s
+b i n
+p r e v i o u s l y
+c l o s i n g
+w o r k s
+t e r r o r i s t s
+t o n y
+c o v e r
+f o l l o w
+l e g i s l a t i v e
+r i c h
+c l a s h e s
+i m p o s e d
+r a n
+m c c a i n
+s u c c e s s f u l
+s e v e n t h
+s c o r i n g
+c a u g h t
+a p p o i n t e d
+a l l i e s
+a d m i t t e d
+w o r l d w i d e
+o r d e r s
+d e m a n d e d
+c r e a t e d
+r a n k e d
+m i l i t a n t
+i n v e s t i g a t o r s
+s h o w i n g
+p o s s i b i l i t y
+s e a t
+d a u g h t e r
+s i t e s
+s h o r t l y
+c o m m e r c e
+n e t a n y a h u
+a d v a n c e
+a i r l i n e
+f i r m s
+a b r o a d
+f o u n d a t i o n
+c o m m i t m e n t
+p l e d g e d
+k i l l
+r e p r e s e n t a t i v e
+n o r t h w e s t
+s c e n e
+b e a t i n g
+i m p r o v e d
+r e s u m e
+w h o m
+s l i g h t l y
+v o t i n g
+b o m b i n g s
+s e r i o u s l y
+s e t t i n g
+c a r l o s
+e f f e c t i v e
+h k
+r e g u l a r
+j i a n g
+p r i n c e
+d e c l i n e
+b a y
+n o r t h e a s t
+s o l d i e r
+r e a c h i n g
+a g r e e m e n t s
+m i k e
+h u r t
+c r i t i c a l
+i d e a
+m i l o s e v i c
+f i s c a l
+t a r g e t s
+a g r i c u l t u r a l
+m u s h a r r a f
+d e s i g n e d
+o v e r n i g h t
+b o y
+d o z e n
+p r o d u c e
+c a l m
+s t a n d a r d s
+l e g i s l a t i o n
+s e n t e n c e
+w i t h d r a w a l
+s e e d e d
+c o m p o s i t e
+t r a d e d
+w i n t e r
+d a v i s
+t r u s t
+c l i m a t e
+i n d u s t r i e s
+p r o f i t s
+v o t e d
+c a m b o d i a
+s y r i a n
+s i g n s
+l o a n
+s t e e l
+e l e c t r i c i t y
+t e h r a n
+c i t i n g
+h u s b a n d
+b i t
+c o m b a t
+h a n d e d
+f e s t i v a l
+i m f
+p r e s i d e n c y
+c a p t u r e d
+s t u d e n t
+f i n e
+s t a t i o n s
+s i l v e r
+c h a v e z
+i n t e r
+m o m e n t
+t a b l e
+c o u p
+p o p e
+p r o v i n c e s
+a h m e d
+b u i l d i n g s
+o u t p u t
+l i b e r a t i o n
+m o n e t a r y
+c l o s e r
+c o l l e g e
+f l u
+a d v a n t a g e
+a s s i s t a n t
+g o n e
+s e c r e t
+x
+c a t h o l i c
+n a m e s
+l i s t e d
+f i n a l l y
+c a n c e r
+p r o d u c e d
+m e a s u r e
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+d e f e a t e d
+c o n g o
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+p r i z e
+b a n g k o k
+a s k
+f r a n c i s c o
+r e g i s t e r e d
+d i s a s t e r
+g o l f
+i n d i v i d u a l
+c o n t i n u e s
+w t o
+i n i t i a l
+a n y o n e
+q u a k e
+f a c e d
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+m o b i l e
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+f i e l d s
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+d e n m a r k
+m a n i l a
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+c e n t
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+e s t a b l i s h
+w i d e l y
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+i n s i s t e d
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+k a s h m i r
+r e f e r e n d u m
+l o c a t e d
+u p o n
+a l l o w i n g
+s c a l e
+o p p o s e d
+w a t c h
+i n d i c a t e d
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+a n a l y s t
+a d d s
+a l o n e
+g o e s
+m o v i e
+e x p e c t s
+a r t
+s e o u l
+m e x i c a n
+y e s t e r d a y
+p l a n e s
+n i n t h
+o n l i n e
+h e l i c o p t e r
+i m m i g r a t i o n
+p a r t n e r s
+i n f r a s t r u c t u r e
+b o a t
+v i s i t s
+n o r m a l
+s t a k e
+g u e r r i l l a s
+m a c a o
+w i l l i n g
+s u n
+a w a r d
+t e l l
+s o u t h w e s t
+s p o r t
+e n t e r
+r e s o l v e
+c h a n c e s
+m i a m i
+e l
+e n t i r e
diff --git a/training/crf/mpi_baum_welch.cc b/training/crf/mpi_baum_welch.cc
new file mode 100644
index 00000000..d69b1769
--- /dev/null
+++ b/training/crf/mpi_baum_welch.cc
@@ -0,0 +1,316 @@
+#include <sstream>
+#include <iostream>
+#include <vector>
+#include <cassert>
+#include <cmath>
+
+#include "config.h"
+#ifdef HAVE_MPI
+#include <boost/mpi/timer.hpp>
+#include <boost/mpi.hpp>
+namespace mpi = boost::mpi;
+#endif
+
+#include <boost/unordered_map.hpp>
+#include <boost/functional/hash.hpp>
+#include <boost/shared_ptr.hpp>
+#include <boost/program_options.hpp>
+#include <boost/program_options/variables_map.hpp>
+
+#include "sentence_metadata.h"
+#include "verbose.h"
+#include "hg.h"
+#include "prob.h"
+#include "inside_outside.h"
+#include "ff_register.h"
+#include "decoder.h"
+#include "filelib.h"
+#include "stringlib.h"
+#include "fdict.h"
+#include "weights.h"
+#include "sparse_vector.h"
+
+using namespace std;
+namespace po = boost::program_options;
+
+bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
+  po::options_description opts("Configuration options");
+  opts.add_options()
+        ("input_weights,w",po::value<string>(),"Input feature weights file")
+        ("iterations,n",po::value<unsigned>()->default_value(50), "Number of training iterations")
+        ("training_data,t",po::value<string>(),"Training data")
+        ("decoder_config,c",po::value<string>(),"Decoder configuration file");
+  po::options_description clo("Command line options");
+  clo.add_options()
+        ("config", po::value<string>(), "Configuration file")
+        ("help,h", "Print this help message and exit");
+  po::options_description dconfig_options, dcmdline_options;
+  dconfig_options.add(opts);
+  dcmdline_options.add(opts).add(clo);
+  
+  po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
+  if (conf->count("config")) {
+    ifstream config((*conf)["config"].as<string>().c_str());
+    po::store(po::parse_config_file(config, dconfig_options), *conf);
+  }
+  po::notify(*conf);
+
+  if (conf->count("help") || !conf->count("input_weights") || !(conf->count("training_data")) || !conf->count("decoder_config")) {
+    cerr << dcmdline_options << endl;
+    return false;
+  }
+  return true;
+}
+
+void ReadTrainingCorpus(const string& fname, int rank, int size, vector<string>* c) {
+  ReadFile rf(fname);
+  istream& in = *rf.stream();
+  string line;
+  int lc = 0;
+  while(in) {
+    getline(in, line);
+    if (!in) break;
+    if (lc % size == rank) c->push_back(line);
+    ++lc;
+  }
+}
+
+static const double kMINUS_EPSILON = -1e-6;
+
+struct TrainingObserver : public DecoderObserver {
+  void Reset() {
+    acc_grad.clear();
+    acc_obj = 0;
+    total_complete = 0;
+    trg_words = 0;
+  } 
+
+  void SetLocalGradientAndObjective(vector<double>* g, double* o) const {
+    *o = acc_obj;
+    for (SparseVector<double>::const_iterator it = acc_grad.begin(); it != acc_grad.end(); ++it)
+      (*g)[it->first] = it->second;
+  }
+
+  virtual void NotifyDecodingStart(const SentenceMetadata& smeta) {
+    state = 1;
+  }
+
+  // compute model expectations, denominator of objective
+  virtual void NotifyTranslationForest(const SentenceMetadata& smeta, Hypergraph* hg) {
+    assert(state == 1);
+    trg_words += smeta.GetSourceLength();
+    state = 2;
+    SparseVector<prob_t> exps;
+    const prob_t z = InsideOutside<prob_t,
+                                   EdgeProb,
+                                   SparseVector<prob_t>,
+                                   EdgeFeaturesAndProbWeightFunction>(*hg, &exps);
+    exps /= z;
+    for (SparseVector<prob_t>::iterator it = exps.begin(); it != exps.end(); ++it)
+      acc_grad.add_value(it->first, it->second.as_float());
+
+    acc_obj += log(z);
+  }
+
+  // compute "empirical" expectations, numerator of objective
+  virtual void NotifyAlignmentForest(const SentenceMetadata& smeta, Hypergraph* hg) {
+    cerr << "Shouldn't get an alignment forest!\n";
+    abort();
+  }
+
+  virtual void NotifyDecodingComplete(const SentenceMetadata& smeta) {
+    ++total_complete;
+  }
+
+  int total_complete;
+  SparseVector<double> acc_grad;
+  double acc_obj;
+  unsigned trg_words;
+  int state;
+};
+
+void ReadConfig(const string& ini, vector<string>* out) {
+  ReadFile rf(ini);
+  istream& in = *rf.stream();
+  while(in) {
+    string line;
+    getline(in, line);
+    if (!in) continue;
+    out->push_back(line);
+  }
+}
+
+void StoreConfig(const vector<string>& cfg, istringstream* o) {
+  ostringstream os;
+  for (int i = 0; i < cfg.size(); ++i) { os << cfg[i] << endl; }
+  o->str(os.str());
+}
+
+#if 0
+template <typename T>
+struct VectorPlus : public binary_function<vector<T>, vector<T>, vector<T> >  {
+  vector<T> operator()(const vector<int>& a, const vector<int>& b) const {
+    assert(a.size() == b.size());
+    vector<T> v(a.size());
+    transform(a.begin(), a.end(), b.begin(), v.begin(), plus<T>()); 
+    return v;
+  } 
+}; 
+#endif
+
+int main(int argc, char** argv) {
+#ifdef HAVE_MPI
+  mpi::environment env(argc, argv);
+  mpi::communicator world;
+  const int size = world.size(); 
+  const int rank = world.rank();
+#else
+  const int size = 1;
+  const int rank = 0;
+#endif
+  SetSilent(true);  // turn off verbose decoder output
+  register_feature_functions();
+
+  po::variables_map conf;
+  if (!InitCommandLine(argc, argv, &conf)) return 1;
+  const unsigned iterations = conf["iterations"].as<unsigned>();
+
+  // load cdec.ini and set up decoder
+  vector<string> cdec_ini;
+  ReadConfig(conf["decoder_config"].as<string>(), &cdec_ini);
+  istringstream ini;
+  StoreConfig(cdec_ini, &ini);
+  Decoder* decoder = new Decoder(&ini);
+  if (decoder->GetConf()["input"].as<string>() != "-") {
+    cerr << "cdec.ini must not set an input file\n";
+    return 1;
+  }
+
+  // load initial weights
+  if (rank == 0) { cerr << "Loading weights...\n"; }
+  vector<weight_t>& lambdas = decoder->CurrentWeightVector();
+  Weights::InitFromFile(conf["input_weights"].as<string>(), &lambdas);
+  if (rank == 0) { cerr << "Done loading weights.\n"; }
+
+  // freeze feature set (should be optional?)
+  const bool freeze_feature_set = true;
+  if (freeze_feature_set) FD::Freeze();
+
+  const int num_feats = FD::NumFeats();
+  if (rank == 0) cerr << "Number of features: " << num_feats << endl;
+  lambdas.resize(num_feats);
+
+  vector<double> gradient(num_feats, 0.0);
+  vector<double> rcv_grad;
+  rcv_grad.clear();
+  bool converged = false;
+
+  vector<string> corpus, test_corpus;
+  ReadTrainingCorpus(conf["training_data"].as<string>(), rank, size, &corpus);
+  assert(corpus.size() > 0);
+  if (conf.count("test_data"))
+    ReadTrainingCorpus(conf["test_data"].as<string>(), rank, size, &test_corpus);
+
+  // build map from feature id to the accumulator that should normalize
+  boost::unordered_map<std::string, boost::unordered_map<int, double>, boost::hash<std::string> > ccs;
+  vector<boost::unordered_map<int, double>* > cpd_to_acc;
+  if (rank == 0) {
+    cpd_to_acc.resize(num_feats);
+    for (unsigned f = 1; f < num_feats; ++f) {
+      string normalizer;
+      //0 ||| 7 9 ||| Bi:BOS_7=1 Bi:7_9=1 Bi:9_EOS=1 Id:a:7=1 Uni:7=1 Id:b:9=1 Uni:9=1 ||| 0
+      const string& fstr = FD::Convert(f);
+      if (fstr.find("Bi:") == 0) {
+        size_t pos = fstr.rfind('_');
+        if (pos < fstr.size())
+          normalizer = fstr.substr(0, pos);
+      } else if (fstr.find("Id:") == 0) {
+        size_t pos = fstr.rfind(':');
+        if (pos < fstr.size()) {
+          normalizer = "Emit:";
+          normalizer += fstr.substr(pos);
+        }
+      }
+      if (normalizer.size() > 0) {
+        boost::unordered_map<int, double>& acc = ccs[normalizer];
+        cpd_to_acc[f] = &acc;
+      }
+    }
+  }
+
+  TrainingObserver observer;
+  int iteration = 0;
+  while (!converged) {
+    ++iteration;
+    observer.Reset();
+#ifdef HAVE_MPI
+    mpi::timer timer;
+    world.barrier();
+#endif
+    if (rank == 0) {
+      cerr << "Starting decoding... (~" << corpus.size() << " sentences / proc)\n";
+      cerr << "  Testset size: " << test_corpus.size() << " sentences / proc)\n";
+      for(boost::unordered_map<string, boost::unordered_map<int,double>, boost::hash<string> >::iterator it = ccs.begin(); it != ccs.end(); ++it)
+        it->second.clear();
+    }
+    for (int i = 0; i < corpus.size(); ++i)
+      decoder->Decode(corpus[i], &observer);
+    cerr << "  process " << rank << '/' << size << " done\n";
+    fill(gradient.begin(), gradient.end(), 0);
+    double objective = 0;
+    observer.SetLocalGradientAndObjective(&gradient, &objective);
+
+    unsigned total_words = 0;
+#ifdef HAVE_MPI
+    double to = 0;
+    rcv_grad.resize(num_feats, 0.0);
+    mpi::reduce(world, &gradient[0], gradient.size(), &rcv_grad[0], plus<double>(), 0);
+    swap(gradient, rcv_grad);
+    rcv_grad.clear();
+
+    reduce(world, observer.trg_words, total_words, std::plus<unsigned>(), 0);
+    mpi::reduce(world, objective, to, plus<double>(), 0);
+    objective = to;
+#else
+    total_words = observer.trg_words;
+#endif
+    if (rank == 0) {  // run optimizer only on rank=0 node
+      cerr << "TRAINING CORPUS: ln p(x)=" << objective << "\t log_2 p(x) = " << (objective/log(2)) << "\t cross entropy = " << (objective/log(2) / total_words) << "\t ppl = " << pow(2, (-objective/log(2) / total_words)) << endl;
+      for (unsigned f = 1; f < num_feats; ++f) {
+        boost::unordered_map<int, double>* m = cpd_to_acc[f];
+        if (m && gradient[f]) {
+          (*m)[f] += gradient[f];
+        }
+        for(boost::unordered_map<string, boost::unordered_map<int,double>, boost::hash<string> >::iterator it = ccs.begin(); it != ccs.end(); ++it) {
+          const boost::unordered_map<int,double>& ccs = it->second;
+          double z = 0;
+          for (boost::unordered_map<int,double>::const_iterator ci = ccs.begin(); ci != ccs.end(); ++ci)
+            z += ci->second + 1e-09;
+          double lz = log(z);
+          for (boost::unordered_map<int,double>::const_iterator ci = ccs.begin(); ci != ccs.end(); ++ci)
+            lambdas[ci->first] = log(ci->second + 1e-09) - lz;
+        }
+      }
+      Weights::SanityCheck(lambdas);
+      Weights::ShowLargestFeatures(lambdas);
+
+      converged = (iteration == iterations);
+
+      string fname = "weights.cur.gz";
+      if (converged) { fname = "weights.final.gz"; }
+      ostringstream vv;
+      vv << "Objective = " << objective << "  (eval count=" << iteration << ")";
+      const string svv = vv.str();
+      Weights::WriteToFile(fname, lambdas, true, &svv);
+    }  // rank == 0
+    int cint = converged;
+#ifdef HAVE_MPI
+    mpi::broadcast(world, &lambdas[0], lambdas.size(), 0);
+    mpi::broadcast(world, cint, 0);
+    if (rank == 0) { cerr << "  ELAPSED TIME THIS ITERATION=" << timer.elapsed() << endl; }
+#endif
+    converged = cint;
+  }
+  return 0;
+}
+