major restructure of the training code

2 files changed, 315 insertions, 0 deletions

diff --git a/training/mira/Makefile.am b/training/mira/Makefile.am
new file mode 100644
index 00000000..ae609ede
--- /dev/null
+++ b/training/mira/Makefile.am
@@ -0,0 +1,6 @@
+bin_PROGRAMS = kbest_mira
+
+kbest_mira_SOURCES = kbest_mira.cc
+kbest_mira_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/klm/search/libksearch.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a $(top_srcdir)/klm/lm/libklm.a $(top_srcdir)/klm/util/libklm_util.a -lz
+
+AM_CPPFLAGS = -W -Wall -Wno-sign-compare -I$(top_srcdir)/utils -I$(top_srcdir)/decoder -I$(top_srcdir)/mteval
diff --git a/training/mira/kbest_mira.cc b/training/mira/kbest_mira.cc
new file mode 100644
index 00000000..8b7993dd
--- /dev/null
+++ b/training/mira/kbest_mira.cc
@@ -0,0 +1,309 @@
+#include <sstream>
+#include <iostream>
+#include <vector>
+#include <cassert>
+#include <cmath>
+#include <tr1/memory>
+
+#include <boost/program_options.hpp>
+#include <boost/program_options/variables_map.hpp>
+
+#include "hg_sampler.h"
+#include "sentence_metadata.h"
+#include "scorer.h"
+#include "verbose.h"
+#include "viterbi.h"
+#include "hg.h"
+#include "prob.h"
+#include "kbest.h"
+#include "ff_register.h"
+#include "decoder.h"
+#include "filelib.h"
+#include "fdict.h"
+#include "weights.h"
+#include "sparse_vector.h"
+#include "sampler.h"
+
+using namespace std;
+namespace po = boost::program_options;
+
+bool invert_score;
+std::tr1::shared_ptr<MT19937> rng;
+
+void RandomPermutation(int len, vector<int>* p_ids) {
+  vector<int>& ids = *p_ids;
+  ids.resize(len);
+  for (int i = 0; i < len; ++i) ids[i] = i;
+  for (int i = len; i > 0; --i) {
+    int j = rng->next() * i;
+    if (j == i) i--;
+    swap(ids[i-1], ids[j]);
+  }  
+}
+
+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")
+        ("source,i",po::value<string>(),"Source file for development set")
+        ("passes,p", po::value<int>()->default_value(15), "Number of passes through the training data")
+        ("reference,r",po::value<vector<string> >(), "[REQD] Reference translation(s) (tokenized text file)")
+        ("mt_metric,m",po::value<string>()->default_value("ibm_bleu"), "Scoring metric (ibm_bleu, nist_bleu, koehn_bleu, ter, combi)")
+        ("max_step_size,C", po::value<double>()->default_value(0.01), "regularization strength (C)")
+        ("mt_metric_scale,s", po::value<double>()->default_value(1.0), "Amount to scale MT loss function by")
+        ("k_best_size,k", po::value<int>()->default_value(250), "Size of hypothesis list to search for oracles")
+        ("sample_forest,f", "Instead of a k-best list, sample k hypotheses from the decoder's forest")
+        ("sample_forest_unit_weight_vector,x", "Before sampling (must use -f option), rescale the weight vector used so it has unit length; this may improve the quality of the samples")
+        ("random_seed,S", po::value<uint32_t>(), "Random seed (if not specified, /dev/random will be used)")
+        ("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("source") || !conf->count("decoder_config") || !conf->count("reference")) {
+    cerr << dcmdline_options << endl;
+    return false;
+  }
+  return true;
+}
+
+static const double kMINUS_EPSILON = -1e-6;
+
+struct HypothesisInfo {
+  SparseVector<double> features;
+  double mt_metric;
+};
+
+struct GoodBadOracle {
+  std::tr1::shared_ptr<HypothesisInfo> good;
+  std::tr1::shared_ptr<HypothesisInfo> bad;
+};
+
+struct TrainingObserver : public DecoderObserver {
+  TrainingObserver(const int k, const DocScorer& d, bool sf, vector<GoodBadOracle>* o) : ds(d), oracles(*o), kbest_size(k), sample_forest(sf) {}
+  const DocScorer& ds;
+  vector<GoodBadOracle>& oracles;
+  std::tr1::shared_ptr<HypothesisInfo> cur_best;
+  const int kbest_size;
+  const bool sample_forest;
+
+  const HypothesisInfo& GetCurrentBestHypothesis() const {
+    return *cur_best;
+  }
+
+  virtual void NotifyTranslationForest(const SentenceMetadata& smeta, Hypergraph* hg) {
+    UpdateOracles(smeta.GetSentenceID(), *hg);
+  }
+
+  std::tr1::shared_ptr<HypothesisInfo> MakeHypothesisInfo(const SparseVector<double>& feats, const double score) {
+    std::tr1::shared_ptr<HypothesisInfo> h(new HypothesisInfo);
+    h->features = feats;
+    h->mt_metric = score;
+    return h;
+  }
+
+  void UpdateOracles(int sent_id, const Hypergraph& forest) {
+    std::tr1::shared_ptr<HypothesisInfo>& cur_good = oracles[sent_id].good;
+    std::tr1::shared_ptr<HypothesisInfo>& cur_bad = oracles[sent_id].bad;
+    cur_bad.reset();  // TODO get rid of??
+
+    if (sample_forest) {
+      vector<WordID> cur_prediction;
+      ViterbiESentence(forest, &cur_prediction);
+      float sentscore = ds[sent_id]->ScoreCandidate(cur_prediction)->ComputeScore();
+      cur_best = MakeHypothesisInfo(ViterbiFeatures(forest), sentscore);
+
+      vector<HypergraphSampler::Hypothesis> samples;
+      HypergraphSampler::sample_hypotheses(forest, kbest_size, &*rng, &samples);
+      for (unsigned i = 0; i < samples.size(); ++i) {
+        sentscore = ds[sent_id]->ScoreCandidate(samples[i].words)->ComputeScore();
+        if (invert_score) sentscore *= -1.0;
+        if (!cur_good || sentscore > cur_good->mt_metric)
+          cur_good = MakeHypothesisInfo(samples[i].fmap, sentscore);
+        if (!cur_bad || sentscore < cur_bad->mt_metric)
+          cur_bad = MakeHypothesisInfo(samples[i].fmap, sentscore);
+      }
+    } else {
+      KBest::KBestDerivations<vector<WordID>, ESentenceTraversal> kbest(forest, kbest_size);
+      for (int i = 0; i < kbest_size; ++i) {
+        const KBest::KBestDerivations<vector<WordID>, ESentenceTraversal>::Derivation* d =
+          kbest.LazyKthBest(forest.nodes_.size() - 1, i);
+        if (!d) break;
+        float sentscore = ds[sent_id]->ScoreCandidate(d->yield)->ComputeScore();
+        if (invert_score) sentscore *= -1.0;
+        // cerr << TD::GetString(d->yield) << " ||| " << d->score << " ||| " << sentscore << endl;
+        if (i == 0)
+          cur_best = MakeHypothesisInfo(d->feature_values, sentscore);
+        if (!cur_good || sentscore > cur_good->mt_metric)
+          cur_good = MakeHypothesisInfo(d->feature_values, sentscore);
+        if (!cur_bad || sentscore < cur_bad->mt_metric)
+          cur_bad = MakeHypothesisInfo(d->feature_values, sentscore);
+      }
+      //cerr << "GOOD: " << cur_good->mt_metric << endl;
+      //cerr << " CUR: " << cur_best->mt_metric << endl;
+      //cerr << " BAD: " << cur_bad->mt_metric << endl;
+    }
+  }
+};
+
+void ReadTrainingCorpus(const string& fname, vector<string>* c) {
+  ReadFile rf(fname);
+  istream& in = *rf.stream();
+  string line;
+  while(in) {
+    getline(in, line);
+    if (!in) break;
+    c->push_back(line);
+  }
+}
+
+bool ApproxEqual(double a, double b) {
+  if (a == b) return true;
+  return (fabs(a-b)/fabs(b)) < 0.000001;
+}
+
+int main(int argc, char** argv) {
+  register_feature_functions();
+  SetSilent(true);  // turn off verbose decoder output
+
+  po::variables_map conf;
+  if (!InitCommandLine(argc, argv, &conf)) return 1;
+
+  if (conf.count("random_seed"))
+    rng.reset(new MT19937(conf["random_seed"].as<uint32_t>()));
+  else
+    rng.reset(new MT19937);
+  const bool sample_forest = conf.count("sample_forest") > 0;
+  const bool sample_forest_unit_weight_vector = conf.count("sample_forest_unit_weight_vector") > 0;
+  if (sample_forest_unit_weight_vector && !sample_forest) {
+    cerr << "Cannot --sample_forest_unit_weight_vector without --sample_forest" << endl;
+    return 1;
+  }
+  vector<string> corpus;
+  ReadTrainingCorpus(conf["source"].as<string>(), &corpus);
+  const string metric_name = conf["mt_metric"].as<string>();
+  ScoreType type = ScoreTypeFromString(metric_name);
+  if (type == TER) {
+    invert_score = true;
+  } else {
+    invert_score = false;
+  }
+  DocScorer ds(type, conf["reference"].as<vector<string> >(), "");
+  cerr << "Loaded " << ds.size() << " references for scoring with " << metric_name << endl;
+  if (ds.size() != corpus.size()) {
+    cerr << "Mismatched number of references (" << ds.size() << ") and sources (" << corpus.size() << ")\n";
+    return 1;
+  }
+
+  ReadFile ini_rf(conf["decoder_config"].as<string>());
+  Decoder decoder(ini_rf.stream());
+
+  // load initial weights
+  vector<weight_t>& dense_weights = decoder.CurrentWeightVector();
+  SparseVector<weight_t> lambdas;
+  Weights::InitFromFile(conf["input_weights"].as<string>(), &dense_weights);
+  Weights::InitSparseVector(dense_weights, &lambdas);
+
+  const double max_step_size = conf["max_step_size"].as<double>();
+  const double mt_metric_scale = conf["mt_metric_scale"].as<double>();
+
+  assert(corpus.size() > 0);
+  vector<GoodBadOracle> oracles(corpus.size());
+
+  TrainingObserver observer(conf["k_best_size"].as<int>(), ds, sample_forest, &oracles);
+  int cur_sent = 0;
+  int lcount = 0;
+  int normalizer = 0;
+  double tot_loss = 0;
+  int dots = 0;
+  int cur_pass = 0;
+  SparseVector<double> tot;
+  tot += lambdas;          // initial weights
+  normalizer++;            // count for initial weights
+  int max_iteration = conf["passes"].as<int>() * corpus.size();
+  string msg = "# MIRA tuned weights";
+  string msga = "# MIRA tuned weights AVERAGED";
+  vector<int> order;
+  RandomPermutation(corpus.size(), &order);
+  while (lcount <= max_iteration) {
+    lambdas.init_vector(&dense_weights);
+    if ((cur_sent * 40 / corpus.size()) > dots) { ++dots; cerr << '.'; }
+    if (corpus.size() == cur_sent) {
+      cerr << " [AVG METRIC LAST PASS=" << (tot_loss / corpus.size()) << "]\n";
+      Weights::ShowLargestFeatures(dense_weights);
+      cur_sent = 0;
+      tot_loss = 0;
+      dots = 0;
+      ostringstream os;
+      os << "weights.mira-pass" << (cur_pass < 10 ? "0" : "") << cur_pass << ".gz";
+      SparseVector<double> x = tot;
+      x /= normalizer;
+      ostringstream sa;
+      sa << "weights.mira-pass" << (cur_pass < 10 ? "0" : "") << cur_pass << "-avg.gz";
+      x.init_vector(&dense_weights);
+      Weights::WriteToFile(os.str(), dense_weights, true, &msg);
+      ++cur_pass;
+      RandomPermutation(corpus.size(), &order);
+    }
+    if (cur_sent == 0) {
+      cerr << "PASS " << (lcount / corpus.size() + 1) << endl;
+    }
+    decoder.SetId(order[cur_sent]);
+    double sc = 1.0;
+    if (sample_forest_unit_weight_vector) {
+      sc = lambdas.l2norm();
+      if (sc > 0) {
+        for (unsigned i = 0; i < dense_weights.size(); ++i)
+          dense_weights[i] /= sc;
+      }
+    }
+    decoder.Decode(corpus[order[cur_sent]], &observer);  // update oracles
+    if (sc && sc != 1.0) {
+      for (unsigned i = 0; i < dense_weights.size(); ++i)
+        dense_weights[i] *= sc;
+    }
+    const HypothesisInfo& cur_hyp = observer.GetCurrentBestHypothesis();
+    const HypothesisInfo& cur_good = *oracles[order[cur_sent]].good;
+    const HypothesisInfo& cur_bad = *oracles[order[cur_sent]].bad;
+    tot_loss += cur_hyp.mt_metric;
+    if (!ApproxEqual(cur_hyp.mt_metric, cur_good.mt_metric)) {
+      const double loss = cur_bad.features.dot(dense_weights) - cur_good.features.dot(dense_weights) +
+          mt_metric_scale * (cur_good.mt_metric - cur_bad.mt_metric);
+      //cerr << "LOSS: " << loss << endl;
+      if (loss > 0.0) {
+        SparseVector<double> diff = cur_good.features;
+        diff -= cur_bad.features;
+        double step_size = loss / diff.l2norm_sq();
+        //cerr << loss << " " << step_size << " " << diff << endl;
+        if (step_size > max_step_size) step_size = max_step_size;
+        lambdas += (cur_good.features * step_size);
+        lambdas -= (cur_bad.features * step_size);
+        //cerr << "L: " << lambdas << endl;
+      }
+    }
+    tot += lambdas;
+    ++normalizer;
+    ++lcount;
+    ++cur_sent;
+  }
+  cerr << endl;
+  Weights::WriteToFile("weights.mira-final.gz", dense_weights, true, &msg);
+  tot /= normalizer;
+  tot.init_vector(dense_weights);
+  msg = "# MIRA tuned weights (averaged vector)";
+  Weights::WriteToFile("weights.mira-final-avg.gz", dense_weights, true, &msg);
+  cerr << "Optimization complete.\nAVERAGED WEIGHTS: weights.mira-final-avg.gz\n";
+  return 0;
+}
+