rename model1 to fast_aligner, since it does way more than model1 but is mostly just fast

1 files changed, 271 insertions, 0 deletions

diff --git a/training/fast_align.cc b/training/fast_align.cc
new file mode 100644
index 00000000..0d7b0202
--- /dev/null
+++ b/training/fast_align.cc
@@ -0,0 +1,271 @@
+#include <iostream>
+#include <cmath>
+
+#include <boost/program_options.hpp>
+#include <boost/program_options/variables_map.hpp>
+
+#include "m.h"
+#include "corpus_tools.h"
+#include "stringlib.h"
+#include "filelib.h"
+#include "ttables.h"
+#include "tdict.h"
+
+namespace po = boost::program_options;
+using namespace std;
+
+bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
+  po::options_description opts("Configuration options");
+  opts.add_options()
+        ("input,i",po::value<string>(),"Parallel corpus input file")
+        ("reverse,r","Reverse estimation (swap source and target during training)")
+        ("iterations,I",po::value<unsigned>()->default_value(5),"Number of iterations of EM training")
+        //("bidir,b", "Run bidirectional alignment")
+        ("favor_diagonal,d", "Use a static alignment distribution that assigns higher probabilities to alignments near the diagonal")
+        ("prob_align_null", po::value<double>()->default_value(0.08), "When --favor_diagonal is set, what's the probability of a null alignment?")
+        ("diagonal_tension,T", po::value<double>()->default_value(4.0), "How sharp or flat around the diagonal is the alignment distribution (<1 = flat >1 = sharp)")
+        ("variational_bayes,v","Infer VB estimate of parameters under a symmetric Dirichlet prior")
+        ("alpha,a", po::value<double>()->default_value(0.01), "Hyperparameter for optional Dirichlet prior")
+        ("no_null_word,N","Do not generate from a null token")
+        ("output_parameters,p", "Write model parameters instead of alignments")
+        ("beam_threshold,t",po::value<double>()->default_value(-4),"When writing parameters, log_10 of beam threshold for writing parameter (-10000 to include everything, 0 max parameter only)")
+        ("testset,x", po::value<string>(), "After training completes, compute the log likelihood of this set of sentence pairs under the learned model")
+        ("no_add_viterbi,V","When writing model parameters, do not add Viterbi alignment points (may generate a grammar where some training sentence pairs are unreachable)");
+  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") == 0) {
+    cerr << "Usage " << argv[0] << " [OPTIONS] -i corpus.fr-en\n";
+    cerr << dcmdline_options << endl;
+    return false;
+  }
+  return true;
+}
+
+double PosteriorInference(const vector<WordID>& src, const vector<WordID>& trg) {
+  double llh = 0;
+  static vector<double> unnormed_a_i;
+  if (src.size() > unnormed_a_i.size())
+    unnormed_a_i.resize(src.size());
+  return llh;
+}
+
+int main(int argc, char** argv) {
+  po::variables_map conf;
+  if (!InitCommandLine(argc, argv, &conf)) return 1;
+  const string fname = conf["input"].as<string>();
+  const bool reverse = conf.count("reverse") > 0;
+  const int ITERATIONS = conf["iterations"].as<unsigned>();
+  const double BEAM_THRESHOLD = pow(10.0, conf["beam_threshold"].as<double>());
+  const bool use_null = (conf.count("no_null_word") == 0);
+  const WordID kNULL = TD::Convert("<eps>");
+  const bool add_viterbi = (conf.count("no_add_viterbi") == 0);
+  const bool variational_bayes = (conf.count("variational_bayes") > 0);
+  const bool write_alignments = (conf.count("output_parameters") == 0);
+  const double diagonal_tension = conf["diagonal_tension"].as<double>();
+  const double prob_align_null = conf["prob_align_null"].as<double>();
+  string testset;
+  if (conf.count("testset")) testset = conf["testset"].as<string>();
+  const double prob_align_not_null = 1.0 - prob_align_null;
+  const double alpha = conf["alpha"].as<double>();
+  const bool favor_diagonal = conf.count("favor_diagonal");
+  if (variational_bayes && alpha <= 0.0) {
+    cerr << "--alpha must be > 0\n";
+    return 1;
+  }
+
+  TTable s2t, t2s;
+  TTable::Word2Word2Double s2t_viterbi;
+  double tot_len_ratio = 0;
+  double mean_srclen_multiplier = 0;
+  vector<double> unnormed_a_i;
+  for (int iter = 0; iter < ITERATIONS; ++iter) {
+    const bool final_iteration = (iter == (ITERATIONS - 1));
+    cerr << "ITERATION " << (iter + 1) << (final_iteration ? " (FINAL)" : "") << endl;
+    ReadFile rf(fname);
+    istream& in = *rf.stream();
+    double likelihood = 0;
+    double denom = 0.0;
+    int lc = 0;
+    bool flag = false;
+    string line;
+    string ssrc, strg;
+    vector<WordID> src, trg;
+    while(true) {
+      getline(in, line);
+      if (!in) break;
+      ++lc;
+      if (lc % 1000 == 0) { cerr << '.'; flag = true; }
+      if (lc %50000 == 0) { cerr << " [" << lc << "]\n" << flush; flag = false; }
+      src.clear(); trg.clear();
+      CorpusTools::ReadLine(line, &src, &trg);
+      if (reverse) swap(src, trg);
+      if (src.size() == 0 || trg.size() == 0) {
+        cerr << "Error: " << lc << "\n" << line << endl;
+        return 1;
+      }
+      if (src.size() > unnormed_a_i.size())
+        unnormed_a_i.resize(src.size());
+      if (iter == 0)
+        tot_len_ratio += static_cast<double>(trg.size()) / static_cast<double>(src.size());
+      denom += trg.size();
+      vector<double> probs(src.size() + 1);
+      bool first_al = true;  // used for write_alignments
+      for (int j = 0; j < trg.size(); ++j) {
+        const WordID& f_j = trg[j];
+        double sum = 0;
+        const double j_over_ts = double(j) / trg.size();
+        double prob_a_i = 1.0 / (src.size() + use_null);  // uniform (model 1)
+        if (use_null) {
+          if (favor_diagonal) prob_a_i = prob_align_null;
+          probs[0] = s2t.prob(kNULL, f_j) * prob_a_i;
+          sum += probs[0];
+        }
+        double az = 0;
+        if (favor_diagonal) {
+          for (int ta = 0; ta < src.size(); ++ta) {
+            unnormed_a_i[ta] = exp(-fabs(double(ta) / src.size() - j_over_ts) * diagonal_tension);
+            az += unnormed_a_i[ta];
+          }
+          az /= prob_align_not_null;
+        }
+        for (int i = 1; i <= src.size(); ++i) {
+          if (favor_diagonal)
+            prob_a_i = unnormed_a_i[i-1] / az;
+          probs[i] = s2t.prob(src[i-1], f_j) * prob_a_i;
+          sum += probs[i];
+        }
+        if (final_iteration) {
+          if (add_viterbi || write_alignments) {
+            WordID max_i = 0;
+            double max_p = -1;
+            int max_index = -1;
+            if (use_null) {
+              max_i = kNULL;
+              max_index = 0;
+              max_p = probs[0];
+            }
+            for (int i = 1; i <= src.size(); ++i) {
+              if (probs[i] > max_p) {
+                max_index = i;
+                max_p = probs[i];
+                max_i = src[i-1];
+              }
+            }
+            if (write_alignments) {
+              if (max_index > 0) {
+                if (first_al) first_al = false; else cout << ' ';
+                if (reverse)
+                  cout << j << '-' << (max_index - 1);
+                else
+                  cout << (max_index - 1) << '-' << j;
+              }
+            }
+            s2t_viterbi[max_i][f_j] = 1.0;
+          }
+        } else {
+          if (use_null)
+            s2t.Increment(kNULL, f_j, probs[0] / sum);
+          for (int i = 1; i <= src.size(); ++i)
+            s2t.Increment(src[i-1], f_j, probs[i] / sum);
+        }
+        likelihood += log(sum);
+      }
+      if (write_alignments && final_iteration) cout << endl;
+    }
+
+    // log(e) = 1.0
+    double base2_likelihood = likelihood / log(2);
+
+    if (flag) { cerr << endl; }
+    if (iter == 0) {
+      mean_srclen_multiplier = tot_len_ratio / lc;
+      cerr << "expected target length = source length * " << mean_srclen_multiplier << endl;
+    }
+    cerr << "  log_e likelihood: " << likelihood << endl;
+    cerr << "  log_2 likelihood: " << base2_likelihood << endl;
+    cerr << "   cross entropy: " << (-base2_likelihood / denom) << endl;
+    cerr << "      perplexity: " << pow(2.0, -base2_likelihood / denom) << endl;
+    if (!final_iteration) {
+      if (variational_bayes)
+        s2t.NormalizeVB(alpha);
+      else
+        s2t.Normalize();
+    }
+  }
+  if (testset.size()) {
+    ReadFile rf(testset);
+    istream& in = *rf.stream();
+    int lc = 0;
+    double tlp = 0;
+    string ssrc, strg, line;
+    while (getline(in, line)) {
+      ++lc;
+      vector<WordID> src, trg;
+      CorpusTools::ReadLine(line, &src, &trg);
+      double log_prob = Md::log_poisson(trg.size(), 0.05 + src.size() * mean_srclen_multiplier);
+      if (src.size() > unnormed_a_i.size())
+        unnormed_a_i.resize(src.size());
+
+      // compute likelihood
+      for (int j = 0; j < trg.size(); ++j) {
+        const WordID& f_j = trg[j];
+        double sum = 0;
+        const double j_over_ts = double(j) / trg.size();
+        double prob_a_i = 1.0 / (src.size() + use_null);  // uniform (model 1)
+        if (use_null) {
+          if (favor_diagonal) prob_a_i = prob_align_null;
+          sum += s2t.prob(kNULL, f_j) * prob_a_i;
+        }
+        double az = 0;
+        if (favor_diagonal) {
+          for (int ta = 0; ta < src.size(); ++ta) {
+            unnormed_a_i[ta] = exp(-fabs(double(ta) / src.size() - j_over_ts) * diagonal_tension);
+            az += unnormed_a_i[ta];
+          }
+          az /= prob_align_not_null;
+        }
+        for (int i = 1; i <= src.size(); ++i) {
+          if (favor_diagonal)
+            prob_a_i = unnormed_a_i[i-1] / az;
+          sum += s2t.prob(src[i-1], f_j) * prob_a_i;
+        }
+        log_prob += log(sum);
+      }
+      tlp += log_prob;
+      cerr << ssrc << " ||| " << strg << " ||| " << log_prob << endl;
+    }
+    cerr << "TOTAL LOG PROB " << tlp << endl;
+  }
+
+  if (write_alignments) return 0;
+
+  for (TTable::Word2Word2Double::iterator ei = s2t.ttable.begin(); ei != s2t.ttable.end(); ++ei) {
+    const TTable::Word2Double& cpd = ei->second;
+    const TTable::Word2Double& vit = s2t_viterbi[ei->first];
+    const string& esym = TD::Convert(ei->first);
+    double max_p = -1;
+    for (TTable::Word2Double::const_iterator fi = cpd.begin(); fi != cpd.end(); ++fi)
+      if (fi->second > max_p) max_p = fi->second;
+    const double threshold = max_p * BEAM_THRESHOLD;
+    for (TTable::Word2Double::const_iterator fi = cpd.begin(); fi != cpd.end(); ++fi) {
+      if (fi->second > threshold || (vit.find(fi->first) != vit.end())) {
+        cout << esym << ' ' << TD::Convert(fi->first) << ' ' << log(fi->second) << endl;
+      }
+    } 
+  }
+  return 0;
+}
+