merge with upstream

1 files changed, 421 insertions, 0 deletions

diff --git a/training/lbl_model.cc b/training/lbl_model.cc
new file mode 100644
index 00000000..a46ce33c
--- /dev/null
+++ b/training/lbl_model.cc
@@ -0,0 +1,421 @@
+#include <iostream>
+
+#include "config.h"
+#ifndef HAVE_EIGEN
+  int main() { std::cerr << "Please rebuild with --with-eigen PATH\n"; return 1; }
+#else
+
+#include <cstdlib>
+#include <algorithm>
+#include <cmath>
+#include <set>
+#include <cstring> // memset
+#include <ctime>
+
+#ifdef HAVE_MPI
+#include <boost/mpi/timer.hpp>
+#include <boost/mpi.hpp>
+#include <boost/archive/text_oarchive.hpp>
+namespace mpi = boost::mpi;
+#endif
+#include <boost/math/special_functions/fpclassify.hpp>
+#include <boost/program_options.hpp>
+#include <boost/program_options/variables_map.hpp>
+#include <Eigen/Dense>
+
+#include "corpus_tools.h"
+#include "optimize.h"
+#include "array2d.h"
+#include "m.h"
+#include "lattice.h"
+#include "stringlib.h"
+#include "filelib.h"
+#include "tdict.h"
+
+namespace po = boost::program_options;
+using namespace std;
+
+#define kDIMENSIONS 10
+typedef Eigen::Matrix<double, kDIMENSIONS, 1> RVector;
+typedef Eigen::Matrix<double, 1, kDIMENSIONS> RTVector;
+typedef Eigen::Matrix<double, kDIMENSIONS, kDIMENSIONS> TMatrix;
+vector<RVector> r_src, r_trg;
+
+#if HAVE_MPI
+namespace boost {
+namespace serialization {
+
+template<class Archive>
+void serialize(Archive & ar, RVector & v, const unsigned int version) {
+  for (unsigned i = 0; i < kDIMENSIONS; ++i)
+    ar & v[i];
+}
+
+} // namespace serialization
+} // namespace boost
+#endif
+
+bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
+  po::options_description opts("Configuration options");
+  opts.add_options()
+        ("input,i",po::value<string>(),"Input file")
+        ("iterations,I",po::value<unsigned>()->default_value(1000),"Number of iterations of training")
+        ("regularization_strength,C",po::value<double>()->default_value(0.1),"L2 regularization strength (0 for no regularization)")
+        ("eta", po::value<double>()->default_value(0.1f), "Eta for SGD")
+        ("source_embeddings,f", po::value<string>(), "File containing source embeddings (if unset, random vectors will be used)")
+        ("target_embeddings,e", po::value<string>(), "File containing target embeddings (if unset, random vectors will be used)")
+        ("random_seed,s", po::value<unsigned>(), "Random seed")
+        ("diagonal_tension,T", po::value<double>()->default_value(4.0), "How sharp or flat around the diagonal is the alignment distribution (0 = uniform, >0 sharpens)")
+        ("testset,x", po::value<string>(), "After training completes, compute the log likelihood of this set of sentence pairs under the learned model");
+  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 (argc < 2 || conf->count("help")) {
+    cerr << "Usage " << argv[0] << " [OPTIONS] -i corpus.fr-en\n";
+    cerr << dcmdline_options << endl;
+    return false;
+  }
+  return true;
+}
+
+void Normalize(RVector* v) {
+  double norm = v->norm();
+  assert(norm > 0.0f);
+  *v /= norm;
+}
+
+void Flatten(const TMatrix& m, vector<double>* v) {
+  unsigned c = 0;
+  v->resize(kDIMENSIONS * kDIMENSIONS);
+  for (unsigned i = 0; i < kDIMENSIONS; ++i)
+    for (unsigned j = 0; j < kDIMENSIONS; ++j) {
+      assert(boost::math::isfinite(m(i, j)));
+      (*v)[c++] = m(i,j);
+    }
+}
+
+void Unflatten(const vector<double>& v, TMatrix* m) {
+  unsigned c = 0;
+  for (unsigned i = 0; i < kDIMENSIONS; ++i)
+    for (unsigned j = 0; j < kDIMENSIONS; ++j) {
+      assert(boost::math::isfinite(v[c]));
+      (*m)(i, j) = v[c++];
+    }
+}
+
+double ApplyRegularization(const double C,
+                           const vector<double>& weights,
+                           vector<double>* g) {
+  assert(weights.size() == g->size());
+  double reg = 0;
+  for (size_t i = 0; i < weights.size(); ++i) {
+    const double& w_i = weights[i];
+    double& g_i = (*g)[i];
+    reg += C * w_i * w_i;
+    g_i += 2 * C * w_i;
+  }
+  return reg;
+}
+
+void LoadEmbeddings(const string& filename, vector<RVector>* pv) {
+  vector<RVector>& v = *pv;
+  cerr << "Reading embeddings from " << filename << " ...\n";
+  ReadFile rf(filename);
+  istream& in = *rf.stream();
+  string line;
+  unsigned lc = 0;
+  while(getline(in, line)) {
+    ++lc;
+    size_t cur = line.find(' ');
+    if (cur == string::npos || cur == 0) {
+      cerr << "Parse error reading line " << lc << ":\n" << line << endl;
+      abort();
+    }
+    WordID w = TD::Convert(line.substr(0, cur));
+    if (w >= v.size()) continue;
+    RVector& curv = v[w];
+    line[cur] = 0;
+    size_t start = cur + 1;
+    cur = start + 1;
+    size_t c = 0;
+    while(cur < line.size()) {
+      if (line[cur] == ' ') {
+        line[cur] = 0;
+        curv[c++] = strtod(&line[start], NULL);
+        start = cur + 1;
+        cur = start;
+        if (c == kDIMENSIONS) break;
+      }
+      ++cur;
+    }
+    if (c < kDIMENSIONS && cur != start) {
+      if (cur < line.size()) line[cur] = 0;
+      curv[c++] = strtod(&line[start], NULL);
+    }
+    if (c != kDIMENSIONS) {
+      static bool first = true;
+      if (first) {
+        cerr << " read " << c << " dimensions from embedding file, but built with " << kDIMENSIONS << " (filling in with random values)\n";
+        first = false;
+      }
+      for (; c < kDIMENSIONS; ++c) curv[c] = rand();
+    }
+    if (c == kDIMENSIONS && cur != line.size()) {
+      static bool first = true;
+      if (first) {
+        cerr << " embedding file contains more dimensions than configured with, truncating.\n";
+        first = false;
+      }
+    }
+  }
+}
+
+int main(int argc, char** argv) {
+#ifdef HAVE_MPI
+  std::cerr << "**MPI enabled.\n";
+  mpi::environment env(argc, argv);
+  mpi::communicator world;
+  const int size = world.size(); 
+  const int rank = world.rank();
+#else
+  std::cerr << "**MPI disabled.\n";
+  const int rank = 0;
+  const int size = 1;
+#endif
+  po::variables_map conf;
+  if (!InitCommandLine(argc, argv, &conf)) return 1;
+  const string fname = conf["input"].as<string>();
+  const double reg_strength = conf["regularization_strength"].as<double>();
+  const bool has_l2 = reg_strength;
+  assert(reg_strength >= 0.0f);
+  const int ITERATIONS = conf["iterations"].as<unsigned>();
+  const double eta = conf["eta"].as<double>();
+  const double diagonal_tension = conf["diagonal_tension"].as<double>();
+  bool SGD = false;
+  if (diagonal_tension < 0.0) {
+    cerr << "Invalid value for diagonal_tension: must be >= 0\n";
+    return 1;
+  }
+  string testset;
+  if (conf.count("testset")) testset = conf["testset"].as<string>();
+
+  unsigned lc = 0;
+  vector<double> unnormed_a_i;
+  bool flag = false;
+  vector<vector<WordID> > srcs, trgs;
+  vector<WordID> vocab_e;
+  {
+    set<WordID> svocab_e, svocab_f;
+    CorpusTools::ReadFromFile(fname, &srcs, NULL, &trgs, &svocab_e, rank, size);
+    copy(svocab_e.begin(), svocab_e.end(), back_inserter(vocab_e));
+  }
+  cerr << "Number of target word types: " << vocab_e.size() << endl;
+  const double num_examples = lc;
+
+  boost::shared_ptr<LBFGSOptimizer> lbfgs;
+  if (rank == 0)
+    lbfgs.reset(new LBFGSOptimizer(kDIMENSIONS * kDIMENSIONS, 100));
+  r_trg.resize(TD::NumWords() + 1);
+  r_src.resize(TD::NumWords() + 1);
+  vector<set<unsigned> > trg_pos(TD::NumWords() + 1);
+
+  if (conf.count("random_seed")) {
+    srand(conf["random_seed"].as<unsigned>());
+  } else {
+    unsigned seed = time(NULL) + rank * 100;
+    cerr << "Random seed: " << seed << endl;
+    srand(seed);
+  }
+  
+  TMatrix t = TMatrix::Zero();
+  if (rank == 0) {
+    t = TMatrix::Random() / 50.0;
+    for (unsigned i = 1; i < r_trg.size(); ++i) {
+      r_trg[i] = RVector::Random();
+      r_src[i] = RVector::Random();
+    }
+    if (conf.count("source_embeddings"))
+      LoadEmbeddings(conf["source_embeddings"].as<string>(), &r_src);
+    if (conf.count("target_embeddings"))
+      LoadEmbeddings(conf["target_embeddings"].as<string>(), &r_trg);
+  }
+
+  // do optimization
+  TMatrix g = TMatrix::Zero();
+  vector<TMatrix> exp_src;
+  vector<double> z_src;
+  vector<double> flat_g, flat_t, rcv_grad;
+  Flatten(t, &flat_t);
+  bool converged = false;
+#if HAVE_MPI
+  mpi::broadcast(world, &flat_t[0], flat_t.size(), 0);
+  mpi::broadcast(world, r_trg, 0);
+  mpi::broadcast(world, r_src, 0);
+#endif
+  cerr << "rank=" << rank << ": " << r_trg[0][4] << endl;
+  for (int iter = 0; !converged && iter < ITERATIONS; ++iter) {
+    if (rank == 0) cerr << "ITERATION " << (iter + 1) << endl;
+    Unflatten(flat_t, &t);
+    double likelihood = 0;
+    double denom = 0.0;
+    lc = 0;
+    flag = false;
+    g *= 0;
+    for (unsigned i = 0; i < srcs.size(); ++i) {
+      const vector<WordID>& src = srcs[i];
+      const vector<WordID>& trg = trgs[i];
+      ++lc;
+      if (rank == 0 && lc % 1000 == 0) { cerr << '.'; flag = true; }
+      if (rank == 0 && lc %50000 == 0) { cerr << " [" << lc << "]\n" << flush; flag = false; }
+      denom += trg.size();
+
+      exp_src.clear(); exp_src.resize(src.size(), TMatrix::Zero());
+      z_src.clear(); z_src.resize(src.size(), 0.0);
+      Array2D<TMatrix> exp_refs(src.size(), trg.size(), TMatrix::Zero());
+      Array2D<double> z_refs(src.size(), trg.size(), 0.0);
+      for (unsigned j = 0; j < trg.size(); ++j)
+        trg_pos[trg[j]].insert(j);
+
+      for (unsigned i = 0; i < src.size(); ++i) {
+        const RVector& r_s = r_src[src[i]];
+        const RTVector pred = r_s.transpose() * t;
+        TMatrix& exp_m = exp_src[i];
+        double& z = z_src[i];
+        for (unsigned k = 0; k < vocab_e.size(); ++k) {
+          const WordID v_k = vocab_e[k];
+          const RVector& r_t = r_trg[v_k];
+          const double dot_prod = pred * r_t;
+          const double u = exp(dot_prod);
+          z += u;
+          const TMatrix v = r_s * r_t.transpose() * u;
+          exp_m += v;
+          set<unsigned>& ref_locs = trg_pos[v_k];
+          if (!ref_locs.empty()) {
+            for (set<unsigned>::iterator it = ref_locs.begin(); it != ref_locs.end(); ++it) {
+              TMatrix& exp_ref_ij = exp_refs(i, *it);
+              double& z_ref_ij = z_refs(i, *it);
+              z_ref_ij += u;
+              exp_ref_ij += v;
+            }
+          }
+        }
+      }
+      for (unsigned j = 0; j < trg.size(); ++j)
+        trg_pos[trg[j]].clear();
+
+      // model expectations for a single target generation with
+      // uniform alignment prior
+      // TODO: when using a non-uniform alignment, m_exp will be
+      // a function of j (below)
+      double m_z = 0;
+      TMatrix m_exp = TMatrix::Zero();
+      for (unsigned i = 0; i < src.size(); ++i) {
+        m_exp += exp_src[i];
+        m_z += z_src[i];
+      }
+      m_exp /= m_z;
+
+      Array2D<bool> al(src.size(), trg.size(), false);
+      for (unsigned j = 0; j < trg.size(); ++j) {
+        double ref_z = 0;
+        TMatrix ref_exp = TMatrix::Zero();
+        int max_i = 0;
+        double max_s = -9999999;
+        for (unsigned i = 0; i < src.size(); ++i) {
+          ref_exp += exp_refs(i, j);
+          ref_z += z_refs(i, j);
+          if (log(z_refs(i, j)) > max_s) {
+            max_s = log(z_refs(i, j));
+            max_i = i;
+          }
+          // TODO handle alignment prob
+        }
+        if (ref_z <= 0) { 
+          cerr << "TRG=" << TD::Convert(trg[j]) << endl;
+          cerr << " LINE=" << lc << " (RANK=" << rank << "/" << size << ")" << endl;
+          cerr << " REF_EXP=\n" << ref_exp << endl;
+          cerr << " M_EXP=\n" << m_exp << endl;
+          abort();
+        }
+        al(max_i, j) = true;
+        ref_exp /= ref_z;
+        g += m_exp - ref_exp;
+        likelihood += log(ref_z) - log(m_z);
+        if (SGD) {
+          t -= g * eta / num_examples;
+          g *= 0;
+        }
+      }
+      
+      if (rank == 0 && (iter == (ITERATIONS - 1) || lc < 12)) { cerr << al << endl; }
+    }
+    if (flag && rank == 0) { cerr << endl; }
+
+    double obj = 0;
+    if (!SGD) {
+      Flatten(g, &flat_g);
+      obj = -likelihood;
+#if HAVE_MPI
+      rcv_grad.resize(flat_g.size(), 0.0);
+      mpi::reduce(world, &flat_g[0], flat_g.size(), &rcv_grad[0], plus<double>(), 0);
+      swap(flat_g, rcv_grad);
+      rcv_grad.clear();
+
+      double to = 0;
+      mpi::reduce(world, obj, to, plus<double>(), 0);
+      obj = to;
+      double tlh = 0;
+      mpi::reduce(world, likelihood, tlh, plus<double>(), 0);
+      likelihood = tlh;
+      double td = 0;
+      mpi::reduce(world, denom, td, plus<double>(), 0);
+      denom = td;
+#endif
+    }
+
+    if (rank == 0) {
+      double gn = 0;
+      for (unsigned i = 0; i < flat_g.size(); ++i)
+        gn += flat_g[i]*flat_g[i];
+      const double base2_likelihood = likelihood / log(2);
+      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;
+      cerr << "     gradient norm: " << sqrt(gn) << endl;
+      if (!SGD) {
+        if (has_l2) {
+          const double r = ApplyRegularization(reg_strength,
+                                               flat_t,
+                                               &flat_g);
+          obj += r;
+          cerr << "    regularization: " << r << endl;
+        }
+        lbfgs->Optimize(obj, flat_g, &flat_t);
+        converged = (lbfgs->HasConverged());
+      }
+    }
+#ifdef HAVE_MPI
+    mpi::broadcast(world, &flat_t[0], flat_t.size(), 0);
+    mpi::broadcast(world, converged, 0);
+#endif
+  }
+  if (rank == 0)
+    cerr << "TRANSLATION MATRIX:" << endl << t << endl;
+  return 0;
+}
+
+#endif
+