remove dead code, add adagrad crf learner

3 files changed, 362 insertions, 3 deletions

diff --git a/training/crf/Makefile.am b/training/crf/Makefile.am
index 4a8c30fd..cd82161f 100644
--- a/training/crf/Makefile.am
+++ b/training/crf/Makefile.am
@@ -1,5 +1,6 @@
 bin_PROGRAMS = \
   mpi_batch_optimize \
+  mpi_adagrad_optimize \
   mpi_compute_cllh \
   mpi_extract_features \
   mpi_extract_reachable \
@@ -10,6 +11,9 @@ bin_PROGRAMS = \
 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_adagrad_optimize_SOURCES = mpi_adagrad_optimize.cc cllh_observer.cc cllh_observer.h
+mpi_adagrad_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
+
 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/mpi_adagrad_optimize.cc b/training/crf/mpi_adagrad_optimize.cc
new file mode 100644
index 00000000..05e7c35f
--- /dev/null
+++ b/training/crf/mpi_adagrad_optimize.cc
@@ -0,0 +1,355 @@
+#include <sstream>
+#include <iostream>
+#include <fstream>
+#include <vector>
+#include <cassert>
+#include <cmath>
+#include <ctime>
+
+#include <boost/program_options.hpp>
+#include <boost/program_options/variables_map.hpp>
+#include <boost/shared_ptr.hpp>
+
+#include "config.h"
+#include "stringlib.h"
+#include "verbose.h"
+#include "cllh_observer.h"
+#include "hg.h"
+#include "prob.h"
+#include "inside_outside.h"
+#include "ff_register.h"
+#include "decoder.h"
+#include "filelib.h"
+#include "online_optimizer.h"
+#include "fdict.h"
+#include "weights.h"
+#include "sparse_vector.h"
+#include "sampler.h"
+
+#ifdef HAVE_MPI
+#include <boost/mpi/timer.hpp>
+#include <boost/mpi.hpp>
+namespace mpi = boost::mpi;
+#endif
+
+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()
+        ("weights,w",po::value<string>(), "Initial feature weights")
+        ("training_data,d",po::value<string>(), "Training data corpus")
+        ("test_data,t",po::value<string>(), "(optional) Test data")
+        ("decoder_config,c",po::value<string>(), "Decoder configuration file")
+        ("minibatch_size_per_proc,s", po::value<unsigned>()->default_value(8),
+            "Number of training instances evaluated per processor in each minibatch")
+        ("max_passes", po::value<double>()->default_value(20.0), "Maximum number of passes through the data")
+        ("max_walltime", po::value<unsigned>(), "Walltime to run (in minutes)")
+        ("write_every_n_minibatches", po::value<unsigned>()->default_value(100), "Write weights every N minibatches processed")
+        ("random_seed,S", po::value<uint32_t>(), "Random seed")
+        ("regularization,r", po::value<string>()->default_value("none"),
+            "Regularization 'none', 'l1', or 'l2'")
+        ("regularization_strength,C", po::value<double>(), "Regularization strength")
+        ("eta,e", po::value<double>()->default_value(1.0), "Initial learning rate (eta)");
+  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("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, vector<int>* order) {
+  ReadFile rf(fname);
+  istream& in = *rf.stream();
+  string line;
+  int id = 0;
+  while(getline(in, line)) {
+    if (id % size == rank) {
+      c->push_back(line);
+      order->push_back(id);
+    }
+    ++id;
+  }
+}
+
+static const double kMINUS_EPSILON = -1e-6;
+
+struct TrainingObserver : public DecoderObserver {
+  void Reset() {
+    acc_grad.clear();
+    acc_obj = 0;
+    total_complete = 0;
+  } 
+
+  virtual void NotifyDecodingStart(const SentenceMetadata&) {
+    cur_model_exp.clear();
+    cur_obj = 0;
+    state = 1;
+  }
+
+  // compute model expectations, denominator of objective
+  virtual void NotifyTranslationForest(const SentenceMetadata&, Hypergraph* hg) {
+    assert(state == 1);
+    state = 2;
+    const prob_t z = InsideOutside<prob_t,
+                                   EdgeProb,
+                                   SparseVector<prob_t>,
+                                   EdgeFeaturesAndProbWeightFunction>(*hg, &cur_model_exp);
+    cur_obj = log(z);
+    cur_model_exp /= z;
+  }
+
+  // compute "empirical" expectations, numerator of objective
+  virtual void NotifyAlignmentForest(const SentenceMetadata&, Hypergraph* hg) {
+    assert(state == 2);
+    state = 3;
+    SparseVector<prob_t> ref_exp;
+    const prob_t ref_z = InsideOutside<prob_t,
+                                       EdgeProb,
+                                       SparseVector<prob_t>,
+                                       EdgeFeaturesAndProbWeightFunction>(*hg, &ref_exp);
+    ref_exp /= ref_z;
+
+    double log_ref_z;
+#if 0
+    if (crf_uniform_empirical) {
+      log_ref_z = ref_exp.dot(feature_weights);
+    } else {
+      log_ref_z = log(ref_z);
+    }
+#else
+    log_ref_z = log(ref_z);
+#endif
+
+    // rounding errors means that <0 is too strict
+    if ((cur_obj - log_ref_z) < kMINUS_EPSILON) {
+      cerr << "DIFF. ERR! log_model_z < log_ref_z: " << cur_obj << " " << log_ref_z << endl;
+      exit(1);
+    }
+    assert(!std::isnan(log_ref_z));
+    ref_exp -= cur_model_exp;
+    acc_grad += ref_exp;
+    acc_obj += (cur_obj - log_ref_z);
+  }
+
+  virtual void NotifyDecodingComplete(const SentenceMetadata&) {
+    if (state == 3) {
+      ++total_complete;
+    } else {
+    }
+  }
+
+  void GetGradient(SparseVector<double>* g) const {
+    g->clear();
+#if HAVE_CXX11
+    for (auto& gi : acc_grad) {
+#else
+    for (FastSparseVector<prob_t>::const_iterator it = acc_grad.begin(); it != acc_grad.end(); ++it) {
+      pair<unsigned, double>& gi = *it;
+#endif
+      g->set_value(gi.first, -gi.second.as_float());
+    }
+  }
+
+  int total_complete;
+  SparseVector<prob_t> cur_model_exp;
+  SparseVector<prob_t> acc_grad;
+  double acc_obj;
+  double cur_obj;
+  int state;
+};
+
+#ifdef HAVE_MPI
+namespace boost { namespace mpi {
+  template<>
+  struct is_commutative<std::plus<SparseVector<double> >, SparseVector<double> > 
+    : mpl::true_ { };
+} } // end namespace boost::mpi
+#endif
+
+class AdaGradOptimizer {
+ public:
+  explicit AdaGradOptimizer(double e) :
+      eta(e),
+      G() {}
+  void update(const SparseVector<double>& g, vector<double>* x) {
+    if (x->size() > G.size()) G.resize(x->size(), 0.0);
+#if HAVE_CXX11
+    for (auto& gi : g) {
+#else
+    for (SparseVector<double>::const_iterator it = g.begin(); it != g.end(); ++it) {
+      const pair<unsigne,double>& gi = *it;
+#endif
+      if (gi.second) {
+        G[gi.first] += gi.second * gi.second;
+        (*x)[gi.first] -= eta / sqrt(G[gi.first]) * gi.second;
+      }
+    }
+  }
+  const double eta;
+  vector<double> G;
+};
+
+unsigned non_zeros(const vector<double>& x) {
+  unsigned nz = 0;
+  for (unsigned i = 0; i < x.size(); ++i)
+    if (x[i]) ++nz;
+  return nz;
+}
+
+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
+  if (size > 1) SetSilent(true);  // turn off verbose decoder output
+  register_feature_functions();
+
+  po::variables_map conf;
+  if (!InitCommandLine(argc, argv, &conf))
+    return 1;
+
+  ReadFile ini_rf(conf["decoder_config"].as<string>());
+  Decoder decoder(ini_rf.stream());
+
+  // load initial weights
+  vector<weight_t> init_weights;
+  if (conf.count("input_weights"))
+    Weights::InitFromFile(conf["input_weights"].as<string>(), &init_weights);
+
+  vector<string> corpus, test_corpus;
+  vector<int> ids;
+  ReadTrainingCorpus(conf["training_data"].as<string>(), rank, size, &corpus, &ids);
+  assert(corpus.size() > 0);
+  if (conf.count("test_data"))
+    ReadTrainingCorpus(conf["test_data"].as<string>(), rank, size, &corpus, &ids);
+
+  const unsigned size_per_proc = conf["minibatch_size_per_proc"].as<unsigned>();
+  if (size_per_proc > corpus.size()) {
+    cerr << "Minibatch size must be smaller than corpus size!\n";
+    return 1;
+  }
+  const double minibatch_size = size_per_proc * size;
+
+  size_t total_corpus_size = 0;
+#ifdef HAVE_MPI
+  reduce(world, corpus.size(), total_corpus_size, std::plus<size_t>(), 0);
+#else
+  total_corpus_size = corpus.size();
+#endif
+
+  if (rank == 0)
+    cerr << "Total corpus size: " << total_corpus_size << endl;
+
+  boost::shared_ptr<MT19937> rng;
+  if (conf.count("random_seed"))
+    rng.reset(new MT19937(conf["random_seed"].as<uint32_t>()));
+  else
+    rng.reset(new MT19937);
+
+  double passes_per_minibatch = static_cast<double>(size_per_proc) / total_corpus_size;
+
+  int write_weights_every_ith = conf["write_every_n_minibatches"].as<unsigned>();
+
+  unsigned max_iteration = conf["max_passes"].as<double>() / passes_per_minibatch;
+  ++max_iteration;
+  if (rank == 0)
+    cerr << "Max passes through data = " << conf["max_passes"].as<double>() << endl
+         << "    --> max minibatches = " << max_iteration << endl;
+  unsigned timeout = 0;
+  if (conf.count("max_walltime"))
+    timeout = 60 * conf["max_walltime"].as<unsigned>();
+  vector<weight_t>& lambdas = decoder.CurrentWeightVector();
+  if (init_weights.size()) {
+    lambdas.swap(init_weights);
+    init_weights.clear();
+  }
+
+  AdaGradOptimizer adagrad(conf["eta"].as<double>());
+  int iter = -1;
+  bool converged = false;
+
+  TrainingObserver observer;
+  ConditionalLikelihoodObserver cllh_observer;
+
+  const time_t start_time = time(NULL);
+  while (!converged) {
+#ifdef HAVE_MPI
+      mpi::timer timer;
+#endif
+      ++iter;
+      observer.Reset();
+      if (rank == 0) {
+        converged = (iter == max_iteration);
+        string fname = "weights.cur.gz";
+        if (iter % write_weights_every_ith == 0) {
+          ostringstream o; o << "weights." << iter << ".gz";
+          fname = o.str();
+        }
+        const time_t cur_time = time(NULL);
+        if (timeout && ((cur_time - start_time) > timeout)) {
+          converged = true;
+          fname = "weights.final.gz";
+        }
+        ostringstream vv;
+        double minutes = (cur_time - start_time) / 60.0;
+        vv << "total walltime=" << minutes << "min iter=" << iter << "  minibatch=" << size_per_proc << " sentences/proc x " << size << " procs.   num_feats=" << non_zeros(lambdas) << '/' << FD::NumFeats() << "   passes_thru_data=" << (iter * size_per_proc / static_cast<double>(corpus.size()));
+        const string svv = vv.str();
+        cerr << svv << endl;
+        Weights::WriteToFile(fname, lambdas, true, &svv);
+      }
+
+      for (int i = 0; i < size_per_proc; ++i) {
+        int ei = corpus.size() * rng->next();
+        int id = ids[ei];
+        decoder.SetId(id);
+        decoder.Decode(corpus[ei], &observer);
+      }
+      SparseVector<double> local_grad, g;
+      observer.GetGradient(&local_grad);
+#ifdef HAVE_MPI
+      reduce(world, local_grad, g, std::plus<SparseVector<double> >(), 0);
+#else
+      g.swap(local_grad);
+#endif
+      local_grad.clear();
+      if (rank == 0) {
+        g /= minibatch_size;
+        lambdas.resize(FD::NumFeats(), 0.0); // might have seen new features
+        adagrad.update(g, &lambdas);
+        Weights::SanityCheck(lambdas);
+        Weights::ShowLargestFeatures(lambdas);
+      }
+#ifdef HAVE_MPI
+      broadcast(world, lambdas, 0);
+      broadcast(world, converged, 0);
+      world.barrier();
+      if (rank == 0) { cerr << "  ELAPSED TIME THIS ITERATION=" << timer.elapsed() << endl; }
+#endif
+  }
+  cerr << "CONVERGED = " << converged << endl;
+  cerr << "EXITING...\n";
+  return 0;
+}
+
diff --git a/training/crf/mpi_batch_optimize.cc b/training/crf/mpi_batch_optimize.cc
index 2eff07e4..da1845b1 100644
--- a/training/crf/mpi_batch_optimize.cc
+++ b/training/crf/mpi_batch_optimize.cc
@@ -97,14 +97,14 @@ struct TrainingObserver : public DecoderObserver {
       (*g)[it->first] = it->second.as_float();
   }
 
-  virtual void NotifyDecodingStart(const SentenceMetadata& smeta) {
+  virtual void NotifyDecodingStart(const SentenceMetadata&) {
     cur_model_exp.clear();
     cur_obj = 0;
     state = 1;
   }
 
   // compute model expectations, denominator of objective
-  virtual void NotifyTranslationForest(const SentenceMetadata& smeta, Hypergraph* hg) {
+  virtual void NotifyTranslationForest(const SentenceMetadata&, Hypergraph* hg) {
     assert(state == 1);
     state = 2;
     const prob_t z = InsideOutside<prob_t,
@@ -149,7 +149,7 @@ struct TrainingObserver : public DecoderObserver {
     trg_words += smeta.GetReference().size();
   }
 
-  virtual void NotifyDecodingComplete(const SentenceMetadata& smeta) {
+  virtual void NotifyDecodingComplete(const SentenceMetadata&) {
     if (state == 3) {
       ++total_complete;
     } else {