Merge branch 'master' of git://github.com/redpony/cdec

1 files changed, 384 insertions, 0 deletions

diff --git a/training/crf/mpi_online_optimize.cc b/training/crf/mpi_online_optimize.cc
new file mode 100644
index 00000000..9e1ae34c
--- /dev/null
+++ b/training/crf/mpi_online_optimize.cc
@@ -0,0 +1,384 @@
+#include <sstream>
+#include <iostream>
+#include <fstream>
+#include <vector>
+#include <cassert>
+#include <cmath>
+#include <tr1/memory>
+#include <ctime>
+
+#include <boost/program_options.hpp>
+#include <boost/program_options/variables_map.hpp>
+
+#include "stringlib.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 "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()
+        ("input_weights,w",po::value<string>(),"Input feature weights file")
+        ("frozen_features,z",po::value<string>(), "List of features not to optimize")
+        ("training_data,t",po::value<string>(),"Training data corpus")
+        ("training_agenda,a",po::value<string>(), "Text file listing a series of configuration files and the number of iterations to train using each configuration successively")
+        ("minibatch_size_per_proc,s", po::value<unsigned>()->default_value(5), "Number of training instances evaluated per processor in each minibatch")
+        ("optimization_method,m", po::value<string>()->default_value("sgd"), "Optimization method (sgd)")
+        ("max_walltime", po::value<unsigned>(), "Maximum walltime to run (in minutes)")
+        ("random_seed,S", po::value<uint32_t>(), "Random seed (if not specified, /dev/random will be used)")
+        ("eta_0,e", po::value<double>()->default_value(0.2), "Initial learning rate for SGD (eta_0)")
+        ("L1,1","Use L1 regularization")
+        ("regularization_strength,C", po::value<double>()->default_value(1.0), "Regularization strength (C)");
+  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("training_agenda")) {
+    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(in) {
+    getline(in, line);
+    if (!in) break;
+    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;
+  } 
+
+  void SetLocalGradientAndObjective(vector<double>* g, double* o) const {
+    *o = acc_obj;
+    for (SparseVector<prob_t>::const_iterator it = acc_grad.begin(); it != acc_grad.end(); ++it)
+      (*g)[it->first] = it->second.as_float();
+  }
+
+  virtual void NotifyDecodingStart(const SentenceMetadata& smeta) {
+    cur_model_exp.clear();
+    cur_obj = 0;
+    state = 1;
+  }
+
+  // compute model expectations, denominator of objective
+  virtual void NotifyTranslationForest(const SentenceMetadata& smeta, 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& smeta, 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& smeta) {
+    if (state == 3) {
+      ++total_complete;
+    } else {
+    }
+  }
+
+  void GetGradient(SparseVector<double>* g) const {
+    g->clear();
+    for (SparseVector<prob_t>::const_iterator it = acc_grad.begin(); it != acc_grad.end(); ++it)
+      g->set_value(it->first, it->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
+
+bool LoadAgenda(const string& file, vector<pair<string, int> >* a) {
+  ReadFile rf(file);
+  istream& in = *rf.stream();
+  string line;
+  while(in) {
+    getline(in, line);
+    if (!in) break;
+    if (line.empty()) continue;
+    if (line[0] == '#') continue;
+    int sc = 0;
+    if (line.size() < 3) return false;
+    for (int i = 0; i < line.size(); ++i) { if (line[i] == ' ') ++sc; }
+    if (sc != 1) { cerr << "Too many spaces in line: " << line << endl; return false; }
+    size_t d = line.find(" ");
+    pair<string, int> x;
+    x.first = line.substr(0,d);
+    x.second = atoi(line.substr(d+1).c_str());
+    a->push_back(x);
+    if (!FileExists(x.first)) {
+      cerr << "Can't find file " << x.first << endl;
+      return false;
+    }
+  }
+  return true;
+}
+
+int main(int argc, char** argv) {
+  cerr << "THIS SOFTWARE IS DEPRECATED YOU SHOULD USE mpi_flex_optimize\n";
+#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();
+  std::tr1::shared_ptr<MT19937> rng;
+
+  po::variables_map conf;
+  if (!InitCommandLine(argc, argv, &conf))
+    return 1;
+
+  vector<pair<string, int> > agenda;
+  if (!LoadAgenda(conf["training_agenda"].as<string>(), &agenda))
+    return 1;
+  if (rank == 0)
+    cerr << "Loaded agenda defining " << agenda.size() << " training epochs\n";
+
+  assert(agenda.size() > 0);
+
+  if (1) {  // hack to load the feature hash functions -- TODO this should not be in cdec.ini
+    const string& cur_config = agenda[0].first;
+    const unsigned max_iteration = agenda[0].second;
+    ReadFile ini_rf(cur_config);
+    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<int> frozen_fids;
+  if (conf.count("frozen_features")) {
+    ReadFile rf(conf["frozen_features"].as<string>());
+    istream& in = *rf.stream();
+    string line;
+    while(in) {
+      getline(in, line);
+      if (line.empty()) continue;
+      if (line[0] == ' ' || line[line.size() - 1] == ' ') { line = Trim(line); }
+      frozen_fids.push_back(FD::Convert(line));
+    }
+    if (rank == 0) cerr << "Freezing " << frozen_fids.size() << " features.\n";
+  }
+
+  vector<string> corpus;
+  vector<int> ids;
+  ReadTrainingCorpus(conf["training_data"].as<string>(), rank, size, &corpus, &ids);
+  assert(corpus.size() > 0);
+
+  std::tr1::shared_ptr<OnlineOptimizer> o;
+  std::tr1::shared_ptr<LearningRateSchedule> lr;
+
+  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;
+  }
+
+  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;
+    const unsigned batch_size = size_per_proc * size;
+    // TODO config
+    lr.reset(new ExponentialDecayLearningRate(batch_size, conf["eta_0"].as<double>()));
+
+    const string omethod = conf["optimization_method"].as<string>();
+    if (omethod == "sgd") {
+      const double C = conf["regularization_strength"].as<double>();
+      o.reset(new CumulativeL1OnlineOptimizer(lr, total_corpus_size, C, frozen_fids));
+    } else {
+      assert(!"fail");
+    }
+  }
+  if (conf.count("random_seed"))
+    rng.reset(new MT19937(conf["random_seed"].as<uint32_t>()));
+  else
+    rng.reset(new MT19937);
+
+  SparseVector<double> x;
+  Weights::InitSparseVector(init_weights, &x);
+  TrainingObserver observer;
+
+  int write_weights_every_ith = 100; // TODO configure
+  int titer = -1;
+
+  unsigned timeout = 0;
+  if (conf.count("max_walltime")) timeout = 60 * conf["max_walltime"].as<unsigned>();
+  const time_t start_time = time(NULL);
+  for (int ai = 0; ai < agenda.size(); ++ai) {
+    const string& cur_config = agenda[ai].first;
+    const unsigned max_iteration = agenda[ai].second;
+    if (rank == 0)
+      cerr << "STARTING TRAINING EPOCH " << (ai+1) << ". CONFIG=" << cur_config << endl;
+    // load cdec.ini and set up decoder
+    ReadFile ini_rf(cur_config);
+    Decoder decoder(ini_rf.stream());
+    vector<weight_t>& lambdas = decoder.CurrentWeightVector();
+    if (ai == 0) { lambdas.swap(init_weights); init_weights.clear(); }
+
+    if (rank == 0)
+      o->ResetEpoch(); // resets the learning rate-- TODO is this good?
+
+    int iter = -1;
+    bool converged = false;
+    while (!converged) {
+#ifdef HAVE_MPI
+      mpi::timer timer;
+#endif
+      x.init_vector(&lambdas);
+      ++iter; ++titer;
+      observer.Reset();
+      if (rank == 0) {
+        converged = (iter == max_iteration);
+        Weights::SanityCheck(lambdas);
+        static int cc = 0; ++cc; if (cc > 1) { Weights::ShowLargestFeatures(lambdas); }
+        string fname = "weights.cur.gz";
+        if (iter % write_weights_every_ith == 0) {
+          ostringstream o; o << "weights.epoch_" << (ai+1) << '.' << iter << ".gz";
+          fname = o.str();
+        }
+        const time_t cur_time = time(NULL);
+        if (timeout) {
+          if ((cur_time - start_time) > timeout) converged = true;
+        }
+        if (converged && ((ai+1)==agenda.size())) { fname = "weights.final.gz"; }
+        ostringstream vv;
+        double minutes = (cur_time - start_time) / 60.0;
+        vv << "total walltime=" << minutes << "min iter=" << titer << " (of current config iter=" << iter << ")  minibatch=" << size_per_proc << " sentences/proc x " << size << " procs.   num_feats=" << x.size() << '/' << FD::NumFeats() << "   passes_thru_data=" << (titer * size_per_proc / static_cast<double>(corpus.size())) << "   eta=" << lr->eta(titer);
+        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 /= (size_per_proc * size);
+        o->UpdateWeights(g, FD::NumFeats(), &x);
+      }
+#ifdef HAVE_MPI
+      broadcast(world, x, 0);
+      broadcast(world, converged, 0);
+      world.barrier();
+      if (rank == 0) { cerr << "  ELAPSED TIME THIS ITERATION=" << timer.elapsed() << endl; }
+#endif
+    }
+  }
+  return 0;
+}