major restructure of the training code

1 files changed, 386 insertions, 0 deletions

diff --git a/training/crf/mpi_flex_optimize.cc b/training/crf/mpi_flex_optimize.cc
new file mode 100644
index 00000000..b52decdc
--- /dev/null
+++ b/training/crf/mpi_flex_optimize.cc
@@ -0,0 +1,386 @@
+#include <sstream>
+#include <iostream>
+#include <fstream>
+#include <vector>
+#include <cassert>
+#include <cmath>
+
+#include <boost/shared_ptr.hpp>
+#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 "optimize.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()
+        ("cdec_config,c",po::value<string>(),"Decoder configuration file")
+        ("weights,w",po::value<string>(),"Initial feature weights")
+        ("training_data,d",po::value<string>(),"Training data")
+        ("minibatch_size_per_proc,s", po::value<unsigned>()->default_value(6), "Number of training instances evaluated per processor in each minibatch")
+        ("minibatch_iterations,i", po::value<unsigned>()->default_value(10), "Number of optimization iterations per minibatch")
+        ("iterations,I", po::value<unsigned>()->default_value(50), "Number of passes through the training data before termination")
+        ("regularization_strength,C", po::value<double>()->default_value(0.2), "Regularization strength")
+        ("time_series_strength,T", po::value<double>()->default_value(0.0), "Time series regularization strength")
+        ("random_seed,S", po::value<uint32_t>(), "Random seed (if not specified, /dev/random will be used)")
+        ("lbfgs_memory_buffers,M", po::value<unsigned>()->default_value(10), "Number of memory buffers for LBFGS history");
+  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("cdec_config")) {
+    cerr << "LBFGS minibatch online optimizer (MPI support "
+#if HAVE_MPI
+         << "enabled"
+#else
+         << "not enabled"
+#endif
+         << ")\n" << 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 CopyHGsObserver : public DecoderObserver {
+  Hypergraph* hg_;
+  Hypergraph* gold_hg_;
+
+  // this can free up some memory
+  void RemoveRules(Hypergraph* h) {
+    for (unsigned i = 0; i < h->edges_.size(); ++i)
+      h->edges_[i].rule_.reset();
+  }
+
+  void SetCurrentHypergraphs(Hypergraph* h, Hypergraph* gold_h) {
+    hg_ = h;
+    gold_hg_ = gold_h;
+  }
+
+  virtual void NotifyDecodingStart(const SentenceMetadata&) {
+    state = 1;
+  }
+
+  // compute model expectations, denominator of objective
+  virtual void NotifyTranslationForest(const SentenceMetadata&, Hypergraph* hg) {
+    *hg_ = *hg;
+    RemoveRules(hg_);
+    assert(state == 1);
+    state = 2;
+  }
+
+  // compute "empirical" expectations, numerator of objective
+  virtual void NotifyAlignmentForest(const SentenceMetadata&, Hypergraph* hg) {
+    assert(state == 2);
+    state = 3;
+    *gold_hg_ = *hg;
+    RemoveRules(gold_hg_);
+  }
+
+  virtual void NotifyDecodingComplete(const SentenceMetadata&) {
+    if (state == 3) {
+    } else {
+      hg_->clear();
+      gold_hg_->clear();
+    }
+  }
+
+  int state;
+};
+
+void ReadConfig(const string& ini, istringstream* out) {
+  ReadFile rf(ini);
+  istream& in = *rf.stream();
+  ostringstream os;
+  while(in) {
+    string line;
+    getline(in, line);
+    if (!in) continue;
+    os << line << endl;
+  }
+  out->str(os.str());
+}
+
+#ifdef HAVE_MPI
+namespace boost { namespace mpi {
+  template<>
+  struct is_commutative<std::plus<SparseVector<double> >, SparseVector<double> > 
+    : mpl::true_ { };
+} } // end namespace boost::mpi
+#endif
+
+void AddGrad(const SparseVector<prob_t> x, double s, SparseVector<double>* acc) {
+  for (SparseVector<prob_t>::const_iterator it = x.begin(); it != x.end(); ++it)
+    acc->add_value(it->first, it->second.as_float() * s);
+}
+
+double PNorm(const vector<double>& v, const double p) {
+  double acc = 0;
+  for (int i = 0; i < v.size(); ++i)
+    acc += pow(v[i], p);
+  return pow(acc, 1.0 / p);
+}
+
+void VV(ostream&os, const vector<double>& v) {
+  for (int i = 1; i < v.size(); ++i)
+    if (v[i]) os << FD::Convert(i) << "=" << v[i] << " ";
+}
+
+double ApplyRegularizationTerms(const double C,
+                                const double T,
+                                const vector<double>& weights,
+                                const vector<double>& prev_weights,
+                                double* g) {
+  double reg = 0;
+  for (size_t i = 0; i < weights.size(); ++i) {
+    const double prev_w_i = (i < prev_weights.size() ? prev_weights[i] : 0.0);
+    const double& w_i = weights[i];
+    reg += C * w_i * w_i;
+    g[i] += 2 * C * w_i;
+
+    reg += T * (w_i - prev_w_i) * (w_i - prev_w_i);
+    g[i] += 2 * T * (w_i - prev_w_i);
+  }
+  return reg;
+}
+
+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();
+  MT19937* rng = NULL;
+
+  po::variables_map conf;
+  if (!InitCommandLine(argc, argv, &conf))
+    return 1;
+
+  boost::shared_ptr<BatchOptimizer> o;
+  const unsigned lbfgs_memory_buffers = conf["lbfgs_memory_buffers"].as<unsigned>();
+  const unsigned size_per_proc = conf["minibatch_size_per_proc"].as<unsigned>();
+  const unsigned minibatch_iterations = conf["minibatch_iterations"].as<unsigned>();
+  const double regularization_strength = conf["regularization_strength"].as<double>();
+  const double time_series_strength = conf["time_series_strength"].as<double>();
+  const bool use_time_series_reg = time_series_strength > 0.0;
+  const unsigned max_iteration = conf["iterations"].as<unsigned>();
+
+  vector<string> corpus;
+  vector<int> ids;
+  ReadTrainingCorpus(conf["training_data"].as<string>(), rank, size, &corpus, &ids);
+  assert(corpus.size() > 0);
+
+  if (size_per_proc > corpus.size()) {
+    cerr << "Minibatch size (per processor) must be smaller or equal to the local corpus size!\n";
+    return 1;
+  }
+
+  // initialize decoder (loads hash functions if necessary)
+  istringstream ins;
+  ReadConfig(conf["cdec_config"].as<string>(), &ins);
+  Decoder decoder(&ins);
+
+  // load initial weights
+  vector<weight_t> prev_weights;
+  if (conf.count("weights"))
+    Weights::InitFromFile(conf["weights"].as<string>(), &prev_weights);
+
+  if (conf.count("random_seed"))
+    rng = new MT19937(conf["random_seed"].as<uint32_t>());
+  else
+    rng = new MT19937;
+
+  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;
+
+  CopyHGsObserver observer;
+
+  int write_weights_every_ith = 100; // TODO configure
+  int titer = -1;
+
+  vector<weight_t>& cur_weights = decoder.CurrentWeightVector();
+  if (use_time_series_reg) {
+    cur_weights = prev_weights;
+  } else {
+    cur_weights.swap(prev_weights);
+    prev_weights.clear();
+  }
+
+  int iter = -1;
+  bool converged = false;
+  vector<double> gg;
+  while (!converged) {
+#ifdef HAVE_MPI
+    mpi::timer timer;
+#endif
+    ++iter; ++titer;
+    if (rank == 0) {
+      converged = (iter == max_iteration);
+        string fname = "weights.cur.gz";
+        if (iter % write_weights_every_ith == 0) {
+          ostringstream o; o << "weights.epoch_" << iter << ".gz";
+          fname = o.str();
+        }
+        if (converged) { fname = "weights.final.gz"; }
+        ostringstream vv;
+        vv << "total iter=" << titer << " (of current config iter=" << iter << ")  minibatch=" << size_per_proc << " sentences/proc x " << size << " procs.   num_feats=" << FD::NumFeats() << "   passes_thru_data=" << (titer * size_per_proc / static_cast<double>(corpus.size()));
+        const string svv = vv.str();
+        Weights::WriteToFile(fname, cur_weights, true, &svv);
+      }
+
+      vector<Hypergraph> hgs(size_per_proc);
+      vector<Hypergraph> gold_hgs(size_per_proc);
+      for (int i = 0; i < size_per_proc; ++i) {
+        int ei = corpus.size() * rng->next();
+        int id = ids[ei];
+        observer.SetCurrentHypergraphs(&hgs[i], &gold_hgs[i]);
+        decoder.SetId(id);
+        decoder.Decode(corpus[ei], &observer);
+      }
+
+      SparseVector<double> local_grad, g;
+      double local_obj = 0;
+      o.reset();
+      for (unsigned mi = 0; mi < minibatch_iterations; ++mi) {
+        local_grad.clear();
+        g.clear();
+        local_obj = 0;
+
+        for (unsigned i = 0; i < size_per_proc; ++i) {
+          Hypergraph& hg = hgs[i];
+          Hypergraph& hg_gold = gold_hgs[i];
+          if (hg.edges_.size() < 2) continue;
+
+          hg.Reweight(cur_weights);
+          hg_gold.Reweight(cur_weights);
+          SparseVector<prob_t> model_exp, gold_exp;
+          const prob_t z = InsideOutside<prob_t,
+                                         EdgeProb,
+                                         SparseVector<prob_t>,
+                                         EdgeFeaturesAndProbWeightFunction>(hg, &model_exp);
+          local_obj += log(z);
+          model_exp /= z;
+          AddGrad(model_exp, 1.0, &local_grad);
+          model_exp.clear();
+
+          const prob_t goldz = InsideOutside<prob_t,
+                                         EdgeProb,
+                                         SparseVector<prob_t>,
+                                         EdgeFeaturesAndProbWeightFunction>(hg_gold, &gold_exp);
+          local_obj -= log(goldz);
+
+          if (log(z) - log(goldz) < kMINUS_EPSILON) {
+            cerr << "DIFF. ERR! log_model_z < log_gold_z: " << log(z) << " " << log(goldz) << endl;
+            return 1;
+          }
+
+          gold_exp /= goldz;
+          AddGrad(gold_exp, -1.0, &local_grad);
+        }
+
+        double obj = 0;
+#ifdef HAVE_MPI
+        reduce(world, local_obj, obj, std::plus<double>(), 0);
+        reduce(world, local_grad, g, std::plus<SparseVector<double> >(), 0);
+#else
+        obj = local_obj;
+        g.swap(local_grad);
+#endif
+        local_grad.clear();
+        if (rank == 0) {
+          // g /= (size_per_proc * size);
+          if (!o)
+            o.reset(new LBFGSOptimizer(FD::NumFeats(), lbfgs_memory_buffers));
+          gg.clear();
+          gg.resize(FD::NumFeats());
+          if (gg.size() != cur_weights.size()) { cur_weights.resize(gg.size()); }
+          for (SparseVector<double>::iterator it = g.begin(); it != g.end(); ++it)
+            if (it->first) { gg[it->first] = it->second; }
+          g.clear();
+          double r = ApplyRegularizationTerms(regularization_strength,
+                                time_series_strength, // * (iter == 0 ? 0.0 : 1.0),
+                                cur_weights,
+                                prev_weights,
+                                &gg[0]);
+          obj += r;
+          if (mi == 0 || mi == (minibatch_iterations - 1)) {
+            if (!mi) cerr << iter << ' '; else cerr << ' ';
+            cerr << "OBJ=" << obj << " (REG=" << r << ")" << " |g|=" << PNorm(gg, 2) << " |w|=" << PNorm(cur_weights, 2); 
+            if (mi > 0) cerr << endl << flush; else cerr << ' ';
+          } else { cerr << '.' << flush; }
+          // cerr << "w = "; VV(cerr, cur_weights); cerr << endl;
+          // cerr << "g = "; VV(cerr, gg); cerr << endl;
+          o->Optimize(obj, gg, &cur_weights);
+        }
+#ifdef HAVE_MPI
+        broadcast(world, cur_weights, 0);
+        broadcast(world, converged, 0);
+        world.barrier();
+#endif
+    }
+    prev_weights = cur_weights;
+  }
+  return 0;
+}