em optimizer- really crappy implementation

1 files changed, 389 insertions, 0 deletions

diff --git a/training/mpi_em_optimize.cc b/training/mpi_em_optimize.cc
new file mode 100644
index 00000000..48683b15
--- /dev/null
+++ b/training/mpi_em_optimize.cc
@@ -0,0 +1,389 @@
+#include <sstream>
+#include <iostream>
+#include <vector>
+#include <cassert>
+#include <cmath>
+
+#ifdef HAVE_MPI
+#include <mpi.h>
+#endif
+
+#include <boost/shared_ptr.hpp>
+#include <boost/program_options.hpp>
+#include <boost/program_options/variables_map.hpp>
+
+#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"
+
+using namespace std;
+using boost::shared_ptr;
+namespace po = boost::program_options;
+
+void SanityCheck(const vector<double>& w) {
+  for (int i = 0; i < w.size(); ++i) {
+    assert(!isnan(w[i]));
+    assert(!isinf(w[i]));
+  }
+}
+
+struct FComp {
+  const vector<double>& w_;
+  FComp(const vector<double>& w) : w_(w) {}
+  bool operator()(int a, int b) const {
+    return fabs(w_[a]) > fabs(w_[b]);
+  }
+};
+
+void ShowLargestFeatures(const vector<double>& w) {
+  vector<int> fnums(w.size());
+  for (int i = 0; i < w.size(); ++i)
+    fnums[i] = i;
+  vector<int>::iterator mid = fnums.begin();
+  mid += (w.size() > 10 ? 10 : w.size());
+  partial_sort(fnums.begin(), mid, fnums.end(), FComp(w));
+  cerr << "TOP FEATURES:";
+  for (vector<int>::iterator i = fnums.begin(); i != mid; ++i) {
+    cerr << ' ' << FD::Convert(*i) << '=' << w[*i];
+  }
+  cerr << endl;
+}
+
+void 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")
+        ("training_data,t",po::value<string>(),"Training data")
+        ("decoder_config,c",po::value<string>(),"Decoder configuration file")
+        ("output_weights,o",po::value<string>()->default_value("-"),"Output feature weights file");
+  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;
+#ifdef HAVE_MPI
+    MPI::Finalize();
+#endif
+    exit(1);
+  }
+}
+
+void ReadTrainingCorpus(const string& fname, int rank, int size, vector<string>* c) {
+  ReadFile rf(fname);
+  istream& in = *rf.stream();
+  string line;
+  int lc = 0;
+  while(in) {
+    getline(in, line);
+    if (!in) break;
+    if (lc % size == rank) c->push_back(line);
+    ++lc;
+  }
+}
+
+static const double kMINUS_EPSILON = -1e-6;
+
+struct TrainingObserver : public DecoderObserver {
+  void Reset() {
+    total_complete = 0;
+    cur_obj = 0;
+    tot_obj = 0;
+    tot.clear();
+  } 
+
+  void SetLocalGradientAndObjective(SparseVector<double>* g, double* o) const {
+    *o = tot_obj;
+    *g = tot;
+  }
+
+  virtual void NotifyDecodingStart(const SentenceMetadata& smeta) {
+    cur_obj = 0;
+    state = 1;
+  }
+
+  void ExtractExpectedCounts(Hypergraph* hg) {
+    vector<prob_t> posts;
+    cur.clear();
+    const prob_t z = hg->ComputeEdgePosteriors(1.0, &posts);
+    cur_obj = log(z);
+    for (int i = 0; i < posts.size(); ++i) {
+      const SparseVector<double>& efeats = hg->edges_[i].feature_values_;
+      const double post = static_cast<double>(posts[i] / z);
+      for (SparseVector<double>::const_iterator j = efeats.begin(); j != efeats.end(); ++j)
+        cur.add_value(j->first, post);
+    }
+  }
+
+  // compute model expectations, denominator of objective
+  virtual void NotifyTranslationForest(const SentenceMetadata& smeta, Hypergraph* hg) {
+    assert(state == 1);
+    state = 2;
+    ExtractExpectedCounts(hg);
+  }
+
+  // replace translation forest, since we're doing EM training (we don't know which)
+  virtual void NotifyAlignmentForest(const SentenceMetadata& smeta, Hypergraph* hg) {
+    assert(state == 2);
+    state = 3;
+    ExtractExpectedCounts(hg);
+  }
+
+  virtual void NotifyDecodingComplete(const SentenceMetadata& smeta) {
+    ++total_complete;
+    tot_obj += cur_obj;
+    tot += cur;
+  }
+
+  int total_complete;
+  double cur_obj;
+  double tot_obj;
+  SparseVector<double> cur, tot;
+  int state;
+};
+
+void ReadConfig(const string& ini, vector<string>* out) {
+  ReadFile rf(ini);
+  istream& in = *rf.stream();
+  while(in) {
+    string line;
+    getline(in, line);
+    if (!in) continue;
+    out->push_back(line);
+  }
+}
+
+void StoreConfig(const vector<string>& cfg, istringstream* o) {
+  ostringstream os;
+  for (int i = 0; i < cfg.size(); ++i) { os << cfg[i] << endl; }
+  o->str(os.str());
+}
+
+struct OptimizableMultinomialFamily {
+  struct CPD {
+    CPD() : z() {}
+    double z;
+    map<WordID, double> c2counts;
+  };
+  map<WordID, CPD> counts;
+  double Value(WordID conditioning, WordID generated) const {
+    map<WordID, CPD>::const_iterator it = counts.find(conditioning);
+    assert(it != counts.end());
+    map<WordID,double>::const_iterator r = it->second.c2counts.find(generated);
+    if (r == it->second.c2counts.end()) return 0;
+    return r->second;
+  }
+  void Increment(WordID conditioning, WordID generated, double count) {
+    CPD& cc = counts[conditioning];
+    cc.z += count;
+    cc.c2counts[generated] += count;
+  }
+  void Optimize() {
+    for (map<WordID, CPD>::iterator i = counts.begin(); i != counts.end(); ++i) {
+      CPD& cpd = i->second;
+      for (map<WordID, double>::iterator j = cpd.c2counts.begin(); j != cpd.c2counts.end(); ++j) {
+        j->second /= cpd.z;
+        // cerr << "P(" << TD::Convert(j->first) << " | " << TD::Convert(i->first) << " ) =  " << j->second << endl;
+      }
+    }
+  }
+  void Clear() {
+    counts.clear();
+  }
+};
+
+struct CountManager {
+  CountManager(size_t num_types) : oms_(num_types) {}
+  virtual ~CountManager();
+  virtual void AddCounts(const SparseVector<double>& c) = 0;
+  void Optimize(SparseVector<double>* weights) {
+    for (int i = 0; i < oms_.size(); ++i) {
+      oms_[i].Optimize();
+    }
+    GetOptimalValues(weights);
+    for (int i = 0; i < oms_.size(); ++i) {
+      oms_[i].Clear();
+    }
+  }
+  virtual void GetOptimalValues(SparseVector<double>* wv) const = 0;
+  vector<OptimizableMultinomialFamily> oms_;
+};
+CountManager::~CountManager() {}
+
+struct TaggerCountManager : public CountManager {
+  // 0 = transitions, 2 = emissions
+  TaggerCountManager() : CountManager(2) {}
+  void AddCounts(const SparseVector<double>& c);
+  void GetOptimalValues(SparseVector<double>* wv) const {
+    for (set<int>::const_iterator it = fids_.begin(); it != fids_.end(); ++it) {
+      int ftype;
+      WordID cond, gen;
+      bool is_optimized = TaggerCountManager::GetFeature(*it, &ftype, &cond, &gen);
+      assert(is_optimized);
+      wv->set_value(*it, log(oms_[ftype].Value(cond, gen)));
+    }
+  }
+  // Id:0:a=1 Bi:a_b=1 Bi:b_c=1 Bi:c_d=1 Uni:a=1 Uni:b=1 Uni:c=1 Uni:d=1 Id:1:b=1 Bi:BOS_a=1 Id:2:c=1
+  static bool GetFeature(const int fid, int* feature_type, WordID* cond, WordID* gen) {
+    const string& feat = FD::Convert(fid);
+    if (feat.size() > 5 && feat[0] == 'I' && feat[1] == 'd' && feat[2] == ':') {
+      // emission
+      const size_t p = feat.rfind(':');
+      assert(p != string::npos);
+      *cond = TD::Convert(feat.substr(p+1));
+      *gen = TD::Convert(feat.substr(3, p - 3));
+      *feature_type = 1;
+      return true;
+    } else if (feat[0] == 'B' && feat.size() > 5 && feat[2] == ':' && feat[1] == 'i') {
+      // transition
+      const size_t p = feat.rfind('_');
+      assert(p != string::npos);
+      *gen = TD::Convert(feat.substr(p+1));
+      *cond = TD::Convert(feat.substr(3, p - 3));
+      *feature_type = 0;
+      return true;
+    } else if (feat[0] == 'U' && feat.size() > 4 && feat[1] == 'n' && feat[2] == 'i' && feat[3] == ':') {
+      // ignore
+      return false;
+    } else {
+      cerr << "Don't know how to deal with feature of type: " << feat << endl;
+      abort();
+    }
+  }
+  set<int> fids_;
+};
+
+void TaggerCountManager::AddCounts(const SparseVector<double>& c) {
+  for (SparseVector<double>::const_iterator it = c.begin(); it != c.end(); ++it) {
+    const double& val = it->second;
+    int ftype;
+    WordID cond, gen;
+    if (GetFeature(it->first, &ftype, &cond, &gen)) {
+      oms_[ftype].Increment(cond, gen, val);
+      fids_.insert(it->first);
+    }
+  }
+}
+
+int main(int argc, char** argv) {
+#ifdef HAVE_MPI
+  MPI::Init(argc, argv);
+  const int size = MPI::COMM_WORLD.Get_size(); 
+  const int rank = MPI::COMM_WORLD.Get_rank();
+#else
+  const int size = 1;
+  const int rank = 0;
+#endif
+  SetSilent(true);  // turn off verbose decoder output
+  register_feature_functions();
+
+  po::variables_map conf;
+  InitCommandLine(argc, argv, &conf);
+
+  TaggerCountManager tcm;
+
+  // load cdec.ini and set up decoder
+  vector<string> cdec_ini;
+  ReadConfig(conf["decoder_config"].as<string>(), &cdec_ini);
+  istringstream ini;
+  StoreConfig(cdec_ini, &ini);
+  if (rank == 0) cerr << "Loading grammar...\n";
+  Decoder* decoder = new Decoder(&ini);
+  if (decoder->GetConf()["input"].as<string>() != "-") {
+    cerr << "cdec.ini must not set an input file\n";
+#ifdef HAVE_MPI
+    MPI::COMM_WORLD.Abort(1);
+#endif
+  }
+  if (rank == 0) cerr << "Done loading grammar!\n";
+  Weights w;
+  if (conf.count("input_weights"))
+    w.InitFromFile(conf["input_weights"].as<string>());
+
+  double objective = 0;
+  bool converged = false;
+
+  vector<double> lambdas;
+  w.InitVector(&lambdas);
+  vector<string> corpus;
+  ReadTrainingCorpus(conf["training_data"].as<string>(), rank, size, &corpus);
+  assert(corpus.size() > 0);
+
+  int iteration = 0;
+  TrainingObserver observer;
+  while (!converged) {
+    ++iteration;
+    observer.Reset();
+    if (rank == 0) {
+      cerr << "Starting decoding... (~" << corpus.size() << " sentences / proc)\n";
+    }
+    decoder->SetWeights(lambdas);
+    for (int i = 0; i < corpus.size(); ++i)
+      decoder->Decode(corpus[i], &observer);
+
+    SparseVector<double> x;
+    observer.SetLocalGradientAndObjective(&x, &objective);
+    cerr << "COUNTS = " << x << endl;
+    cerr << "   OBJ = " << objective << endl;
+    tcm.AddCounts(x);
+
+#if 0
+#ifdef HAVE_MPI
+    MPI::COMM_WORLD.Reduce(const_cast<double*>(&gradient.data()[0]), &rcv_grad[0], num_feats, MPI::DOUBLE, MPI::SUM, 0);
+    MPI::COMM_WORLD.Reduce(&objective, &to, 1, MPI::DOUBLE, MPI::SUM, 0);
+    swap(gradient, rcv_grad);
+    objective = to;
+#endif
+#endif
+
+    if (rank == 0) {
+      SparseVector<double> wsv;
+      tcm.Optimize(&wsv);
+
+      w.InitFromVector(wsv);
+      w.InitVector(&lambdas);
+
+      ShowLargestFeatures(lambdas);
+
+      converged = iteration > 100;
+      if (converged) { cerr << "OPTIMIZER REPORTS CONVERGENCE!\n"; }
+
+      string fname = "weights.cur.gz";
+      if (converged) { fname = "weights.final.gz"; }
+      ostringstream vv;
+      vv << "Objective = " << objective << "  (ITERATION=" << iteration << ")";
+      const string svv = vv.str();
+      w.WriteToFile(fname, true, &svv);
+    }  // rank == 0
+    int cint = converged;
+#ifdef HAVE_MPI
+    MPI::COMM_WORLD.Bcast(const_cast<double*>(&lambdas.data()[0]), num_feats, MPI::DOUBLE, 0);
+    MPI::COMM_WORLD.Bcast(&cint, 1, MPI::INT, 0);
+    MPI::COMM_WORLD.Barrier();
+#endif
+    converged = cint;
+  }
+#ifdef HAVE_MPI
+  MPI::Finalize(); 
+#endif
+  return 0;
+}