finalized merge

1 files changed, 28 insertions, 47 deletions

diff --git a/training/mpi_online_optimize.cc b/training/mpi_online_optimize.cc
index 32033c19..993627f0 100644
--- a/training/mpi_online_optimize.cc
+++ b/training/mpi_online_optimize.cc
@@ -9,6 +9,7 @@
 #include <boost/program_options.hpp>
 #include <boost/program_options/variables_map.hpp>
 
+#include "stringlib.h"
 #include "verbose.h"
 #include "hg.h"
 #include "prob.h"
@@ -31,35 +32,6 @@ namespace mpi = boost::mpi;
 using namespace std;
 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;
-}
-
 bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
   po::options_description opts("Configuration options");
   opts.add_options()
@@ -123,7 +95,7 @@ struct TrainingObserver : public DecoderObserver {
   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;
+      (*g)[it->first] = it->second.as_float();
   }
 
   virtual void NotifyDecodingStart(const SentenceMetadata& smeta) {
@@ -187,7 +159,7 @@ struct TrainingObserver : public DecoderObserver {
   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);
+      g->set_value(it->first, it->second.as_float());
   }
 
   int total_complete;
@@ -233,6 +205,7 @@ bool LoadAgenda(const string& file, vector<pair<string, int> >* a) {
 }
 
 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;
@@ -250,10 +223,25 @@ int main(int argc, char** argv) {
   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
-  Weights weights;
+  vector<weight_t> init_weights;
   if (conf.count("input_weights"))
-    weights.InitFromFile(conf["input_weights"].as<string>());
+    Weights::InitFromFile(conf["input_weights"].as<string>(), &init_weights);
 
   vector<int> frozen_fids;
   if (conf.count("frozen_features")) {
@@ -310,19 +298,12 @@ int main(int argc, char** argv) {
     rng.reset(new MT19937);
 
   SparseVector<double> x;
-  weights.InitSparseVector(&x);
+  Weights::InitSparseVector(init_weights, &x);
   TrainingObserver observer;
 
   int write_weights_every_ith = 100; // TODO configure
   int titer = -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";
-
-  vector<double> lambdas;
   for (int ai = 0; ai < agenda.size(); ++ai) {
     const string& cur_config = agenda[ai].first;
     const unsigned max_iteration = agenda[ai].second;
@@ -331,6 +312,8 @@ int main(int argc, char** argv) {
     // 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?
@@ -341,15 +324,13 @@ int main(int argc, char** argv) {
 #ifdef HAVE_MPI
       mpi::timer timer;
 #endif
-      weights.InitFromVector(x);
-      weights.InitVector(&lambdas);
+      x.init_vector(&lambdas);
       ++iter; ++titer;
       observer.Reset();
-      decoder.SetWeights(lambdas);
       if (rank == 0) {
         converged = (iter == max_iteration);
-        SanityCheck(lambdas);
-        ShowLargestFeatures(lambdas);
+        Weights::SanityCheck(lambdas);
+        Weights::ShowLargestFeatures(lambdas);
         string fname = "weights.cur.gz";
         if (iter % write_weights_every_ith == 0) {
           ostringstream o; o << "weights.epoch_" << (ai+1) << '.' << iter << ".gz";
@@ -360,7 +341,7 @@ int main(int argc, char** argv) {
         vv << "total 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, true, &svv);
+        Weights::WriteToFile(fname, lambdas, true, &svv);
       }
 
       for (int i = 0; i < size_per_proc; ++i) {