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-rw-r--r--training/mpi_online_optimize.cc209
1 files changed, 111 insertions, 98 deletions
diff --git a/training/mpi_online_optimize.cc b/training/mpi_online_optimize.cc
index 509fbf15..4c08b181 100644
--- a/training/mpi_online_optimize.cc
+++ b/training/mpi_online_optimize.cc
@@ -6,6 +6,7 @@
#include <cmath>
#include <tr1/memory>
+#include <boost/mpi/timer.hpp>
#include <boost/mpi.hpp>
#include <boost/program_options.hpp>
#include <boost/program_options/variables_map.hpp>
@@ -61,13 +62,9 @@ bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
opts.add_options()
("input_weights,w",po::value<string>(),"Input feature weights file")
("training_data,t",po::value<string>(),"Training data corpus")
- ("decoder_config,c",po::value<string>(),"Decoder configuration file")
- ("output_weights,o",po::value<string>()->default_value("-"),"Output feature weights file")
- ("maximum_iteration,i", po::value<unsigned>(), "Maximum number of iterations")
+ ("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")
- ("freeze_feature_set,Z", "The feature set specified in the initial weights file is frozen throughout the duration of training")
("optimization_method,m", po::value<string>()->default_value("sgd"), "Optimization method (sgd)")
- ("fully_random,r", "Fully random draws from the training corpus")
("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")
@@ -87,21 +84,26 @@ bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
}
po::notify(*conf);
- if (conf->count("help") || !conf->count("training_data") || !conf->count("decoder_config")) {
+ 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, vector<string>* c) {
+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;
- c->push_back(line);
+ if (id % size == rank) {
+ c->push_back(line);
+ order->push_back(id);
+ }
+ ++id;
}
}
@@ -192,15 +194,6 @@ struct TrainingObserver : public DecoderObserver {
int state;
};
-template <typename T>
-inline void Shuffle(vector<T>* c, MT19937* rng) {
- unsigned size = c->size();
- for (unsigned i = size - 1; i > 0; --i) {
- const unsigned j = static_cast<unsigned>(rng->next() * i);
- swap((*c)[j], (*c)[i]);
- }
-}
-
namespace mpi = boost::mpi;
namespace boost { namespace mpi {
@@ -209,6 +202,32 @@ namespace boost { namespace mpi {
: mpl::true_ { };
} } // end namespace boost::mpi
+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);
+ cerr << "X: " << x.second << " - " << x.first << "'\n";
+ if (!FileExists(x.first)) {
+ cerr << "Can't find file " << x.first << endl;
+ return false;
+ }
+ }
+ return true;
+}
int main(int argc, char** argv) {
mpi::environment env(argc, argv);
@@ -228,36 +247,22 @@ int main(int argc, char** argv) {
if (conf.count("input_weights"))
weights.InitFromFile(conf["input_weights"].as<string>());
- // freeze feature set
- const bool freeze_feature_set = conf.count("freeze_feature_set");
- if (freeze_feature_set) FD::Freeze();
-
- // load cdec.ini and set up decoder
- ReadFile ini_rf(conf["decoder_config"].as<string>());
- Decoder decoder(ini_rf.stream());
- if (decoder.GetConf()["input"].as<string>() != "-") {
- cerr << "cdec.ini must not set an input file\n";
- abort();
- }
-
vector<string> corpus;
- ReadTrainingCorpus(conf["training_data"].as<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;
- vector<int> order(corpus.size());
- const bool fully_random = conf.count("fully_random");
const unsigned size_per_proc = conf["minibatch_size_per_proc"].as<unsigned>();
- const unsigned batch_size = size_per_proc * size;
- if (rank == 0) {
- cerr << "Corpus: " << corpus.size() << " batch size: " << batch_size << endl;
- if (batch_size > corpus.size()) {
- cerr << " Reduce minibatch_size_per_proc!";
- abort();
- }
+ if (size_per_proc > corpus.size()) {
+ cerr << "Minibatch size must be smaller than corpus size!\n";
+ return 1;
+ }
+ if (rank == 0) {
+ const unsigned batch_size = size_per_proc * size;
// TODO config
lr.reset(new ExponentialDecayLearningRate(batch_size, conf["eta_0"].as<double>()));
@@ -268,75 +273,83 @@ int main(int argc, char** argv) {
} else {
assert(!"fail");
}
-
- for (unsigned i = 0; i < order.size(); ++i) order[i]=i;
- // randomize corpus
- if (conf.count("random_seed"))
- rng.reset(new MT19937(conf["random_seed"].as<uint32_t>()));
- else
- rng.reset(new MT19937);
}
+ 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(&x);
- int miter = corpus.size(); // hack to cause initial broadcast of order info
TrainingObserver observer;
- double objective = 0;
- bool converged = false;
int write_weights_every_ith = 100; // TODO configure
- int iter = -1;
+ 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;
- while (!converged) {
- weights.InitFromVector(x);
- weights.InitVector(&lambdas);
- ++miter; ++iter;
- observer.Reset();
- decoder.SetWeights(lambdas);
- if (rank == 0) {
- if (conf.count("maximum_iteration")) {
- if (iter == conf["maximum_iteration"].as<unsigned>())
- converged = true;
- }
- SanityCheck(lambdas);
- ShowLargestFeatures(lambdas);
- string fname = "weights.cur.gz";
- if (converged) { fname = "weights.final.gz"; }
- if (iter % write_weights_every_ith == 0) {
- ostringstream o; o << "weights." << iter << ".gz";
- fname = o.str();
+ 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());
+
+ o->ResetEpoch(); // resets the learning rate-- TODO is this good?
+
+ int iter = -1;
+ bool converged = false;
+ while (!converged) {
+ mpi::timer timer;
+ weights.InitFromVector(x);
+ weights.InitVector(&lambdas);
+ ++iter; ++titer;
+ observer.Reset();
+ decoder.SetWeights(lambdas);
+ if (rank == 0) {
+ converged = (iter == max_iteration);
+ SanityCheck(lambdas);
+ 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();
+ }
+ if (converged && ((ai+1)==agenda.size())) { 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=" << x.size() << '/' << FD::NumFeats() << " passes_thru_data=" << (titer * size * 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);
}
- ostringstream vv;
- vv << "Objective = " << objective; // << " (eval count=" << o->EvaluationCount() << ")";
- const string svv = vv.str();
- weights.WriteToFile(fname, true, &svv);
- }
- if (fully_random || size * size_per_proc * miter > corpus.size()) {
- if (rank == 0)
- Shuffle(&order, rng.get());
- miter = 0;
- broadcast(world, order, 0);
- }
- if (rank == 0)
- cerr << "iter=" << iter << " minibatch=" << size_per_proc << " sentences/proc x " << size << " procs. num_feats=" << x.size() << '/' << FD::NumFeats() << " passes_thru_data=" << (iter * batch_size / static_cast<double>(corpus.size())) << " eta=" << lr->eta(iter) << endl;
-
- const int beg = size * miter * size_per_proc + rank * size_per_proc;
- const int end = beg + size_per_proc;
- for (int i = beg; i < end; ++i) {
- int ex_num = order[i % order.size()];
- if (rank ==0 && size < 3) cerr << rank << ": ex_num=" << ex_num << endl;
- decoder.SetId(ex_num);
- decoder.Decode(corpus[ex_num], &observer);
- }
- SparseVector<double> local_grad, g;
- observer.GetGradient(&local_grad);
- reduce(world, local_grad, g, std::plus<SparseVector<double> >(), 0);
- if (rank == 0) {
- g /= batch_size;
- o->UpdateWeights(g, FD::NumFeats(), &x);
+ 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);
+ reduce(world, local_grad, g, std::plus<SparseVector<double> >(), 0);
+ local_grad.clear();
+ if (rank == 0) {
+ g /= (size_per_proc * size);
+ o->UpdateWeights(g, FD::NumFeats(), &x);
+ cerr << "XX: " << x << endl;
+ }
+ broadcast(world, x, 0);
+ broadcast(world, converged, 0);
+ world.barrier();
+ if (rank == 0) { cerr << " ELAPSED TIME THIS ITERATION=" << timer.elapsed() << endl; }
}
- broadcast(world, x, 0);
- world.barrier();
}
return 0;
}
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