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Diffstat (limited to 'training/mpi_online_optimize.cc')
| -rw-r--r-- | training/mpi_online_optimize.cc | 374 |
1 files changed, 0 insertions, 374 deletions
diff --git a/training/mpi_online_optimize.cc b/training/mpi_online_optimize.cc deleted file mode 100644 index d6968848..00000000 --- a/training/mpi_online_optimize.cc +++ /dev/null @@ -1,374 +0,0 @@ -#include <sstream> -#include <iostream> -#include <fstream> -#include <vector> -#include <cassert> -#include <cmath> -#include <tr1/memory> - -#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)") - ("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; - - 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(); - } - 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_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; -} |