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Diffstat (limited to 'training/crf/mpi_flex_optimize.cc')
| -rw-r--r-- | training/crf/mpi_flex_optimize.cc | 386 |
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; +} |