From 1b8181bf0d6e9137e6b9ccdbe414aec37377a1a9 Mon Sep 17 00:00:00 2001 From: Chris Dyer Date: Sun, 18 Nov 2012 13:35:42 -0500 Subject: major restructure of the training code --- training/crf/mpi_batch_optimize.cc | 372 +++++++++++++++++++++++++++++++++++++ 1 file changed, 372 insertions(+) create mode 100644 training/crf/mpi_batch_optimize.cc (limited to 'training/crf/mpi_batch_optimize.cc') diff --git a/training/crf/mpi_batch_optimize.cc b/training/crf/mpi_batch_optimize.cc new file mode 100644 index 00000000..2eff07e4 --- /dev/null +++ b/training/crf/mpi_batch_optimize.cc @@ -0,0 +1,372 @@ +#include +#include +#include +#include +#include + +#include "config.h" +#ifdef HAVE_MPI +#include +#include +namespace mpi = boost::mpi; +#endif + +#include +#include +#include + +#include "sentence_metadata.h" +#include "cllh_observer.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 "stringlib.h" +#include "optimize.h" +#include "fdict.h" +#include "weights.h" +#include "sparse_vector.h" + +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(),"Input feature weights file") + ("training_data,t",po::value(),"Training data") + ("test_data,T",po::value(),"(optional) test data") + ("decoder_config,c",po::value(),"Decoder configuration file") + ("output_weights,o",po::value()->default_value("-"),"Output feature weights file") + ("optimization_method,m", po::value()->default_value("lbfgs"), "Optimization method (sgd, lbfgs, rprop)") + ("correction_buffers,M", po::value()->default_value(10), "Number of gradients for LBFGS to maintain in memory") + ("gaussian_prior,p","Use a Gaussian prior on the weights") + ("sigma_squared", po::value()->default_value(1.0), "Sigma squared term for spherical Gaussian prior") + ("means,u", po::value(), "(optional) file containing the means for Gaussian prior"); + po::options_description clo("Command line options"); + clo.add_options() + ("config", po::value(), "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().c_str()); + po::store(po::parse_config_file(config, dconfig_options), *conf); + } + po::notify(*conf); + + if (conf->count("help") || !conf->count("input_weights") || !(conf->count("training_data")) || !conf->count("decoder_config")) { + cerr << dcmdline_options << endl; + return false; + } + return true; +} + +void ReadTrainingCorpus(const string& fname, int rank, int size, vector* 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() { + acc_grad.clear(); + acc_obj = 0; + total_complete = 0; + trg_words = 0; + } + + void SetLocalGradientAndObjective(vector* g, double* o) const { + *o = acc_obj; + for (SparseVector::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, + 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 ref_exp; + const prob_t ref_z = InsideOutside, + 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); + trg_words += smeta.GetReference().size(); + } + + virtual void NotifyDecodingComplete(const SentenceMetadata& smeta) { + if (state == 3) { + ++total_complete; + } else { + } + } + + int total_complete; + SparseVector cur_model_exp; + SparseVector acc_grad; + double acc_obj; + double cur_obj; + unsigned trg_words; + int state; +}; + +void ReadConfig(const string& ini, vector* 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& cfg, istringstream* o) { + ostringstream os; + for (int i = 0; i < cfg.size(); ++i) { os << cfg[i] << endl; } + o->str(os.str()); +} + +template +struct VectorPlus : public binary_function, vector, vector > { + vector operator()(const vector& a, const vector& b) const { + assert(a.size() == b.size()); + vector v(a.size()); + transform(a.begin(), a.end(), b.begin(), v.begin(), plus()); + return v; + } +}; + +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 + SetSilent(true); // turn off verbose decoder output + register_feature_functions(); + + po::variables_map conf; + if (!InitCommandLine(argc, argv, &conf)) return 1; + + // load cdec.ini and set up decoder + vector cdec_ini; + ReadConfig(conf["decoder_config"].as(), &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() != "-") { + cerr << "cdec.ini must not set an input file\n"; + return 1; + } + if (rank == 0) cerr << "Done loading grammar!\n"; + + // load initial weights + if (rank == 0) { cerr << "Loading weights...\n"; } + vector& lambdas = decoder->CurrentWeightVector(); + Weights::InitFromFile(conf["input_weights"].as(), &lambdas); + if (rank == 0) { cerr << "Done loading weights.\n"; } + + // freeze feature set (should be optional?) + const bool freeze_feature_set = true; + if (freeze_feature_set) FD::Freeze(); + + const int num_feats = FD::NumFeats(); + if (rank == 0) cerr << "Number of features: " << num_feats << endl; + lambdas.resize(num_feats); + + const bool gaussian_prior = conf.count("gaussian_prior"); + vector means(num_feats, 0); + if (conf.count("means")) { + if (!gaussian_prior) { + cerr << "Don't use --means without --gaussian_prior!\n"; + exit(1); + } + Weights::InitFromFile(conf["means"].as(), &means); + } + boost::shared_ptr o; + if (rank == 0) { + const string omethod = conf["optimization_method"].as(); + if (omethod == "rprop") + o.reset(new RPropOptimizer(num_feats)); // TODO add configuration + else + o.reset(new LBFGSOptimizer(num_feats, conf["correction_buffers"].as())); + cerr << "Optimizer: " << o->Name() << endl; + } + double objective = 0; + vector gradient(num_feats, 0.0); + vector rcv_grad; + rcv_grad.clear(); + bool converged = false; + + vector corpus, test_corpus; + ReadTrainingCorpus(conf["training_data"].as(), rank, size, &corpus); + assert(corpus.size() > 0); + if (conf.count("test_data")) + ReadTrainingCorpus(conf["test_data"].as(), rank, size, &test_corpus); + + TrainingObserver observer; + ConditionalLikelihoodObserver cllh_observer; + while (!converged) { + observer.Reset(); + cllh_observer.Reset(); +#ifdef HAVE_MPI + mpi::timer timer; + world.barrier(); +#endif + if (rank == 0) { + cerr << "Starting decoding... (~" << corpus.size() << " sentences / proc)\n"; + cerr << " Testset size: " << test_corpus.size() << " sentences / proc)\n"; + } + for (int i = 0; i < corpus.size(); ++i) + decoder->Decode(corpus[i], &observer); + cerr << " process " << rank << '/' << size << " done\n"; + fill(gradient.begin(), gradient.end(), 0); + observer.SetLocalGradientAndObjective(&gradient, &objective); + + unsigned total_words = 0; +#ifdef HAVE_MPI + double to = 0; + rcv_grad.resize(num_feats, 0.0); + mpi::reduce(world, &gradient[0], gradient.size(), &rcv_grad[0], plus(), 0); + swap(gradient, rcv_grad); + rcv_grad.clear(); + + reduce(world, observer.trg_words, total_words, std::plus(), 0); + mpi::reduce(world, objective, to, plus(), 0); + objective = to; +#else + total_words = observer.trg_words; +#endif + if (rank == 0) + cerr << "TRAINING CORPUS: ln p(f|e)=" << objective << "\t log_2 p(f|e) = " << (objective/log(2)) << "\t cond. entropy = " << (objective/log(2) / total_words) << "\t ppl = " << pow(2, (objective/log(2) / total_words)) << endl; + + for (int i = 0; i < test_corpus.size(); ++i) + decoder->Decode(test_corpus[i], &cllh_observer); + + double test_objective = 0; + unsigned test_total_words = 0; +#ifdef HAVE_MPI + reduce(world, cllh_observer.acc_obj, test_objective, std::plus(), 0); + reduce(world, cllh_observer.trg_words, test_total_words, std::plus(), 0); +#else + test_objective = cllh_observer.acc_obj; + test_total_words = cllh_observer.trg_words; +#endif + + if (rank == 0) { // run optimizer only on rank=0 node + if (test_corpus.size()) + cerr << " TEST CORPUS: ln p(f|e)=" << test_objective << "\t log_2 p(f|e) = " << (test_objective/log(2)) << "\t cond. entropy = " << (test_objective/log(2) / test_total_words) << "\t ppl = " << pow(2, (test_objective/log(2) / test_total_words)) << endl; + if (gaussian_prior) { + const double sigsq = conf["sigma_squared"].as(); + double norm = 0; + for (int k = 1; k < lambdas.size(); ++k) { + const double& lambda_k = lambdas[k]; + if (lambda_k) { + const double param = (lambda_k - means[k]); + norm += param * param; + gradient[k] += param / sigsq; + } + } + const double reg = norm / (2.0 * sigsq); + cerr << "REGULARIZATION TERM: " << reg << endl; + objective += reg; + } + cerr << "EVALUATION #" << o->EvaluationCount() << " OBJECTIVE: " << objective << endl; + double gnorm = 0; + for (int i = 0; i < gradient.size(); ++i) + gnorm += gradient[i] * gradient[i]; + cerr << " GNORM=" << sqrt(gnorm) << endl; + vector old = lambdas; + int c = 0; + while (old == lambdas) { + ++c; + if (c > 1) { cerr << "Same lambdas, repeating optimization\n"; } + o->Optimize(objective, gradient, &lambdas); + assert(c < 5); + } + old.clear(); + Weights::SanityCheck(lambdas); + Weights::ShowLargestFeatures(lambdas); + + converged = o->HasConverged(); + if (converged) { cerr << "OPTIMIZER REPORTS CONVERGENCE!\n"; } + + string fname = "weights.cur.gz"; + if (converged) { fname = "weights.final.gz"; } + ostringstream vv; + vv << "Objective = " << objective << " (eval count=" << o->EvaluationCount() << ")"; + const string svv = vv.str(); + Weights::WriteToFile(fname, lambdas, true, &svv); + } // rank == 0 + int cint = converged; +#ifdef HAVE_MPI + mpi::broadcast(world, &lambdas[0], lambdas.size(), 0); + mpi::broadcast(world, cint, 0); + if (rank == 0) { cerr << " ELAPSED TIME THIS ITERATION=" << timer.elapsed() << endl; } +#endif + converged = cint; + } + return 0; +} + -- cgit v1.2.3