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authorChris Dyer <cdyer@allegro.clab.cs.cmu.edu>2012-11-18 13:35:42 -0500
committerChris Dyer <cdyer@allegro.clab.cs.cmu.edu>2012-11-18 13:35:42 -0500
commit1b8181bf0d6e9137e6b9ccdbe414aec37377a1a9 (patch)
tree33e5f3aa5abff1f41314cf8f6afbd2c2c40e4bfd /training/mpi_batch_optimize.cc
parent7c4665949fb93fb3de402e4ce1d19bef67850d05 (diff)
major restructure of the training code
Diffstat (limited to 'training/mpi_batch_optimize.cc')
-rw-r--r--training/mpi_batch_optimize.cc372
1 files changed, 0 insertions, 372 deletions
diff --git a/training/mpi_batch_optimize.cc b/training/mpi_batch_optimize.cc
deleted file mode 100644
index 2eff07e4..00000000
--- a/training/mpi_batch_optimize.cc
+++ /dev/null
@@ -1,372 +0,0 @@
-#include <sstream>
-#include <iostream>
-#include <vector>
-#include <cassert>
-#include <cmath>
-
-#include "config.h"
-#ifdef HAVE_MPI
-#include <boost/mpi/timer.hpp>
-#include <boost/mpi.hpp>
-namespace mpi = boost::mpi;
-#endif
-
-#include <boost/shared_ptr.hpp>
-#include <boost/program_options.hpp>
-#include <boost/program_options/variables_map.hpp>
-
-#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<string>(),"Input feature weights file")
- ("training_data,t",po::value<string>(),"Training data")
- ("test_data,T",po::value<string>(),"(optional) test data")
- ("decoder_config,c",po::value<string>(),"Decoder configuration file")
- ("output_weights,o",po::value<string>()->default_value("-"),"Output feature weights file")
- ("optimization_method,m", po::value<string>()->default_value("lbfgs"), "Optimization method (sgd, lbfgs, rprop)")
- ("correction_buffers,M", po::value<int>()->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<double>()->default_value(1.0), "Sigma squared term for spherical Gaussian prior")
- ("means,u", po::value<string>(), "(optional) file containing the means for Gaussian prior");
- 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("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<string>* 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<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);
- trg_words += smeta.GetReference().size();
- }
-
- virtual void NotifyDecodingComplete(const SentenceMetadata& smeta) {
- if (state == 3) {
- ++total_complete;
- } else {
- }
- }
-
- int total_complete;
- SparseVector<prob_t> cur_model_exp;
- SparseVector<prob_t> acc_grad;
- double acc_obj;
- double cur_obj;
- unsigned trg_words;
- int state;
-};
-
-void ReadConfig(const string& ini, vector<string>* 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<string>& cfg, istringstream* o) {
- ostringstream os;
- for (int i = 0; i < cfg.size(); ++i) { os << cfg[i] << endl; }
- o->str(os.str());
-}
-
-template <typename T>
-struct VectorPlus : public binary_function<vector<T>, vector<T>, vector<T> > {
- vector<T> operator()(const vector<int>& a, const vector<int>& b) const {
- assert(a.size() == b.size());
- vector<T> v(a.size());
- transform(a.begin(), a.end(), b.begin(), v.begin(), plus<T>());
- 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<string> cdec_ini;
- ReadConfig(conf["decoder_config"].as<string>(), &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<string>() != "-") {
- 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<weight_t>& lambdas = decoder->CurrentWeightVector();
- Weights::InitFromFile(conf["input_weights"].as<string>(), &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<weight_t> 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<string>(), &means);
- }
- boost::shared_ptr<BatchOptimizer> o;
- if (rank == 0) {
- const string omethod = conf["optimization_method"].as<string>();
- if (omethod == "rprop")
- o.reset(new RPropOptimizer(num_feats)); // TODO add configuration
- else
- o.reset(new LBFGSOptimizer(num_feats, conf["correction_buffers"].as<int>()));
- cerr << "Optimizer: " << o->Name() << endl;
- }
- double objective = 0;
- vector<double> gradient(num_feats, 0.0);
- vector<double> rcv_grad;
- rcv_grad.clear();
- bool converged = false;
-
- vector<string> corpus, test_corpus;
- ReadTrainingCorpus(conf["training_data"].as<string>(), rank, size, &corpus);
- assert(corpus.size() > 0);
- if (conf.count("test_data"))
- ReadTrainingCorpus(conf["test_data"].as<string>(), 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<double>(), 0);
- swap(gradient, rcv_grad);
- rcv_grad.clear();
-
- reduce(world, observer.trg_words, total_words, std::plus<unsigned>(), 0);
- mpi::reduce(world, objective, to, plus<double>(), 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<double>(), 0);
- reduce(world, cllh_observer.trg_words, test_total_words, std::plus<unsigned>(), 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>();
- 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<weight_t> 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;
-}
-