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-rw-r--r--training/mpi_online_optimize.cc322
1 files changed, 322 insertions, 0 deletions
diff --git a/training/mpi_online_optimize.cc b/training/mpi_online_optimize.cc
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+++ b/training/mpi_online_optimize.cc
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+#include <sstream>
+#include <iostream>
+#include <fstream>
+#include <vector>
+#include <cassert>
+#include <cmath>
+
+#include <mpi.h>
+#include <boost/shared_ptr.hpp>
+#include <boost/program_options.hpp>
+#include <boost/program_options/variables_map.hpp>
+
+#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"
+
+using namespace std;
+using boost::shared_ptr;
+namespace po = boost::program_options;
+
+void SanityCheck(const vector<double>& w) {
+ for (int i = 0; i < w.size(); ++i) {
+ assert(!isnan(w[i]));
+ assert(!isinf(w[i]));
+ }
+}
+
+struct FComp {
+ const vector<double>& w_;
+ FComp(const vector<double>& w) : w_(w) {}
+ bool operator()(int a, int b) const {
+ return fabs(w_[a]) > fabs(w_[b]);
+ }
+};
+
+void ShowLargestFeatures(const vector<double>& w) {
+ vector<int> fnums(w.size());
+ for (int i = 0; i < w.size(); ++i)
+ fnums[i] = i;
+ vector<int>::iterator mid = fnums.begin();
+ mid += (w.size() > 10 ? 10 : w.size());
+ partial_sort(fnums.begin(), mid, fnums.end(), FComp(w));
+ cerr << "TOP FEATURES:";
+ for (vector<int>::iterator i = fnums.begin(); i != mid; ++i) {
+ cerr << ' ' << FD::Convert(*i) << '=' << w[*i];
+ }
+ cerr << endl;
+}
+
+void 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 corpus")
+ ("decoder_config,c",po::value<string>(),"Decoder configuration file")
+ ("output_weights,o",po::value<string>()->default_value("-"),"Output feature weights file")
+ ("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)")
+ ("eta_0,e", po::value<double>()->default_value(0.2), "Initial learning rate for SGD (eta_0)")
+ ("L1,1","Use L1 regularization")
+ ("gaussian_prior,g","Use a Gaussian prior on the weights")
+ ("sigma_squared", po::value<double>()->default_value(1.0), "Sigma squared term for spherical 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;
+ MPI::Finalize();
+ exit(1);
+ }
+}
+
+void ReadTrainingCorpus(const string& fname, vector<string>* c) {
+ ReadFile rf(fname);
+ istream& in = *rf.stream();
+ string line;
+ while(in) {
+ getline(in, line);
+ if (!in) break;
+ c->push_back(line);
+ }
+}
+
+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;
+ }
+
+ 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(!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 {
+ }
+ }
+
+ int total_complete;
+ SparseVector<prob_t> cur_model_exp;
+ SparseVector<prob_t> acc_grad;
+ double acc_obj;
+ double cur_obj;
+ 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]);
+ }
+}
+
+int main(int argc, char** argv) {
+ MPI::Init(argc, argv);
+ const int size = MPI::COMM_WORLD.Get_size();
+ const int rank = MPI::COMM_WORLD.Get_rank();
+ SetSilent(true); // turn off verbose decoder output
+ cerr << "MPI: I am " << rank << '/' << size << endl;
+ register_feature_functions();
+ MT19937* rng = NULL;
+ if (rank == 0) rng = new MT19937;
+
+ po::variables_map conf;
+ InitCommandLine(argc, argv, &conf);
+
+ // load initial weights
+ Weights 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";
+ MPI::COMM_WORLD.Abort(1);
+ }
+
+ vector<string> corpus;
+ ReadTrainingCorpus(conf["training_data"].as<string>(), &corpus);
+ assert(corpus.size() > 0);
+
+ std::tr1::shared_ptr<OnlineOptimizer> o;
+ std::tr1::shared_ptr<LearningRateSchedule> lr;
+ if (rank == 0) {
+ // TODO config
+ lr.reset(new ExponentialDecayLearningRate(corpus.size(), conf["eta_0"].as<double>()));
+
+ const string omethod = conf["optimization_method"].as<string>();
+ if (omethod == "sgd") {
+ const double C = 1.0;
+ o.reset(new CumulativeL1OnlineOptimizer(lr, corpus.size(), C));
+ } else {
+ assert(!"fail");
+ }
+ }
+ double objective = 0;
+ vector<double> lambdas;
+ weights.InitVector(&lambdas);
+ bool converged = false;
+
+ TrainingObserver observer;
+ while (!converged) {
+ observer.Reset();
+ if (rank == 0) {
+ cerr << "Starting decoding... (~" << corpus.size() << " sentences / proc)\n";
+ }
+ decoder.SetWeights(lambdas);
+#if 0
+ for (int i = 0; i < corpus.size(); ++i)
+ decoder.Decode(corpus[i], &observer);
+
+ fill(gradient.begin(), gradient.end(), 0);
+ fill(rcv_grad.begin(), rcv_grad.end(), 0);
+ observer.SetLocalGradientAndObjective(&gradient, &objective);
+
+ double to = 0;
+ MPI::COMM_WORLD.Reduce(const_cast<double*>(&gradient.data()[0]), &rcv_grad[0], num_feats, MPI::DOUBLE, MPI::SUM, 0);
+ MPI::COMM_WORLD.Reduce(&objective, &to, 1, MPI::DOUBLE, MPI::SUM, 0);
+ swap(gradient, rcv_grad);
+ objective = to;
+
+ if (rank == 0) { // run optimizer only on rank=0 node
+ 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<double> 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();
+ SanityCheck(lambdas);
+ ShowLargestFeatures(lambdas);
+ weights.InitFromVector(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, true, &svv);
+ } // rank == 0
+ int cint = converged;
+ MPI::COMM_WORLD.Bcast(const_cast<double*>(&lambdas.data()[0]), num_feats, MPI::DOUBLE, 0);
+ MPI::COMM_WORLD.Bcast(&cint, 1, MPI::INT, 0);
+ MPI::COMM_WORLD.Barrier();
+ converged = cint;
+#endif
+ }
+ MPI::Finalize();
+ return 0;
+}