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-rw-r--r--training/dtrain/dtrain.cc807
1 files changed, 168 insertions, 639 deletions
diff --git a/training/dtrain/dtrain.cc b/training/dtrain/dtrain.cc
index ccb50af2..1b7047b0 100644
--- a/training/dtrain/dtrain.cc
+++ b/training/dtrain/dtrain.cc
@@ -1,698 +1,227 @@
#include "dtrain.h"
#include "score.h"
-#include "kbestget.h"
-#include "ksampler.h"
-#include "pairsampling.h"
+#include "sample.h"
+#include "update.h"
using namespace dtrain;
-
-bool
-dtrain_init(int argc, char** argv, po::variables_map* cfg)
-{
- po::options_description ini("Configuration File Options");
- ini.add_options()
- ("input", po::value<string>(), "input file (src)")
- ("refs,r", po::value<string>(), "references")
- ("bitext,b", po::value<string>(), "bitext: 'src ||| tgt'")
- ("output", po::value<string>()->default_value("-"), "output weights file, '-' for STDOUT")
- ("input_weights", po::value<string>(), "input weights file (e.g. from previous iteration)")
- ("decoder_config", po::value<string>(), "configuration file for cdec")
- ("print_weights", po::value<string>(), "weights to print on each iteration")
- ("stop_after", po::value<unsigned>()->default_value(0), "stop after X input sentences")
- ("keep", po::value<bool>()->zero_tokens(), "keep weights files for each iteration")
- ("epochs", po::value<unsigned>()->default_value(10), "# of iterations T (per shard)")
- ("k", po::value<unsigned>()->default_value(100), "how many translations to sample")
- ("sample_from", po::value<string>()->default_value("kbest"), "where to sample translations from: 'kbest', 'forest'")
- ("filter", po::value<string>()->default_value("uniq"), "filter kbest list: 'not', 'uniq'")
- ("pair_sampling", po::value<string>()->default_value("XYX"), "how to sample pairs: 'all', 'XYX' or 'PRO'")
- ("hi_lo", po::value<float>()->default_value(0.1), "hi and lo (X) for XYX (default 0.1), <= 0.5")
- ("pair_threshold", po::value<score_t>()->default_value(0.), "bleu [0,1] threshold to filter pairs")
- ("N", po::value<unsigned>()->default_value(4), "N for Ngrams (BLEU)")
- ("scorer", po::value<string>()->default_value("stupid_bleu"), "scoring: bleu, stupid_, smooth_, approx_, lc_")
- ("learning_rate", po::value<weight_t>()->default_value(1.0), "learning rate")
- ("gamma", po::value<weight_t>()->default_value(0.), "gamma for SVM (0 for perceptron)")
- ("select_weights", po::value<string>()->default_value("last"), "output best, last, avg weights ('VOID' to throw away)")
- ("rescale", po::value<bool>()->zero_tokens(), "rescale weight vector after each input")
- ("l1_reg", po::value<string>()->default_value("none"), "apply l1 regularization as in 'Tsuroka et al' (2010) UNTESTED")
- ("l1_reg_strength", po::value<weight_t>(), "l1 regularization strength")
- ("fselect", po::value<weight_t>()->default_value(-1), "select top x percent (or by threshold) of features after each epoch NOT IMPLEMENTED") // TODO
- ("approx_bleu_d", po::value<score_t>()->default_value(0.9), "discount for approx. BLEU")
- ("scale_bleu_diff", po::value<bool>()->zero_tokens(), "learning rate <- bleu diff of a misranked pair")
- ("loss_margin", po::value<weight_t>()->default_value(0.), "update if no error in pref pair but model scores this near")
- ("max_pairs", po::value<unsigned>()->default_value(std::numeric_limits<unsigned>::max()), "max. # of pairs per Sent.")
- ("pclr", po::value<string>()->default_value("no"), "use a (simple|adagrad) per-coordinate learning rate")
- ("batch", po::value<bool>()->zero_tokens(), "do batch optimization")
- ("repeat", po::value<unsigned>()->default_value(1), "repeat optimization over kbest list this number of times")
- ("check", po::value<bool>()->zero_tokens(), "produce list of loss differentials")
- ("noup", po::value<bool>()->zero_tokens(), "do not update weights");
- po::options_description cl("Command Line Options");
- cl.add_options()
- ("config,c", po::value<string>(), "dtrain config file")
- ("quiet,q", po::value<bool>()->zero_tokens(), "be quiet")
- ("verbose,v", po::value<bool>()->zero_tokens(), "be verbose");
- cl.add(ini);
- po::store(parse_command_line(argc, argv, cl), *cfg);
- if (cfg->count("config")) {
- ifstream ini_f((*cfg)["config"].as<string>().c_str());
- po::store(po::parse_config_file(ini_f, ini), *cfg);
- }
- po::notify(*cfg);
- if (!cfg->count("decoder_config")) {
- cerr << cl << endl;
- return false;
- }
- if ((*cfg)["sample_from"].as<string>() != "kbest"
- && (*cfg)["sample_from"].as<string>() != "forest") {
- cerr << "Wrong 'sample_from' param: '" << (*cfg)["sample_from"].as<string>() << "', use 'kbest' or 'forest'." << endl;
- return false;
- }
- if ((*cfg)["sample_from"].as<string>() == "kbest" && (*cfg)["filter"].as<string>() != "uniq" &&
- (*cfg)["filter"].as<string>() != "not") {
- cerr << "Wrong 'filter' param: '" << (*cfg)["filter"].as<string>() << "', use 'uniq' or 'not'." << endl;
- return false;
- }
- if ((*cfg)["pair_sampling"].as<string>() != "all" && (*cfg)["pair_sampling"].as<string>() != "XYX" &&
- (*cfg)["pair_sampling"].as<string>() != "PRO") {
- cerr << "Wrong 'pair_sampling' param: '" << (*cfg)["pair_sampling"].as<string>() << "'." << endl;
- return false;
- }
- if (cfg->count("hi_lo") && (*cfg)["pair_sampling"].as<string>() != "XYX") {
- cerr << "Warning: hi_lo only works with pair_sampling XYX." << endl;
- }
- if ((*cfg)["hi_lo"].as<float>() > 0.5 || (*cfg)["hi_lo"].as<float>() < 0.01) {
- cerr << "hi_lo must lie in [0.01, 0.5]" << endl;
- return false;
- }
- if ((cfg->count("input")>0 || cfg->count("refs")>0) && cfg->count("bitext")>0) {
- cerr << "Provide 'input' and 'refs' or 'bitext', not both." << endl;
- return false;
- }
- if ((*cfg)["pair_threshold"].as<score_t>() < 0) {
- cerr << "The threshold must be >= 0!" << endl;
- return false;
- }
- if ((*cfg)["select_weights"].as<string>() != "last" && (*cfg)["select_weights"].as<string>() != "best" &&
- (*cfg)["select_weights"].as<string>() != "avg" && (*cfg)["select_weights"].as<string>() != "VOID") {
- cerr << "Wrong 'select_weights' param: '" << (*cfg)["select_weights"].as<string>() << "', use 'last' or 'best'." << endl;
- return false;
- }
- return true;
-}
-
int
main(int argc, char** argv)
{
- // handle most parameters
- po::variables_map cfg;
- if (!dtrain_init(argc, argv, &cfg)) exit(1); // something is wrong
- bool quiet = false;
- if (cfg.count("quiet")) quiet = true;
- bool verbose = false;
- if (cfg.count("verbose")) verbose = true;
- bool noup = false;
- if (cfg.count("noup")) noup = true;
- bool rescale = false;
- if (cfg.count("rescale")) rescale = true;
- bool keep = false;
- if (cfg.count("keep")) keep = true;
-
- const unsigned k = cfg["k"].as<unsigned>();
- const unsigned N = cfg["N"].as<unsigned>();
- const unsigned T = cfg["epochs"].as<unsigned>();
- const unsigned stop_after = cfg["stop_after"].as<unsigned>();
- const string filter_type = cfg["filter"].as<string>();
- const string sample_from = cfg["sample_from"].as<string>();
- const string pair_sampling = cfg["pair_sampling"].as<string>();
- const score_t pair_threshold = cfg["pair_threshold"].as<score_t>();
- const string select_weights = cfg["select_weights"].as<string>();
- const float hi_lo = cfg["hi_lo"].as<float>();
- const score_t approx_bleu_d = cfg["approx_bleu_d"].as<score_t>();
- const unsigned max_pairs = cfg["max_pairs"].as<unsigned>();
- int repeat = cfg["repeat"].as<unsigned>();
- bool check = false;
- if (cfg.count("check")) check = true;
- weight_t loss_margin = cfg["loss_margin"].as<weight_t>();
- bool batch = false;
- if (cfg.count("batch")) batch = true;
- if (loss_margin > 9998.) loss_margin = std::numeric_limits<float>::max();
- bool scale_bleu_diff = false;
- if (cfg.count("scale_bleu_diff")) scale_bleu_diff = true;
- const string pclr = cfg["pclr"].as<string>();
- bool average = false;
- if (select_weights == "avg")
- average = true;
+ // get configuration
+ po::variables_map conf;
+ if (!dtrain_init(argc, argv, &conf))
+ exit(1); // something is wrong
+ const size_t k = conf["k"].as<size_t>();
+ const size_t N = conf["N"].as<size_t>();
+ const size_t T = conf["iterations"].as<size_t>();
+ const weight_t eta = conf["learning_rate"].as<weight_t>();
+ const weight_t error_margin = conf["error_margin"].as<weight_t>();
+ const bool average = conf["average"].as<bool>();
+ const bool keep = conf["keep"].as<bool>();
+ const weight_t l1_reg = conf["l1_reg"].as<weight_t>();
+ const string output_fn = conf["output"].as<string>();
vector<string> print_weights;
- if (cfg.count("print_weights"))
- boost::split(print_weights, cfg["print_weights"].as<string>(), boost::is_any_of(" "));
+ boost::split(print_weights, conf["print_weights"].as<string>(), boost::is_any_of(" "));
// setup decoder
register_feature_functions();
SetSilent(true);
- ReadFile ini_rf(cfg["decoder_config"].as<string>());
- if (!quiet)
- cerr << setw(25) << "cdec cfg " << "'" << cfg["decoder_config"].as<string>() << "'" << endl;
- Decoder decoder(ini_rf.stream());
-
- // scoring metric/scorer
- string scorer_str = cfg["scorer"].as<string>();
- LocalScorer* scorer;
- if (scorer_str == "bleu") {
- scorer = static_cast<BleuScorer*>(new BleuScorer);
- } else if (scorer_str == "stupid_bleu") {
- scorer = static_cast<StupidBleuScorer*>(new StupidBleuScorer);
- } else if (scorer_str == "fixed_stupid_bleu") {
- scorer = static_cast<FixedStupidBleuScorer*>(new FixedStupidBleuScorer);
- } else if (scorer_str == "smooth_bleu") {
- scorer = static_cast<SmoothBleuScorer*>(new SmoothBleuScorer);
- } else if (scorer_str == "sum_bleu") {
- scorer = static_cast<SumBleuScorer*>(new SumBleuScorer);
- } else if (scorer_str == "sumexp_bleu") {
- scorer = static_cast<SumExpBleuScorer*>(new SumExpBleuScorer);
- } else if (scorer_str == "sumwhatever_bleu") {
- scorer = static_cast<SumWhateverBleuScorer*>(new SumWhateverBleuScorer);
- } else if (scorer_str == "approx_bleu") {
- scorer = static_cast<ApproxBleuScorer*>(new ApproxBleuScorer(N, approx_bleu_d));
- } else if (scorer_str == "lc_bleu") {
- scorer = static_cast<LinearBleuScorer*>(new LinearBleuScorer(N));
- } else {
- cerr << "Don't know scoring metric: '" << scorer_str << "', exiting." << endl;
- exit(1);
- }
- vector<score_t> bleu_weights;
- scorer->Init(N, bleu_weights);
+ ReadFile f(conf["decoder_config"].as<string>());
+ Decoder decoder(f.stream());
// setup decoder observer
- MT19937 rng; // random number generator, only for forest sampling
- HypSampler* observer;
- if (sample_from == "kbest")
- observer = static_cast<KBestGetter*>(new KBestGetter(k, filter_type));
- else
- observer = static_cast<KSampler*>(new KSampler(k, &rng));
- observer->SetScorer(scorer);
-
- // init weights
+ ScoredKbest* observer = new ScoredKbest(k, new PerSentenceBleuScorer(N));
+
+ // weights
vector<weight_t>& decoder_weights = decoder.CurrentWeightVector();
- SparseVector<weight_t> lambdas, cumulative_penalties, w_average;
- if (cfg.count("input_weights")) Weights::InitFromFile(cfg["input_weights"].as<string>(), &decoder_weights);
- Weights::InitSparseVector(decoder_weights, &lambdas);
-
- // meta params for perceptron, SVM
- weight_t eta = cfg["learning_rate"].as<weight_t>();
- weight_t gamma = cfg["gamma"].as<weight_t>();
-
- // faster perceptron: consider only misranked pairs, see
- bool faster_perceptron = false;
- if (gamma==0 && loss_margin==0) faster_perceptron = true;
-
- // l1 regularization
- bool l1naive = false;
- bool l1clip = false;
- bool l1cumul = false;
- weight_t l1_reg = 0;
- if (cfg["l1_reg"].as<string>() != "none") {
- string s = cfg["l1_reg"].as<string>();
- if (s == "naive") l1naive = true;
- else if (s == "clip") l1clip = true;
- else if (s == "cumul") l1cumul = true;
- l1_reg = cfg["l1_reg_strength"].as<weight_t>();
+ SparseVector<weight_t> lambdas, w_average;
+ if (conf.count("input_weights")) {
+ Weights::InitFromFile(conf["input_weights"].as<string>(), &decoder_weights);
+ Weights::InitSparseVector(decoder_weights, &lambdas);
}
- // output
- string output_fn = cfg["output"].as<string>();
// input
- bool read_bitext = false;
- string input_fn;
- if (cfg.count("bitext")) {
- read_bitext = true;
- input_fn = cfg["bitext"].as<string>();
- } else {
- input_fn = cfg["input"].as<string>();
- }
+ string input_fn = conf["bitext"].as<string>();
ReadFile input(input_fn);
- // buffer input for t > 0
- vector<string> src_str_buf; // source strings (decoder takes only strings)
- vector<vector<WordID> > ref_ids_buf; // references as WordID vecs
- ReadFile refs;
- string refs_fn;
- if (!read_bitext) {
- refs_fn = cfg["refs"].as<string>();
- refs.Init(refs_fn);
- }
-
- unsigned in_sz = std::numeric_limits<unsigned>::max(); // input index, input size
- vector<pair<score_t, score_t> > all_scores;
- score_t max_score = 0.;
- unsigned best_it = 0;
- float overall_time = 0.;
-
- // output cfg
- if (!quiet) {
- cerr << _p5;
- cerr << endl << "dtrain" << endl << "Parameters:" << endl;
- cerr << setw(25) << "k " << k << endl;
- cerr << setw(25) << "N " << N << endl;
- cerr << setw(25) << "T " << T << endl;
- cerr << setw(25) << "batch " << batch << endl;
- cerr << setw(26) << "scorer '" << scorer_str << "'" << endl;
- if (scorer_str == "approx_bleu")
- cerr << setw(25) << "approx. B discount " << approx_bleu_d << endl;
- cerr << setw(25) << "sample from " << "'" << sample_from << "'" << endl;
- if (sample_from == "kbest")
- cerr << setw(25) << "filter " << "'" << filter_type << "'" << endl;
- if (!scale_bleu_diff) cerr << setw(25) << "learning rate " << eta << endl;
- else cerr << setw(25) << "learning rate " << "bleu diff" << endl;
- cerr << setw(25) << "gamma " << gamma << endl;
- cerr << setw(25) << "loss margin " << loss_margin << endl;
- cerr << setw(25) << "faster perceptron " << faster_perceptron << endl;
- cerr << setw(25) << "pairs " << "'" << pair_sampling << "'" << endl;
- if (pair_sampling == "XYX")
- cerr << setw(25) << "hi lo " << hi_lo << endl;
- cerr << setw(25) << "pair threshold " << pair_threshold << endl;
- cerr << setw(25) << "select weights " << "'" << select_weights << "'" << endl;
- if (cfg.count("l1_reg"))
- cerr << setw(25) << "l1 reg " << l1_reg << " '" << cfg["l1_reg"].as<string>() << "'" << endl;
- if (rescale)
- cerr << setw(25) << "rescale " << rescale << endl;
- cerr << setw(25) << "pclr " << pclr << endl;
- cerr << setw(25) << "max pairs " << max_pairs << endl;
- cerr << setw(25) << "repeat " << repeat << endl;
- //cerr << setw(25) << "test k-best " << test_k_best << endl;
- cerr << setw(25) << "cdec cfg " << "'" << cfg["decoder_config"].as<string>() << "'" << endl;
- cerr << setw(25) << "input " << "'" << input_fn << "'" << endl;
- if (!read_bitext)
- cerr << setw(25) << "refs " << "'" << refs_fn << "'" << endl;
- cerr << setw(25) << "output " << "'" << output_fn << "'" << endl;
- if (cfg.count("input_weights"))
- cerr << setw(25) << "weights in " << "'" << cfg["input_weights"].as<string>() << "'" << endl;
- if (stop_after > 0)
- cerr << setw(25) << "stop_after " << stop_after << endl;
- if (!verbose) cerr << "(a dot represents " << DTRAIN_DOTS << " inputs)" << endl;
- }
-
- // pclr
- SparseVector<weight_t> learning_rates;
- // batch
- SparseVector<weight_t> batch_updates;
- score_t batch_loss;
-
- for (unsigned t = 0; t < T; t++) // T epochs
+ vector<string> buf; // source strings (decoder takes only strings)
+ vector<vector<Ngrams> > buf_ngs; // compute ngrams and lengths of references
+ vector<vector<size_t> > buf_ls; // just once
+ size_t input_sz = 0;
+
+ // output configuration
+ cerr << _p5 << "dtrain" << endl << "Parameters:" << endl;
+ cerr << setw(25) << "k " << k << endl;
+ cerr << setw(25) << "N " << N << endl;
+ cerr << setw(25) << "T " << T << endl;
+ cerr << setw(25) << "learning rate " << eta << endl;
+ cerr << setw(25) << "error margin " << error_margin << endl;
+ cerr << setw(25) << "l1 reg " << l1_reg << endl;
+ cerr << setw(25) << "decoder conf " << "'" << conf["decoder_config"].as<string>() << "'" << endl;
+ cerr << setw(25) << "input " << "'" << input_fn << "'" << endl;
+ cerr << setw(25) << "output " << "'" << output_fn << "'" << endl;
+ if (conf.count("input_weights"))
+ cerr << setw(25) << "weights in " << "'" << conf["input_weights"].as<string>() << "'" << endl;
+ cerr << "(a dot per input)" << endl;
+
+ // meta
+ weight_t best=0., gold_prev=0.;
+ size_t best_iteration = 0;
+ time_t total_time = 0.;
+
+ for (size_t t = 0; t < T; t++) // T iterations
{
time_t start, end;
time(&start);
- score_t score_sum = 0.;
- score_t model_sum(0);
- unsigned ii = 0, rank_errors = 0, margin_violations = 0, npairs = 0, f_count = 0, list_sz = 0, kbest_loss_improve = 0;
- batch_loss = 0.;
- if (!quiet) cerr << "Iteration #" << t+1 << " of " << T << "." << endl;
+ weight_t gold_sum=0., model_sum=0.;
+ size_t i = 0, num_pairs = 0, feature_count = 0, list_sz = 0;
+
+ cerr << "Iteration #" << t+1 << " of " << T << "." << endl;
while(true)
{
+ bool next = true;
- string in;
- string ref;
- bool next = false, stop = false; // next iteration or premature stop
+ // getting input
if (t == 0) {
- if(!getline(*input, in)) next = true;
- if(read_bitext) {
- vector<string> strs;
- boost::algorithm::split_regex(strs, in, boost::regex(" \\|\\|\\| "));
- in = strs[0];
- ref = strs[1];
- }
- } else {
- if (ii == in_sz) next = true; // stop if we reach the end of our input
- }
- // stop after X sentences (but still go on for those)
- if (stop_after > 0 && stop_after == ii && !next) stop = true;
-
- // produce some pretty output
- if (!quiet && !verbose) {
- if (ii == 0) cerr << " ";
- if ((ii+1) % (DTRAIN_DOTS) == 0) {
- cerr << ".";
- cerr.flush();
- }
- if ((ii+1) % (20*DTRAIN_DOTS) == 0) {
- cerr << " " << ii+1 << endl;
- if (!next && !stop) cerr << " ";
- }
- if (stop) {
- if (ii % (20*DTRAIN_DOTS) != 0) cerr << " " << ii << endl;
- cerr << "Stopping after " << stop_after << " input sentences." << endl;
+ string in;
+ if(!getline(*input, in)) {
+ next = false;
} else {
- if (next) {
- if (ii % (20*DTRAIN_DOTS) != 0) cerr << " " << ii << endl;
+ vector<string> parts;
+ boost::algorithm::split_regex(parts, in, boost::regex(" \\|\\|\\| "));
+ buf.push_back(parts[0]);
+ parts.erase(parts.begin());
+ buf_ngs.push_back({});
+ buf_ls.push_back({});
+ for (auto s: parts) {
+ vector<WordID> r;
+ vector<string> tok;
+ boost::split(tok, s, boost::is_any_of(" "));
+ RegisterAndConvert(tok, r);
+ buf_ngs.back().emplace_back(MakeNgrams(r, N));
+ buf_ls.back().push_back(r.size());
}
}
- }
-
- // next iteration
- if (next || stop) break;
-
- // weights
- lambdas.init_vector(&decoder_weights);
-
- // getting input
- vector<WordID> ref_ids; // reference as vector<WordID>
- if (t == 0) {
- if (!read_bitext) {
- getline(*refs, ref);
- }
- vector<string> ref_tok;
- boost::split(ref_tok, ref, boost::is_any_of(" "));
- register_and_convert(ref_tok, ref_ids);
- ref_ids_buf.push_back(ref_ids);
- src_str_buf.push_back(in);
} else {
- ref_ids = ref_ids_buf[ii];
+ next = i<input_sz;
}
- observer->SetRef(ref_ids);
- if (t == 0)
- decoder.Decode(in, observer);
- else
- decoder.Decode(src_str_buf[ii], observer);
- // get (scored) samples
+ // produce some pretty output
+ if (i == 0 || (i+1)%20==0)
+ cerr << " ";
+ cerr << ".";
+ cerr.flush();
+ if (!next)
+ if (i%20 != 0) cerr << " " << i << endl;
+
+ // stop iterating
+ if (!next) break;
+
+ // decode
+ if (t > 0 || i > 0)
+ lambdas.init_vector(&decoder_weights);
+ observer->SetReference(buf_ngs[i], buf_ls[i]);
+ decoder.Decode(buf[i], observer);
vector<ScoredHyp>* samples = observer->GetSamples();
- if (verbose) {
- cerr << "--- ref for " << ii << ": ";
- if (t > 0) printWordIDVec(ref_ids_buf[ii]);
- else printWordIDVec(ref_ids);
- cerr << endl;
- for (unsigned u = 0; u < samples->size(); u++) {
- cerr << _p2 << _np << "[" << u << ". '";
- printWordIDVec((*samples)[u].w);
- cerr << "'" << endl;
- cerr << "SCORE=" << (*samples)[u].score << ",model="<< (*samples)[u].model << endl;
- cerr << "F{" << (*samples)[u].f << "} ]" << endl << endl;
- }
- }
-
- if (repeat == 1) {
- score_sum += (*samples)[0].score; // stats for 1best
- model_sum += (*samples)[0].model;
- }
-
- f_count += observer->get_f_count();
- list_sz += observer->get_sz();
-
- // weight updates
- if (!noup) {
- // get pairs
- vector<pair<ScoredHyp,ScoredHyp> > pairs;
- if (pair_sampling == "all")
- all_pairs(samples, pairs, pair_threshold, max_pairs, faster_perceptron);
- if (pair_sampling == "XYX")
- partXYX(samples, pairs, pair_threshold, max_pairs, faster_perceptron, hi_lo);
- if (pair_sampling == "PRO")
- PROsampling(samples, pairs, pair_threshold, max_pairs);
- int cur_npairs = pairs.size();
- npairs += cur_npairs;
-
- score_t kbest_loss_first = 0.0, kbest_loss_last = 0.0;
-
- if (check) repeat = 2;
- vector<float> losses; // for check
-
- for (vector<pair<ScoredHyp,ScoredHyp> >::iterator it = pairs.begin();
- it != pairs.end(); it++) {
- score_t model_diff = it->first.model - it->second.model;
- score_t loss = max(0.0, -1.0 * model_diff);
- losses.push_back(loss);
- kbest_loss_first += loss;
- }
-
- score_t kbest_loss = 0.0;
- for (int ki=0; ki < repeat; ki++) {
-
- SparseVector<weight_t> lambdas_copy; // for l1 regularization
- SparseVector<weight_t> sum_up; // for pclr
- if (l1naive||l1clip||l1cumul) lambdas_copy = lambdas;
-
- unsigned pair_idx = 0; // for check
- for (vector<pair<ScoredHyp,ScoredHyp> >::iterator it = pairs.begin();
- it != pairs.end(); it++) {
- score_t model_diff = it->first.model - it->second.model;
- score_t loss = max(0.0, -1.0 * model_diff);
-
- if (check && ki==repeat-1) cout << losses[pair_idx] - loss << endl;
- pair_idx++;
-
- if (repeat > 1) {
- model_diff = lambdas.dot(it->first.f) - lambdas.dot(it->second.f);
- kbest_loss += loss;
- }
- bool rank_error = false;
- score_t margin;
- if (faster_perceptron) { // we only have considering misranked pairs
- rank_error = true; // pair sampling already did this for us
- margin = std::numeric_limits<float>::max();
- } else {
- rank_error = model_diff<=0.0;
- margin = fabs(model_diff);
- if (!rank_error && margin < loss_margin) margin_violations++;
- }
- if (rank_error && ki==0) rank_errors++;
- if (scale_bleu_diff) eta = it->first.score - it->second.score;
- if (rank_error || margin < loss_margin) {
- SparseVector<weight_t> diff_vec = it->first.f - it->second.f;
- if (batch) {
- batch_loss += max(0., -1.0 * model_diff);
- batch_updates += diff_vec;
- continue;
- }
- if (pclr != "no") {
- sum_up += diff_vec;
+ // stats for 1best
+ gold_sum += samples->front().gold;
+ model_sum += samples->front().model;
+ feature_count += observer->GetFeatureCount();
+ list_sz += observer->GetSize();
+
+ // get pairs and update
+ vector<pair<ScoredHyp,ScoredHyp> > pairs;
+ SparseVector<weight_t> updates;
+ num_pairs += CollectUpdates(samples, updates, error_margin);
+ SparseVector<weight_t> lambdas_copy;
+ if (l1_reg)
+ lambdas_copy = lambdas;
+ lambdas.plus_eq_v_times_s(updates, eta);
+
+ // l1 regularization
+ // NB: regularization is done after each sentence,
+ // not after every single pair!
+ if (l1_reg) {
+ SparseVector<weight_t>::iterator it = lambdas.begin();
+ for (; it != lambdas.end(); ++it) {
+ if (it->second == 0) continue;
+ if (!lambdas_copy.get(it->first) // new or..
+ || lambdas_copy.get(it->first)!=it->second) // updated feature
+ {
+ weight_t v = it->second;
+ if (v > 0) {
+ it->second = max(0., v - l1_reg);
} else {
- lambdas.plus_eq_v_times_s(diff_vec, eta);
- if (gamma) lambdas.plus_eq_v_times_s(lambdas, -2*gamma*eta*(1./cur_npairs));
+ it->second = min(0., v + l1_reg);
}
}
}
+ }
- // per-coordinate learning rate
- if (pclr != "no") {
- SparseVector<weight_t>::iterator it = sum_up.begin();
- for (; it != sum_up.end(); ++it) {
- if (pclr == "simple") {
- lambdas[it->first] += it->second / max(1.0, learning_rates[it->first]);
- learning_rates[it->first]++;
- } else if (pclr == "adagrad") {
- if (learning_rates[it->first] == 0) {
- lambdas[it->first] += it->second * eta;
- } else {
- lambdas[it->first] += it->second * eta * learning_rates[it->first];
- }
- learning_rates[it->first] += pow(it->second, 2.0);
- }
- }
- }
-
- // l1 regularization
- // please note that this regularizations happen
- // after a _sentence_ -- not after each example/pair!
- if (l1naive) {
- SparseVector<weight_t>::iterator it = lambdas.begin();
- for (; it != lambdas.end(); ++it) {
- if (!lambdas_copy.get(it->first) || lambdas_copy.get(it->first)!=it->second) {
- it->second *= max(0.0000001, eta/(eta+learning_rates[it->first])); // FIXME
- learning_rates[it->first]++;
- it->second -= sign(it->second) * l1_reg;
- }
- }
- } else if (l1clip) {
- SparseVector<weight_t>::iterator it = lambdas.begin();
- for (; it != lambdas.end(); ++it) {
- if (!lambdas_copy.get(it->first) || lambdas_copy.get(it->first)!=it->second) {
- if (it->second != 0) {
- weight_t v = it->second;
- if (v > 0) {
- it->second = max(0., v - l1_reg);
- } else {
- it->second = min(0., v + l1_reg);
- }
- }
- }
- }
- } else if (l1cumul) {
- weight_t acc_penalty = (ii+1) * l1_reg; // ii is the index of the current input
- SparseVector<weight_t>::iterator it = lambdas.begin();
- for (; it != lambdas.end(); ++it) {
- if (!lambdas_copy.get(it->first) || lambdas_copy.get(it->first)!=it->second) {
- if (it->second != 0) {
- weight_t v = it->second;
- weight_t penalized = 0.;
- if (v > 0) {
- penalized = max(0., v-(acc_penalty + cumulative_penalties.get(it->first)));
- } else {
- penalized = min(0., v+(acc_penalty - cumulative_penalties.get(it->first)));
- }
- it->second = penalized;
- cumulative_penalties.set_value(it->first, cumulative_penalties.get(it->first)+penalized);
- }
- }
- }
- }
-
- if (ki==repeat-1) { // done
- kbest_loss_last = kbest_loss;
- if (repeat > 1) {
- score_t best_model = -std::numeric_limits<score_t>::max();
- unsigned best_idx = 0;
- for (unsigned i=0; i < samples->size(); i++) {
- score_t s = lambdas.dot((*samples)[i].f);
- if (s > best_model) {
- best_idx = i;
- best_model = s;
- }
- }
- score_sum += (*samples)[best_idx].score;
- model_sum += best_model;
- }
- }
- } // repeat
-
- if ((kbest_loss_first - kbest_loss_last) >= 0) kbest_loss_improve++;
-
- } // noup
-
- if (rescale) lambdas /= lambdas.l2norm();
-
- ++ii;
+ i++;
} // input loop
- if (t == 0) in_sz = ii; // remember size of input (# lines)
-
+ if (t == 0)
+ input_sz = i; // remember size of input (# lines)
+
+ // update average
+ if (average)
+ w_average += lambdas;
+
+ // stats
+ weight_t gold_avg = gold_sum/(weight_t)input_sz;
+ size_t non_zero = (size_t)lambdas.num_nonzero();
+ cerr << _p5 << _p << "WEIGHTS" << endl;
+ for (auto name: print_weights)
+ cerr << setw(18) << name << " = " << lambdas.get(FD::Convert(name)) << endl;
+ cerr << " ---" << endl;
+ cerr << _np << " 1best avg score: " << gold_avg;
+ cerr << _p << " (" << gold_avg-gold_prev << ")" << endl;
+ cerr << _np << " 1best avg model score: " << model_sum/(weight_t)input_sz << endl;
+ cerr << " avg # pairs: ";
+ cerr << _np << num_pairs/(float)input_sz << endl;
+ cerr << " non-0 feature count: " << non_zero << endl;
+ cerr << " avg list sz: " << list_sz/(float)input_sz << endl;
+ cerr << " avg f count: " << feature_count/(float)list_sz << endl;
+
+ if (gold_avg > best) {
+ best = gold_avg;
+ best_iteration = t;
+ }
+ gold_prev = gold_avg;
- if (batch) {
- lambdas.plus_eq_v_times_s(batch_updates, eta);
- if (gamma) lambdas.plus_eq_v_times_s(lambdas, -2*gamma*eta*(1./npairs));
- batch_updates.clear();
- }
-
- if (average) w_average += lambdas;
-
- if (scorer_str == "approx_bleu" || scorer_str == "lc_bleu") scorer->Reset();
-
- // print some stats
- score_t score_avg = score_sum/(score_t)in_sz;
- score_t model_avg = model_sum/(score_t)in_sz;
- score_t score_diff, model_diff;
- if (t > 0) {
- score_diff = score_avg - all_scores[t-1].first;
- model_diff = model_avg - all_scores[t-1].second;
- } else {
- score_diff = score_avg;
- model_diff = model_avg;
- }
-
- unsigned nonz = 0;
- if (!quiet) nonz = (unsigned)lambdas.num_nonzero();
-
- if (!quiet) {
- cerr << _p5 << _p << "WEIGHTS" << endl;
- for (vector<string>::iterator it = print_weights.begin(); it != print_weights.end(); it++) {
- cerr << setw(18) << *it << " = " << lambdas.get(FD::Convert(*it)) << endl;
- }
- cerr << " ---" << endl;
- cerr << _np << " 1best avg score: " << score_avg;
- cerr << _p << " (" << score_diff << ")" << endl;
- cerr << _np << " 1best avg model score: " << model_avg;
- cerr << _p << " (" << model_diff << ")" << endl;
- cerr << " avg # pairs: ";
- cerr << _np << npairs/(float)in_sz << endl;
- cerr << " avg # rank err: ";
- cerr << rank_errors/(float)in_sz;
- if (faster_perceptron) cerr << " (meaningless)";
- cerr << endl;
- cerr << " avg # margin viol: ";
- cerr << margin_violations/(float)in_sz << endl;
- if (batch) cerr << " batch loss: " << batch_loss << endl;
- cerr << " k-best loss imp: " << ((float)kbest_loss_improve/in_sz)*100 << "%" << endl;
- cerr << " non0 feature count: " << nonz << endl;
- cerr << " avg list sz: " << list_sz/(float)in_sz << endl;
- cerr << " avg f count: " << f_count/(float)list_sz << endl;
- }
-
- pair<score_t,score_t> remember;
- remember.first = score_avg;
- remember.second = model_avg;
- all_scores.push_back(remember);
- if (score_avg > max_score) {
- max_score = score_avg;
- best_it = t;
- }
time (&end);
- float time_diff = difftime(end, start);
- overall_time += time_diff;
- if (!quiet) {
- cerr << _p2 << _np << "(time " << time_diff/60. << " min, ";
- cerr << time_diff/in_sz << " s/S)" << endl;
- }
- if (t+1 != T && !quiet) cerr << endl;
-
- if (noup) break;
+ time_t time_diff = difftime(end, start);
+ total_time += time_diff;
+ cerr << _p2 << _np << "(time " << time_diff/60. << " min, ";
+ cerr << time_diff/input_sz << " s/S)" << endl;
+ if (t+1 != T) cerr << endl;
- // write weights to file
- if (select_weights == "best" || keep) {
+ if (keep) { // keep intermediate weights
lambdas.init_vector(&decoder_weights);
string w_fn = "weights." + boost::lexical_cast<string>(t) + ".gz";
Weights::WriteToFile(w_fn, decoder_weights, true);
}
- if (check) cout << "---" << endl;
-
} // outer loop
- if (average) w_average /= (weight_t)T;
-
- if (!noup) {
- if (!quiet) cerr << endl << "Writing weights file to '" << output_fn << "' ..." << endl;
- if (select_weights == "last" || average) { // last, average
- WriteFile of(output_fn); // works with '-'
- ostream& o = *of.stream();
- o.precision(17);
- o << _np;
- if (average) {
- for (SparseVector<weight_t>::iterator it = w_average.begin(); it != w_average.end(); ++it) {
- if (it->second == 0) continue;
- o << FD::Convert(it->first) << '\t' << it->second << endl;
- }
- } else {
- for (SparseVector<weight_t>::iterator it = lambdas.begin(); it != lambdas.end(); ++it) {
- if (it->second == 0) continue;
- o << FD::Convert(it->first) << '\t' << it->second << endl;
- }
- }
- } else if (select_weights == "VOID") { // do nothing with the weights
- } else { // best
- if (output_fn != "-") {
- CopyFile("weights."+boost::lexical_cast<string>(best_it)+".gz", output_fn);
- } else {
- ReadFile bestw("weights."+boost::lexical_cast<string>(best_it)+".gz");
- string o;
- cout.precision(17);
- cout << _np;
- while(getline(*bestw, o)) cout << o << endl;
- }
- if (!keep) {
- for (unsigned i = 0; i < T; i++) {
- string s = "weights." + boost::lexical_cast<string>(i) + ".gz";
- unlink(s.c_str());
- }
- }
- }
- if (!quiet) cerr << "done" << endl;
+ // final weights
+ if (average) {
+ w_average /= (weight_t)T;
+ w_average.init_vector(decoder_weights);
+ } else if (!keep) {
+ lambdas.init_vector(decoder_weights);
}
+ Weights::WriteToFile(output_fn, decoder_weights, true);
- if (!quiet) {
- cerr << _p5 << _np << endl << "---" << endl << "Best iteration: ";
- cerr << best_it+1 << " [SCORE '" << scorer_str << "'=" << max_score << "]." << endl;
- cerr << "This took " << overall_time/60. << " min." << endl;
- }
+ cerr << _p5 << _np << endl << "---" << endl << "Best iteration: ";
+ cerr << best_iteration+1 << " [GOLD = " << best << "]." << endl;
+ cerr << "This took " << total_time/60. << " min." << endl;
+
+ return 0;
}
generated by cgit v1.2.3 (git 2.39.1) at 2024-12-26 06:27:13 +0000