mirror of
https://github.com/moses-smt/mosesdecoder.git
synced 2024-12-26 05:14:36 +03:00
257 lines
9.6 KiB
C++
257 lines
9.6 KiB
C++
// $Id$
|
|
// vim:tabstop=2
|
|
/***********************************************************************
|
|
K-best Batch MIRA for Moses
|
|
Copyright (C) 2012, National Research Council Canada / Conseil national
|
|
de recherches du Canada
|
|
***********************************************************************/
|
|
|
|
/**
|
|
* k-best Batch Mira, as described in:
|
|
*
|
|
* Colin Cherry and George Foster
|
|
* Batch Tuning Strategies for Statistical Machine Translation
|
|
* NAACL 2012
|
|
*
|
|
* Implemented by colin.cherry@nrc-cnrc.gc.ca
|
|
*
|
|
* To license implementations of any of the other tuners in that paper,
|
|
* please get in touch with any member of NRC Canada's Portage project
|
|
*
|
|
* Input is a set of n-best lists, encoded as feature and score files.
|
|
*
|
|
* Output is a weight file that results from running MIRA on these
|
|
* n-btest lists for J iterations. Will return the set that maximizes
|
|
* training BLEU.
|
|
**/
|
|
|
|
#include <cmath>
|
|
#include <cstddef>
|
|
#include <cstdlib>
|
|
#include <ctime>
|
|
#include <cassert>
|
|
#include <iostream>
|
|
#include <string>
|
|
#include <vector>
|
|
#include <utility>
|
|
#include <algorithm>
|
|
|
|
#include <boost/program_options.hpp>
|
|
#include <boost/scoped_ptr.hpp>
|
|
|
|
#include "util/exception.hh"
|
|
#include "util/random.hh"
|
|
|
|
#include "BleuScorer.h"
|
|
#include "HopeFearDecoder.h"
|
|
#include "MiraFeatureVector.h"
|
|
#include "MiraWeightVector.h"
|
|
|
|
#include "Scorer.h"
|
|
#include "ScorerFactory.h"
|
|
|
|
using namespace std;
|
|
using namespace MosesTuning;
|
|
|
|
namespace po = boost::program_options;
|
|
|
|
int main(int argc, char** argv)
|
|
{
|
|
bool help;
|
|
string denseInitFile;
|
|
string sparseInitFile;
|
|
string type = "nbest";
|
|
string sctype = "BLEU";
|
|
string scconfig = "";
|
|
vector<string> scoreFiles;
|
|
vector<string> featureFiles;
|
|
vector<string> referenceFiles; //for hg mira
|
|
string hgDir;
|
|
int seed;
|
|
string outputFile;
|
|
float c = 0.01; // Step-size cap C
|
|
float decay = 0.999; // Pseudo-corpus decay \gamma
|
|
int n_iters = 60; // Max epochs J
|
|
bool streaming = false; // Stream all k-best lists?
|
|
bool streaming_out = false; // Stream output after each sentence?
|
|
bool no_shuffle = false; // Don't shuffle, even for in memory version
|
|
bool model_bg = false; // Use model for background corpus
|
|
bool verbose = false; // Verbose updates
|
|
bool safe_hope = false; // Model score cannot have more than BLEU_RATIO times more influence than BLEU
|
|
size_t hgPruning = 50; //prune hypergraphs to have this many edges per reference word
|
|
|
|
// Command-line processing follows pro.cpp
|
|
po::options_description desc("Allowed options");
|
|
desc.add_options()
|
|
("help,h", po::value(&help)->zero_tokens()->default_value(false), "Print this help message and exit")
|
|
("type,t", po::value<string>(&type), "Either nbest or hypergraph")
|
|
("sctype", po::value<string>(&sctype), "the scorer type (default BLEU)")
|
|
("scconfig,c", po::value<string>(&scconfig), "configuration string passed to scorer")
|
|
("scfile,S", po::value<vector<string> >(&scoreFiles), "Scorer data files")
|
|
("ffile,F", po::value<vector<string> > (&featureFiles), "Feature data files")
|
|
("hgdir,H", po::value<string> (&hgDir), "Directory containing hypergraphs")
|
|
("reference,R", po::value<vector<string> > (&referenceFiles), "Reference files, only required for hypergraph mira")
|
|
("random-seed,r", po::value<int>(&seed), "Seed for random number generation")
|
|
("output-file,o", po::value<string>(&outputFile), "Output file")
|
|
("cparam,C", po::value<float>(&c), "MIRA C-parameter, lower for more regularization (default 0.01)")
|
|
("decay,D", po::value<float>(&decay), "BLEU background corpus decay rate (default 0.999)")
|
|
("iters,J", po::value<int>(&n_iters), "Number of MIRA iterations to run (default 60)")
|
|
("dense-init,d", po::value<string>(&denseInitFile), "Weight file for dense features. This should have 'name= value' on each line, or (legacy) should be the Moses mert 'init.opt' format.")
|
|
("sparse-init,s", po::value<string>(&sparseInitFile), "Weight file for sparse features")
|
|
("streaming", po::value(&streaming)->zero_tokens()->default_value(false), "Stream n-best lists to save memory, implies --no-shuffle")
|
|
("streaming-out", po::value(&streaming_out)->zero_tokens()->default_value(false), "Stream weights to stdout after each sentence")
|
|
("no-shuffle", po::value(&no_shuffle)->zero_tokens()->default_value(false), "Don't shuffle hypotheses before each epoch")
|
|
("model-bg", po::value(&model_bg)->zero_tokens()->default_value(false), "Use model instead of hope for BLEU background")
|
|
("verbose", po::value(&verbose)->zero_tokens()->default_value(false), "Verbose updates")
|
|
("safe-hope", po::value(&safe_hope)->zero_tokens()->default_value(false), "Mode score's influence on hope decoding is limited")
|
|
("hg-prune", po::value<size_t>(&hgPruning), "Prune hypergraphs to have this many edges per reference word")
|
|
;
|
|
|
|
po::options_description cmdline_options;
|
|
cmdline_options.add(desc);
|
|
po::variables_map vm;
|
|
po::store(po::command_line_parser(argc,argv).
|
|
options(cmdline_options).run(), vm);
|
|
po::notify(vm);
|
|
if (help) {
|
|
cout << "Usage: " + string(argv[0]) + " [options]" << endl;
|
|
cout << desc << endl;
|
|
exit(0);
|
|
}
|
|
|
|
cerr << "kbmira with c=" << c << " decay=" << decay << " no_shuffle=" << no_shuffle << endl;
|
|
|
|
if (vm.count("random-seed")) {
|
|
cerr << "Initialising random seed to " << seed << endl;
|
|
util::rand_init(seed);
|
|
} else {
|
|
cerr << "Initialising random seed from system clock" << endl;
|
|
util::rand_init();
|
|
}
|
|
|
|
|
|
pair<MiraWeightVector*, size_t> ret = InitialiseWeights(denseInitFile, sparseInitFile, type, verbose);
|
|
boost::scoped_ptr<MiraWeightVector> wv(ret.first);
|
|
size_t initDenseSize = ret.second;
|
|
|
|
// Initialize scorer
|
|
if(sctype != "BLEU" && type == "hypergraph") {
|
|
UTIL_THROW(util::Exception, "hypergraph mira only supports BLEU");
|
|
}
|
|
boost::scoped_ptr<Scorer> scorer(ScorerFactory::getScorer(sctype, scconfig));
|
|
|
|
// Initialize background corpus
|
|
vector<ValType> bg(scorer->NumberOfScores(), 1);
|
|
|
|
boost::scoped_ptr<HopeFearDecoder> decoder;
|
|
if (type == "nbest") {
|
|
decoder.reset(new NbestHopeFearDecoder(featureFiles, scoreFiles, streaming, no_shuffle, safe_hope, scorer.get()));
|
|
} else if (type == "hypergraph") {
|
|
decoder.reset(new HypergraphHopeFearDecoder(hgDir, referenceFiles, initDenseSize, streaming, no_shuffle, safe_hope, hgPruning, *wv, scorer.get()));
|
|
} else {
|
|
UTIL_THROW(util::Exception, "Unknown batch mira type: '" << type << "'");
|
|
}
|
|
|
|
// Training loop
|
|
if (!streaming_out)
|
|
cerr << "Initial BLEU = " << decoder->Evaluate(wv->avg()) << endl;
|
|
ValType bestBleu = 0;
|
|
for(int j=0; j<n_iters; j++) {
|
|
// MIRA train for one epoch
|
|
int iNumExamples = 0;
|
|
int iNumUpdates = 0;
|
|
ValType totalLoss = 0.0;
|
|
size_t sentenceIndex = 0;
|
|
for(decoder->reset(); !decoder->finished(); decoder->next()) {
|
|
HopeFearData hfd;
|
|
decoder->HopeFear(bg,*wv,&hfd);
|
|
|
|
// Update weights
|
|
if (!hfd.hopeFearEqual && hfd.hopeBleu > hfd.fearBleu) {
|
|
// Vector difference
|
|
MiraFeatureVector diff = hfd.hopeFeatures - hfd.fearFeatures;
|
|
// Bleu difference
|
|
//assert(hfd.hopeBleu + 1e-8 >= hfd.fearBleu);
|
|
ValType delta = hfd.hopeBleu - hfd.fearBleu;
|
|
// Loss and update
|
|
ValType diff_score = wv->score(diff);
|
|
ValType loss = delta - diff_score;
|
|
if(verbose) {
|
|
cerr << "Updating sent " << sentenceIndex << endl;
|
|
cerr << "Wght: " << *wv << endl;
|
|
cerr << "Hope: " << hfd.hopeFeatures << " BLEU:" << hfd.hopeBleu << " Score:" << wv->score(hfd.hopeFeatures) << endl;
|
|
cerr << "Fear: " << hfd.fearFeatures << " BLEU:" << hfd.fearBleu << " Score:" << wv->score(hfd.fearFeatures) << endl;
|
|
cerr << "Diff: " << diff << " BLEU:" << delta << " Score:" << diff_score << endl;
|
|
cerr << "Loss: " << loss << " Scale: " << 1 << endl;
|
|
cerr << endl;
|
|
}
|
|
if(loss > 0) {
|
|
ValType eta = min(c, loss / diff.sqrNorm());
|
|
wv->update(diff,eta);
|
|
totalLoss+=loss;
|
|
iNumUpdates++;
|
|
}
|
|
// Update BLEU statistics
|
|
for(size_t k=0; k<bg.size(); k++) {
|
|
bg[k]*=decay;
|
|
if(model_bg)
|
|
bg[k]+=hfd.modelStats[k];
|
|
else
|
|
bg[k]+=hfd.hopeStats[k];
|
|
}
|
|
}
|
|
iNumExamples++;
|
|
++sentenceIndex;
|
|
if (streaming_out)
|
|
cout << *wv << endl;
|
|
}
|
|
// Training Epoch summary
|
|
cerr << iNumUpdates << "/" << iNumExamples << " updates"
|
|
<< ", avg loss = " << (totalLoss / iNumExamples);
|
|
|
|
|
|
// Evaluate current average weights
|
|
AvgWeightVector avg = wv->avg();
|
|
ValType bleu = decoder->Evaluate(avg);
|
|
cerr << ", BLEU = " << bleu << endl;
|
|
if(bleu > bestBleu) {
|
|
/*
|
|
size_t num_dense = train->num_dense();
|
|
if(initDenseSize>0 && initDenseSize!=num_dense) {
|
|
cerr << "Error: Initial dense feature count and dense feature count from n-best do not match: "
|
|
<< initDenseSize << "!=" << num_dense << endl;
|
|
exit(1);
|
|
}*/
|
|
// Write to a file
|
|
ostream* out;
|
|
ofstream outFile;
|
|
if (!outputFile.empty() ) {
|
|
outFile.open(outputFile.c_str());
|
|
if (!(outFile)) {
|
|
cerr << "Error: Failed to open " << outputFile << endl;
|
|
exit(1);
|
|
}
|
|
out = &outFile;
|
|
} else {
|
|
out = &cout;
|
|
}
|
|
for(size_t i=0; i<avg.size(); i++) {
|
|
if(i<initDenseSize)
|
|
*out << "F" << i << " " << avg.weight(i) << endl;
|
|
else {
|
|
if(abs(avg.weight(i))>1e-8)
|
|
*out << SparseVector::decode(i-initDenseSize) << " " << avg.weight(i) << endl;
|
|
}
|
|
}
|
|
outFile.close();
|
|
bestBleu = bleu;
|
|
}
|
|
}
|
|
cerr << "Best BLEU = " << bestBleu << endl;
|
|
}
|
|
// --Emacs trickery--
|
|
// Local Variables:
|
|
// mode:c++
|
|
// c-basic-offset:2
|
|
// End:
|