mosesdecoder/contrib/mira/Main.cpp
Jeroen Vermeulen 10bd942127 Seed the randomizer once, not every time.
This bit of mira code used to re-seed the randomizer on every call, instead
of just once on startup.  The result of time(NULL) was used as a seed, meaning
that every such call to the randomizer within the same second would return the
same value.
2015-04-24 20:00:07 +07:00

1850 lines
84 KiB
C++

/***********************************************************************
Moses - factored phrase-based language decoder
Copyright (C) 2010 University of Edinburgh
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
***********************************************************************/
#include <algorithm>
#include <cstdlib>
#include <ctime>
#include <string>
#include <vector>
#include <map>
#include <boost/program_options.hpp>
#include <boost/algorithm/string.hpp>
#ifdef MPI_ENABLE
#include <boost/mpi.hpp>
namespace mpi = boost::mpi;
#endif
#include "Main.h"
#include "Optimiser.h"
#include "Hildreth.h"
#include "HypothesisQueue.h"
#include "moses/StaticData.h"
#include "moses/ScoreComponentCollection.h"
#include "moses/ThreadPool.h"
#include "mert/BleuScorer.h"
#include "moses/FeatureVector.h"
#include "moses/FF/WordTranslationFeature.h"
#include "moses/FF/PhrasePairFeature.h"
#include "moses/FF/WordPenaltyProducer.h"
#include "moses/LM/Base.h"
#include "util/random.hh"
using namespace Mira;
using namespace std;
using namespace Moses;
namespace po = boost::program_options;
int main(int argc, char** argv)
{
util::rand_init();
size_t rank = 0;
size_t size = 1;
#ifdef MPI_ENABLE
mpi::environment env(argc,argv);
mpi::communicator world;
rank = world.rank();
size = world.size();
#endif
bool help;
int verbosity;
string mosesConfigFile;
string inputFile;
vector<string> referenceFiles;
vector<string> mosesConfigFilesFolds, inputFilesFolds, referenceFilesFolds;
// string coreWeightFile, startWeightFile;
size_t epochs;
string learner;
bool shuffle;
size_t mixingFrequency;
size_t weightDumpFrequency;
string weightDumpStem;
bool scale_margin;
bool scale_update;
size_t n;
size_t batchSize;
bool distinctNbest;
bool accumulateWeights;
float historySmoothing;
bool scaleByInputLength, scaleByAvgInputLength;
bool scaleByInverseLength, scaleByAvgInverseLength;
float scaleByX;
float slack;
bool averageWeights;
bool weightConvergence;
float learning_rate;
float mira_learning_rate;
float perceptron_learning_rate;
string decoder_settings;
float min_weight_change;
bool normaliseWeights, normaliseMargin;
bool print_feature_values;
bool historyBleu ;
bool sentenceBleu;
bool perceptron_update;
bool hope_fear;
bool model_hope_fear;
size_t hope_n, fear_n;
size_t bleu_smoothing_scheme;
float min_oracle_bleu;
float minBleuRatio, maxBleuRatio;
bool boost;
bool decode_hope, decode_fear, decode_model;
string decode_filename;
bool batchEqualsShard;
bool sparseAverage, dumpMixedWeights, sparseNoAverage;
int featureCutoff;
bool pruneZeroWeights;
bool printFeatureCounts, printNbestWithFeatures;
bool avgRefLength;
bool print_weights, print_core_weights, debug_model, scale_lm, scale_wp;
float scale_lm_factor, scale_wp_factor;
bool kbest;
string moses_src;
float sigmoidParam;
float bleuWeight, bleuWeight_hope, bleuWeight_fear;
bool bleu_weight_lm;
float bleu_weight_lm_factor;
bool l1_regularize, l2_regularize, l1_reg_sparse, l2_reg_sparse;
float l1_lambda, l2_lambda;
bool most_violated, most_violated_reg, all_violated, max_bleu_diff;
bool feature_confidence, signed_counts;
float decay_core, decay_sparse, core_r0, sparse_r0;
float bleu_weight_fear_factor;
bool hildreth;
float add2lm;
// compute real sentence Bleu scores on complete translations, disable Bleu feature
bool realBleu, disableBleuFeature;
bool rescaleSlack;
bool makePairs;
bool debug;
bool reg_on_every_mix;
size_t continue_epoch;
bool modelPlusBleu, simpleHistoryBleu;
po::options_description desc("Allowed options");
desc.add_options()
("continue-epoch", po::value<size_t>(&continue_epoch)->default_value(0), "Continue an interrupted experiment from this epoch on")
("freq-reg", po::value<bool>(&reg_on_every_mix)->default_value(false), "Regularize after every weight mixing")
("l1sparse", po::value<bool>(&l1_reg_sparse)->default_value(true), "L1-regularization for sparse weights only")
("l2sparse", po::value<bool>(&l2_reg_sparse)->default_value(true), "L2-regularization for sparse weights only")
("mv-reg", po::value<bool>(&most_violated_reg)->default_value(false), "Regularize most violated constraint")
("most-violated", po::value<bool>(&most_violated)->default_value(false), "Add most violated constraint")
("all-violated", po::value<bool>(&all_violated)->default_value(false), "Add all violated constraints")
("feature-confidence", po::value<bool>(&feature_confidence)->default_value(false), "Confidence-weighted learning")
("signed-counts", po::value<bool>(&signed_counts)->default_value(false), "Use signed feature counts for CWL")
("dbg", po::value<bool>(&debug)->default_value(true), "More debug output")
("make-pairs", po::value<bool>(&makePairs)->default_value(true), "Make pairs of hypotheses for 1slack")
("debug", po::value<bool>(&debug)->default_value(true), "More debug output")
("rescale-slack", po::value<bool>(&rescaleSlack)->default_value(false), "Rescale slack in 1-slack formulation")
("add2lm", po::value<float>(&add2lm)->default_value(0.0), "Add the specified amount to all LM weights")
("hildreth", po::value<bool>(&hildreth)->default_value(false), "Prefer Hildreth over analytical update")
("model-plus-bleu", po::value<bool>(&modelPlusBleu)->default_value(false), "Use the sum of model score and +/- bleu to select hope and fear translations")
("simple-history-bleu", po::value<bool>(&simpleHistoryBleu)->default_value(false), "Simple history Bleu")
("bleu-weight", po::value<float>(&bleuWeight)->default_value(1.0), "Bleu weight used in decoder objective")
("bw-hope", po::value<float>(&bleuWeight_hope)->default_value(-1.0), "Bleu weight used in decoder objective for hope")
("bw-fear", po::value<float>(&bleuWeight_fear)->default_value(-1.0), "Bleu weight used in decoder objective for fear")
("core-r0", po::value<float>(&core_r0)->default_value(1.0), "Start learning rate for core features")
("sparse-r0", po::value<float>(&sparse_r0)->default_value(1.0), "Start learning rate for sparse features")
("decay-core", po::value<float>(&decay_core)->default_value(0.01), "Decay for core feature learning rate")
("decay-sparse", po::value<float>(&decay_sparse)->default_value(0.01), "Decay for sparse feature learning rate")
("tie-bw-to-lm", po::value<bool>(&bleu_weight_lm)->default_value(true), "Make bleu weight depend on lm weight")
("bw-lm-factor", po::value<float>(&bleu_weight_lm_factor)->default_value(2.0), "Make bleu weight depend on lm weight by this factor")
("bw-factor-fear", po::value<float>(&bleu_weight_fear_factor)->default_value(1.0), "Multiply fear weight by this factor")
("accumulate-weights", po::value<bool>(&accumulateWeights)->default_value(false), "Accumulate and average weights over all epochs")
("average-weights", po::value<bool>(&averageWeights)->default_value(false), "Set decoder weights to average weights after each update")
("avg-ref-length", po::value<bool>(&avgRefLength)->default_value(false), "Use average reference length instead of shortest for BLEU score feature")
("batch-equals-shard", po::value<bool>(&batchEqualsShard)->default_value(false), "Batch size is equal to shard size (purely batch)")
("batch-size,b", po::value<size_t>(&batchSize)->default_value(1), "Size of batch that is send to optimiser for weight adjustments")
("bleu-smoothing-scheme", po::value<size_t>(&bleu_smoothing_scheme)->default_value(1), "Set a smoothing scheme for sentence-Bleu: +1 (1), +0.1 (2), papineni (3) (default:1)")
("boost", po::value<bool>(&boost)->default_value(false), "Apply boosting factor to updates on misranked candidates")
("config,f", po::value<string>(&mosesConfigFile), "Moses ini-file")
("configs-folds", po::value<vector<string> >(&mosesConfigFilesFolds), "Moses ini-files, one for each fold")
("debug-model", po::value<bool>(&debug_model)->default_value(false), "Get best model translation for debugging purposes")
("decode-hope", po::value<bool>(&decode_hope)->default_value(false), "Decode dev input set according to hope objective")
("decode-fear", po::value<bool>(&decode_fear)->default_value(false), "Decode dev input set according to fear objective")
("decode-model", po::value<bool>(&decode_model)->default_value(false), "Decode dev input set according to normal objective")
("decode-filename", po::value<string>(&decode_filename), "Filename for Bleu objective translations")
("decoder-settings", po::value<string>(&decoder_settings)->default_value(""), "Decoder settings for tuning runs")
("distinct-nbest", po::value<bool>(&distinctNbest)->default_value(true), "Use n-best list with distinct translations in inference step")
("dump-mixed-weights", po::value<bool>(&dumpMixedWeights)->default_value(false), "Dump mixed weights instead of averaged weights")
("epochs,e", po::value<size_t>(&epochs)->default_value(10), "Number of epochs")
("feature-cutoff", po::value<int>(&featureCutoff)->default_value(-1), "Feature cutoff as additional regularization for sparse features")
("fear-n", po::value<size_t>(&fear_n)->default_value(1), "Number of fear translations used")
("help", po::value(&help)->zero_tokens()->default_value(false), "Print this help message and exit")
("history-bleu", po::value<bool>(&historyBleu)->default_value(false), "Use 1best translations to update the history")
("history-smoothing", po::value<float>(&historySmoothing)->default_value(0.9), "Adjust the factor for history smoothing")
("hope-fear", po::value<bool>(&hope_fear)->default_value(true), "Use only hope and fear translations for optimisation (not model)")
("hope-n", po::value<size_t>(&hope_n)->default_value(2), "Number of hope translations used")
("input-file,i", po::value<string>(&inputFile), "Input file containing tokenised source")
("input-files-folds", po::value<vector<string> >(&inputFilesFolds), "Input files containing tokenised source, one for each fold")
("learner,l", po::value<string>(&learner)->default_value("mira"), "Learning algorithm")
("l1-lambda", po::value<float>(&l1_lambda)->default_value(0.0001), "Lambda for l1-regularization (w_i +/- lambda)")
("l2-lambda", po::value<float>(&l2_lambda)->default_value(0.01), "Lambda for l2-regularization (w_i * (1 - lambda))")
("l1-reg", po::value<bool>(&l1_regularize)->default_value(false), "L1-regularization")
("l2-reg", po::value<bool>(&l2_regularize)->default_value(false), "L2-regularization")
("min-bleu-ratio", po::value<float>(&minBleuRatio)->default_value(-1), "Set a minimum BLEU ratio between hope and fear")
("max-bleu-ratio", po::value<float>(&maxBleuRatio)->default_value(-1), "Set a maximum BLEU ratio between hope and fear")
("max-bleu-diff", po::value<bool>(&max_bleu_diff)->default_value(true), "Select hope/fear with maximum Bleu difference")
("min-oracle-bleu", po::value<float>(&min_oracle_bleu)->default_value(0), "Set a minimum oracle BLEU score")
("min-weight-change", po::value<float>(&min_weight_change)->default_value(0.0001), "Set minimum weight change for stopping criterion")
("mira-learning-rate", po::value<float>(&mira_learning_rate)->default_value(1), "Learning rate for MIRA (fixed or flexible)")
("mixing-frequency", po::value<size_t>(&mixingFrequency)->default_value(10), "How often per epoch to mix weights, when using mpi")
("model-hope-fear", po::value<bool>(&model_hope_fear)->default_value(false), "Use model, hope and fear translations for optimisation")
("moses-src", po::value<string>(&moses_src)->default_value(""), "Moses source directory")
("nbest,n", po::value<size_t>(&n)->default_value(30), "Number of translations in n-best list")
("normalise-weights", po::value<bool>(&normaliseWeights)->default_value(false), "Whether to normalise the updated weights before passing them to the decoder")
("normalise-margin", po::value<bool>(&normaliseMargin)->default_value(false), "Normalise the margin: squash between 0 and 1")
("perceptron-learning-rate", po::value<float>(&perceptron_learning_rate)->default_value(0.01), "Perceptron learning rate")
("print-feature-values", po::value<bool>(&print_feature_values)->default_value(false), "Print out feature values")
("print-feature-counts", po::value<bool>(&printFeatureCounts)->default_value(false), "Print out feature values, print feature list with hope counts after 1st epoch")
("print-nbest-with-features", po::value<bool>(&printNbestWithFeatures)->default_value(false), "Print out feature values, print feature list with hope counts after 1st epoch")
("print-weights", po::value<bool>(&print_weights)->default_value(false), "Print out current weights")
("print-core-weights", po::value<bool>(&print_core_weights)->default_value(true), "Print out current core weights")
("prune-zero-weights", po::value<bool>(&pruneZeroWeights)->default_value(false), "Prune zero-valued sparse feature weights")
("reference-files,r", po::value<vector<string> >(&referenceFiles), "Reference translation files for training")
("reference-files-folds", po::value<vector<string> >(&referenceFilesFolds), "Reference translation files for training, one for each fold")
("kbest", po::value<bool>(&kbest)->default_value(true), "Select hope/fear pairs from a list of nbest translations")
("scale-by-inverse-length", po::value<bool>(&scaleByInverseLength)->default_value(false), "Scale BLEU by (history of) inverse input length")
("scale-by-input-length", po::value<bool>(&scaleByInputLength)->default_value(true), "Scale BLEU by (history of) input length")
("scale-by-avg-input-length", po::value<bool>(&scaleByAvgInputLength)->default_value(false), "Scale BLEU by average input length")
("scale-by-avg-inverse-length", po::value<bool>(&scaleByAvgInverseLength)->default_value(false), "Scale BLEU by average inverse input length")
("scale-by-x", po::value<float>(&scaleByX)->default_value(0.1), "Scale the BLEU score by value x")
("scale-lm", po::value<bool>(&scale_lm)->default_value(true), "Scale the language model feature")
("scale-factor-lm", po::value<float>(&scale_lm_factor)->default_value(0.5), "Scale the language model feature by this factor")
("scale-wp", po::value<bool>(&scale_wp)->default_value(false), "Scale the word penalty feature")
("scale-factor-wp", po::value<float>(&scale_wp_factor)->default_value(2), "Scale the word penalty feature by this factor")
("scale-margin", po::value<bool>(&scale_margin)->default_value(0), "Scale the margin by the Bleu score of the oracle translation")
("sentence-level-bleu", po::value<bool>(&sentenceBleu)->default_value(true), "Use a sentences level Bleu scoring function")
("shuffle", po::value<bool>(&shuffle)->default_value(false), "Shuffle input sentences before processing")
("sigmoid-param", po::value<float>(&sigmoidParam)->default_value(1), "y=sigmoidParam is the axis that this sigmoid approaches")
("slack", po::value<float>(&slack)->default_value(0.05), "Use slack in optimiser")
("sparse-average", po::value<bool>(&sparseAverage)->default_value(false), "Average weights by the number of processes")
("sparse-no-average", po::value<bool>(&sparseNoAverage)->default_value(false), "Don't average sparse weights, just sum")
("stop-weights", po::value<bool>(&weightConvergence)->default_value(true), "Stop when weights converge")
("verbosity,v", po::value<int>(&verbosity)->default_value(0), "Verbosity level")
("weight-dump-frequency", po::value<size_t>(&weightDumpFrequency)->default_value(2), "How often per epoch to dump weights (mpi)")
("weight-dump-stem", po::value<string>(&weightDumpStem)->default_value("weights"), "Stem of filename to use for dumping weights");
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) {
std::cout << "Usage: " + string(argv[0])
+ " -f mosesini-file -i input-file -r reference-file(s) [options]" << std::endl;
std::cout << desc << std::endl;
return 0;
}
const StaticData &staticData = StaticData::Instance();
bool trainWithMultipleFolds = false;
if (mosesConfigFilesFolds.size() > 0 || inputFilesFolds.size() > 0 || referenceFilesFolds.size() > 0) {
if (rank == 0)
cerr << "Training with " << mosesConfigFilesFolds.size() << " folds" << endl;
trainWithMultipleFolds = true;
}
if (dumpMixedWeights && (mixingFrequency != weightDumpFrequency)) {
cerr << "Set mixing frequency = weight dump frequency for dumping mixed weights!" << endl;
exit(1);
}
if ((sparseAverage || sparseNoAverage) && averageWeights) {
cerr << "Parameters --sparse-average 1/--sparse-no-average 1 and --average-weights 1 are incompatible (not implemented)" << endl;
exit(1);
}
if (trainWithMultipleFolds) {
if (!mosesConfigFilesFolds.size()) {
cerr << "Error: No moses ini files specified for training with folds" << endl;
exit(1);
}
if (!inputFilesFolds.size()) {
cerr << "Error: No input files specified for training with folds" << endl;
exit(1);
}
if (!referenceFilesFolds.size()) {
cerr << "Error: No reference files specified for training with folds" << endl;
exit(1);
}
} else {
if (mosesConfigFile.empty()) {
cerr << "Error: No moses ini file specified" << endl;
return 1;
}
if (inputFile.empty()) {
cerr << "Error: No input file specified" << endl;
return 1;
}
if (!referenceFiles.size()) {
cerr << "Error: No reference files specified" << endl;
return 1;
}
}
// load input and references
vector<string> inputSentences;
size_t inputSize = trainWithMultipleFolds? inputFilesFolds.size(): 0;
size_t refSize = trainWithMultipleFolds? referenceFilesFolds.size(): referenceFiles.size();
vector<vector<string> > inputSentencesFolds(inputSize);
vector<vector<string> > referenceSentences(refSize);
// number of cores for each fold
size_t coresPerFold = 0, myFold = 0;
if (trainWithMultipleFolds) {
if (mosesConfigFilesFolds.size() > size) {
cerr << "Number of cores has to be a multiple of the number of folds" << endl;
exit(1);
}
coresPerFold = size/mosesConfigFilesFolds.size();
if (size % coresPerFold > 0) {
cerr << "Number of cores has to be a multiple of the number of folds" << endl;
exit(1);
}
if (rank == 0)
cerr << "Number of cores per fold: " << coresPerFold << endl;
myFold = rank/coresPerFold;
cerr << "Rank " << rank << ", my fold: " << myFold << endl;
}
// NOTE: we do not actually need the references here, because we are reading them in from StaticData
if (trainWithMultipleFolds) {
if (!loadSentences(inputFilesFolds[myFold], inputSentencesFolds[myFold])) {
cerr << "Error: Failed to load input sentences from " << inputFilesFolds[myFold] << endl;
exit(1);
}
VERBOSE(1, "Rank " << rank << " reading inputs from " << inputFilesFolds[myFold] << endl);
if (!loadSentences(referenceFilesFolds[myFold], referenceSentences[myFold])) {
cerr << "Error: Failed to load reference sentences from " << referenceFilesFolds[myFold] << endl;
exit(1);
}
if (referenceSentences[myFold].size() != inputSentencesFolds[myFold].size()) {
cerr << "Error: Input file length (" << inputSentencesFolds[myFold].size() << ") != ("
<< referenceSentences[myFold].size() << ") reference file length (rank " << rank << ")" << endl;
exit(1);
}
VERBOSE(1, "Rank " << rank << " reading references from " << referenceFilesFolds[myFold] << endl);
} else {
if (!loadSentences(inputFile, inputSentences)) {
cerr << "Error: Failed to load input sentences from " << inputFile << endl;
return 1;
}
for (size_t i = 0; i < referenceFiles.size(); ++i) {
if (!loadSentences(referenceFiles[i], referenceSentences[i])) {
cerr << "Error: Failed to load reference sentences from "
<< referenceFiles[i] << endl;
return 1;
}
if (referenceSentences[i].size() != inputSentences.size()) {
cerr << "Error: Input file length (" << inputSentences.size() << ") != ("
<< referenceSentences[i].size() << ") length of reference file " << i
<< endl;
return 1;
}
}
}
if (scaleByAvgInputLength || scaleByInverseLength || scaleByAvgInverseLength)
scaleByInputLength = false;
if (historyBleu || simpleHistoryBleu) {
sentenceBleu = false;
cerr << "Using history Bleu. " << endl;
}
if (kbest) {
realBleu = true;
disableBleuFeature = true;
cerr << "Use kbest lists and real Bleu scores, disable Bleu feature.." << endl;
}
// initialise Moses
// add references to initialize Bleu feature
boost::trim(decoder_settings);
decoder_settings += " -mira -n-best-list - " + boost::lexical_cast<string>(n) + " distinct";
vector<string> decoder_params;
boost::split(decoder_params, decoder_settings, boost::is_any_of("\t "));
// bleu feature
decoder_params.push_back("-feature-add");
decoder_settings = "BleuScoreFeature tuneable=false references=";
if (trainWithMultipleFolds) {
decoder_settings += referenceFilesFolds[myFold];
} else {
decoder_settings += referenceFiles[0];
for (size_t i=1; i < referenceFiles.size(); ++i) {
decoder_settings += ",";
decoder_settings += referenceFiles[i];
}
}
decoder_params.push_back(decoder_settings);
string configFile = trainWithMultipleFolds? mosesConfigFilesFolds[myFold] : mosesConfigFile;
VERBOSE(1, "Rank " << rank << " reading config file from " << configFile << endl);
MosesDecoder* decoder = new MosesDecoder(configFile, verbosity, decoder_params.size(), decoder_params);
decoder->setBleuParameters(disableBleuFeature, sentenceBleu, scaleByInputLength, scaleByAvgInputLength,
scaleByInverseLength, scaleByAvgInverseLength,
scaleByX, historySmoothing, bleu_smoothing_scheme, simpleHistoryBleu);
bool chartDecoding = staticData.IsChart();
// Optionally shuffle the sentences
vector<size_t> order;
if (trainWithMultipleFolds) {
for (size_t i = 0; i < inputSentencesFolds[myFold].size(); ++i) {
order.push_back(i);
}
} else {
if (rank == 0) {
for (size_t i = 0; i < inputSentences.size(); ++i) {
order.push_back(i);
}
}
}
// initialise optimizer
Optimiser* optimiser = NULL;
if (learner == "mira") {
if (rank == 0) {
cerr << "Optimising using Mira" << endl;
cerr << "slack: " << slack << ", learning rate: " << mira_learning_rate << endl;
if (normaliseMargin)
cerr << "sigmoid parameter: " << sigmoidParam << endl;
}
optimiser = new MiraOptimiser(slack, scale_margin, scale_update, boost, normaliseMargin, sigmoidParam);
learning_rate = mira_learning_rate;
perceptron_update = false;
} else if (learner == "perceptron") {
if (rank == 0) {
cerr << "Optimising using Perceptron" << endl;
}
optimiser = new Perceptron();
learning_rate = perceptron_learning_rate;
perceptron_update = true;
model_hope_fear = false; // mira only
hope_fear = false; // mira only
n = 1;
hope_n = 1;
fear_n = 1;
} else {
cerr << "Error: Unknown optimiser: " << learner << endl;
return 1;
}
// resolve parameter dependencies
if (batchSize > 1 && perceptron_update) {
batchSize = 1;
cerr << "Info: Setting batch size to 1 for perceptron update" << endl;
}
if (hope_n == 0)
hope_n = n;
if (fear_n == 0)
fear_n = n;
if (model_hope_fear || kbest)
hope_fear = false; // is true by default
if (learner == "mira" && !(hope_fear || model_hope_fear || kbest)) {
cerr << "Error: Need to select one of parameters --hope-fear/--model-hope-fear/--kbest for mira update." << endl;
return 1;
}
#ifdef MPI_ENABLE
if (!trainWithMultipleFolds)
mpi::broadcast(world, order, 0);
#endif
// Create shards according to the number of processes used
vector<size_t> shard;
if (trainWithMultipleFolds) {
size_t shardSize = order.size()/coresPerFold;
size_t shardStart = (size_t) (shardSize * (rank % coresPerFold));
size_t shardEnd = shardStart + shardSize;
if (rank % coresPerFold == coresPerFold - 1) { // last rank of each fold
shardEnd = order.size();
shardSize = shardEnd - shardStart;
}
VERBOSE(1, "Rank: " << rank << ", shard size: " << shardSize << endl);
VERBOSE(1, "Rank: " << rank << ", shard start: " << shardStart << " shard end: " << shardEnd << endl);
shard.resize(shardSize);
copy(order.begin() + shardStart, order.begin() + shardEnd, shard.begin());
batchSize = 1;
} else {
size_t shardSize = order.size() / size;
size_t shardStart = (size_t) (shardSize * rank);
size_t shardEnd = (size_t) (shardSize * (rank + 1));
if (rank == size - 1) {
shardEnd = order.size();
shardSize = shardEnd - shardStart;
}
VERBOSE(1, "Rank: " << rank << " Shard size: " << shardSize << endl);
VERBOSE(1, "Rank: " << rank << " Shard start: " << shardStart << " Shard end: " << shardEnd << endl);
shard.resize(shardSize);
copy(order.begin() + shardStart, order.begin() + shardEnd, shard.begin());
if (batchEqualsShard)
batchSize = shardSize;
}
// get reference to feature functions
// const vector<FeatureFunction*> &featureFunctions = FeatureFunction::GetFeatureFunctions();
ScoreComponentCollection initialWeights = decoder->getWeights();
if (add2lm != 0) {
const std::vector<const StatefulFeatureFunction*> &statefulFFs = StatefulFeatureFunction::GetStatefulFeatureFunctions();
for (size_t i = 0; i < statefulFFs.size(); ++i) {
const StatefulFeatureFunction *ff = statefulFFs[i];
const LanguageModel *lm = dynamic_cast<const LanguageModel*>(ff);
if (lm) {
float lmWeight = initialWeights.GetScoreForProducer(lm) + add2lm;
initialWeights.Assign(lm, lmWeight);
cerr << "Rank " << rank << ", add " << add2lm << " to lm weight." << endl;
}
}
}
if (normaliseWeights) {
initialWeights.L1Normalise();
cerr << "Rank " << rank << ", normalised initial weights: " << initialWeights << endl;
}
decoder->setWeights(initialWeights);
// set bleu weight to twice the size of the language model weight(s)
if (bleu_weight_lm) {
float lmSum = 0;
const std::vector<const StatefulFeatureFunction*> &statefulFFs = StatefulFeatureFunction::GetStatefulFeatureFunctions();
for (size_t i = 0; i < statefulFFs.size(); ++i) {
const StatefulFeatureFunction *ff = statefulFFs[i];
const LanguageModel *lm = dynamic_cast<const LanguageModel*>(ff);
if (lm) {
lmSum += abs(initialWeights.GetScoreForProducer(lm));
}
}
bleuWeight = lmSum * bleu_weight_lm_factor;
if (!kbest) cerr << "Set bleu weight to lm weight * " << bleu_weight_lm_factor << endl;
}
// bleu weights can be set separately for hope and fear; otherwise they are both set to 'lm weight * bleu_weight_lm_factor'
if (bleuWeight_hope == -1) {
bleuWeight_hope = bleuWeight;
}
if (bleuWeight_fear == -1) {
bleuWeight_fear = bleuWeight;
}
bleuWeight_fear *= bleu_weight_fear_factor;
if (!kbest) {
cerr << "Bleu weight: " << bleuWeight << endl;
cerr << "Bleu weight fear: " << bleuWeight_fear << endl;
}
if (decode_hope || decode_fear || decode_model) {
size_t decode = 1;
if (decode_fear) decode = 2;
if (decode_model) decode = 3;
decodeHopeOrFear(rank, size, decode, decode_filename, inputSentences, decoder, n, bleuWeight);
}
//Main loop:
ScoreComponentCollection cumulativeWeights; // collect weights per epoch to produce an average
ScoreComponentCollection cumulativeWeightsBinary;
size_t numberOfUpdates = 0;
size_t numberOfUpdatesThisEpoch = 0;
time_t now;
time(&now);
cerr << "Rank " << rank << ", " << ctime(&now);
float avgInputLength = 0;
float sumOfInputs = 0;
size_t numberOfInputs = 0;
ScoreComponentCollection mixedWeights;
ScoreComponentCollection mixedWeightsPrevious;
ScoreComponentCollection mixedWeightsBeforePrevious;
ScoreComponentCollection mixedAverageWeights;
ScoreComponentCollection mixedAverageWeightsPrevious;
ScoreComponentCollection mixedAverageWeightsBeforePrevious;
bool stop = false;
// int sumStillViolatedConstraints;
float epsilon = 0.0001;
// Variables for feature confidence
ScoreComponentCollection confidenceCounts, mixedConfidenceCounts, featureLearningRates;
featureLearningRates.UpdateLearningRates(decay_core, decay_sparse, confidenceCounts, core_r0, sparse_r0); //initialise core learning rates
cerr << "Initial learning rates, core: " << core_r0 << ", sparse: " << sparse_r0 << endl;
for (size_t epoch = continue_epoch; epoch < epochs && !stop; ++epoch) {
if (shuffle) {
if (trainWithMultipleFolds || rank == 0) {
cerr << "Rank " << rank << ", epoch " << epoch << ", shuffling input sentences.." << endl;
RandomIndex rindex;
random_shuffle(order.begin(), order.end(), rindex);
}
#ifdef MPI_ENABLE
if (!trainWithMultipleFolds)
mpi::broadcast(world, order, 0);
#endif
// redo shards
if (trainWithMultipleFolds) {
size_t shardSize = order.size()/coresPerFold;
size_t shardStart = (size_t) (shardSize * (rank % coresPerFold));
size_t shardEnd = shardStart + shardSize;
if (rank % coresPerFold == coresPerFold - 1) { // last rank of each fold
shardEnd = order.size();
shardSize = shardEnd - shardStart;
}
VERBOSE(1, "Rank: " << rank << ", shard size: " << shardSize << endl);
VERBOSE(1, "Rank: " << rank << ", shard start: " << shardStart << " shard end: " << shardEnd << endl);
shard.resize(shardSize);
copy(order.begin() + shardStart, order.begin() + shardEnd, shard.begin());
batchSize = 1;
} else {
size_t shardSize = order.size()/size;
size_t shardStart = (size_t) (shardSize * rank);
size_t shardEnd = (size_t) (shardSize * (rank + 1));
if (rank == size - 1) {
shardEnd = order.size();
shardSize = shardEnd - shardStart;
}
VERBOSE(1, "Shard size: " << shardSize << endl);
VERBOSE(1, "Rank: " << rank << " Shard start: " << shardStart << " Shard end: " << shardEnd << endl);
shard.resize(shardSize);
copy(order.begin() + shardStart, order.begin() + shardEnd, shard.begin());
if (batchEqualsShard)
batchSize = shardSize;
}
}
// sum of violated constraints in an epoch
// sumStillViolatedConstraints = 0;
numberOfUpdatesThisEpoch = 0;
// Sum up weights over one epoch, final average uses weights from last epoch
if (!accumulateWeights) {
cumulativeWeights.ZeroAll();
cumulativeWeightsBinary.ZeroAll();
}
// number of weight dumps this epoch
size_t weightMixingThisEpoch = 0;
size_t weightEpochDump = 0;
size_t shardPosition = 0;
vector<size_t>::const_iterator sid = shard.begin();
while (sid != shard.end()) {
// feature values for hypotheses i,j (matrix: batchSize x 3*n x featureValues)
vector<vector<ScoreComponentCollection> > featureValues;
vector<vector<float> > bleuScores;
vector<vector<float> > modelScores;
// variables for hope-fear/perceptron setting
vector<vector<ScoreComponentCollection> > featureValuesHope;
vector<vector<ScoreComponentCollection> > featureValuesFear;
vector<vector<float> > bleuScoresHope;
vector<vector<float> > bleuScoresFear;
vector<vector<float> > modelScoresHope;
vector<vector<float> > modelScoresFear;
// get moses weights
ScoreComponentCollection mosesWeights = decoder->getWeights();
VERBOSE(1, "\nRank " << rank << ", epoch " << epoch << ", weights: " << mosesWeights << endl);
if (historyBleu || simpleHistoryBleu) {
decoder->printBleuFeatureHistory(cerr);
}
// BATCHING: produce nbest lists for all input sentences in batch
vector<float> oracleBleuScores;
vector<float> oracleModelScores;
vector<vector<const Word*> > oneBests;
vector<ScoreComponentCollection> oracleFeatureValues;
vector<size_t> inputLengths;
vector<size_t> ref_ids;
size_t actualBatchSize = 0;
size_t examples_in_batch = 0;
bool skip_example = false;
for (size_t batchPosition = 0; batchPosition < batchSize && sid
!= shard.end(); ++batchPosition) {
string input;
if (trainWithMultipleFolds)
input = inputSentencesFolds[myFold][*sid];
else
input = inputSentences[*sid];
Moses::Sentence *sentence = new Sentence();
stringstream in(input + "\n");
const vector<FactorType> inputFactorOrder = staticData.GetInputFactorOrder();
sentence->Read(in,inputFactorOrder);
cerr << "\nRank " << rank << ", epoch " << epoch << ", input sentence " << *sid << ": \"";
sentence->Print(cerr);
cerr << "\"" << " (batch pos " << batchPosition << ")" << endl;
size_t current_input_length = (*sentence).GetSize();
if (epoch == 0 && (scaleByAvgInputLength || scaleByAvgInverseLength)) {
sumOfInputs += current_input_length;
++numberOfInputs;
avgInputLength = sumOfInputs/numberOfInputs;
decoder->setAvgInputLength(avgInputLength);
cerr << "Rank " << rank << ", epoch 0, average input length: " << avgInputLength << endl;
}
vector<ScoreComponentCollection> newFeatureValues;
vector<float> newScores;
if (model_hope_fear) {
featureValues.push_back(newFeatureValues);
bleuScores.push_back(newScores);
modelScores.push_back(newScores);
}
if (hope_fear || perceptron_update) {
featureValuesHope.push_back(newFeatureValues);
featureValuesFear.push_back(newFeatureValues);
bleuScoresHope.push_back(newScores);
bleuScoresFear.push_back(newScores);
modelScoresHope.push_back(newScores);
modelScoresFear.push_back(newScores);
if (historyBleu || simpleHistoryBleu || debug_model) {
featureValues.push_back(newFeatureValues);
bleuScores.push_back(newScores);
modelScores.push_back(newScores);
}
}
if (kbest) {
// for decoding
featureValues.push_back(newFeatureValues);
bleuScores.push_back(newScores);
modelScores.push_back(newScores);
// for storing selected examples
featureValuesHope.push_back(newFeatureValues);
featureValuesFear.push_back(newFeatureValues);
bleuScoresHope.push_back(newScores);
bleuScoresFear.push_back(newScores);
modelScoresHope.push_back(newScores);
modelScoresFear.push_back(newScores);
}
size_t ref_length;
float avg_ref_length;
if (print_weights)
cerr << "Rank " << rank << ", epoch " << epoch << ", current weights: " << mosesWeights << endl;
if (print_core_weights) {
cerr << "Rank " << rank << ", epoch " << epoch << ", current weights: ";
mosesWeights.PrintCoreFeatures();
cerr << endl;
}
// check LM weight
const std::vector<const StatefulFeatureFunction*> &statefulFFs = StatefulFeatureFunction::GetStatefulFeatureFunctions();
for (size_t i = 0; i < statefulFFs.size(); ++i) {
const StatefulFeatureFunction *ff = statefulFFs[i];
const LanguageModel *lm = dynamic_cast<const LanguageModel*>(ff);
if (lm) {
float lmWeight = mosesWeights.GetScoreForProducer(lm);
cerr << "Rank " << rank << ", epoch " << epoch << ", lm weight: " << lmWeight << endl;
if (lmWeight <= 0) {
cerr << "Rank " << rank << ", epoch " << epoch << ", ERROR: language model weight should never be <= 0." << endl;
mosesWeights.Assign(lm, 0.1);
cerr << "Rank " << rank << ", epoch " << epoch << ", assign lm weights of 0.1" << endl;
}
}
}
// select inference scheme
cerr << "Rank " << rank << ", epoch " << epoch << ", real Bleu? " << realBleu << endl;
if (hope_fear || perceptron_update) {
// HOPE
cerr << "Rank " << rank << ", epoch " << epoch << ", " << hope_n <<
"best hope translations" << endl;
vector< vector<const Word*> > outputHope = decoder->getNBest(input, *sid, hope_n, 1.0, bleuWeight_hope,
featureValuesHope[batchPosition], bleuScoresHope[batchPosition], modelScoresHope[batchPosition],
1, realBleu, distinctNbest, avgRefLength, rank, epoch, "");
vector<const Word*> oracle = outputHope[0];
decoder->cleanup(chartDecoding);
ref_length = decoder->getClosestReferenceLength(*sid, oracle.size());
avg_ref_length = ref_length;
float hope_length_ratio = (float)oracle.size()/ref_length;
cerr << endl;
// count sparse features occurring in hope translation
featureValuesHope[batchPosition][0].IncrementSparseHopeFeatures();
vector<const Word*> bestModel;
if (debug_model || historyBleu || simpleHistoryBleu) {
// MODEL (for updating the history only, using dummy vectors)
cerr << "Rank " << rank << ", epoch " << epoch << ", 1best wrt model score (debug or history)" << endl;
vector< vector<const Word*> > outputModel = decoder->getNBest(input, *sid, n, 0.0, bleuWeight,
featureValues[batchPosition], bleuScores[batchPosition], modelScores[batchPosition],
1, realBleu, distinctNbest, avgRefLength, rank, epoch, "");
bestModel = outputModel[0];
decoder->cleanup(chartDecoding);
cerr << endl;
ref_length = decoder->getClosestReferenceLength(*sid, bestModel.size());
}
// FEAR
//float fear_length_ratio = 0;
float bleuRatioHopeFear = 0;
//int fearSize = 0;
cerr << "Rank " << rank << ", epoch " << epoch << ", " << fear_n << "best fear translations" << endl;
vector< vector<const Word*> > outputFear = decoder->getNBest(input, *sid, fear_n, -1.0, bleuWeight_fear,
featureValuesFear[batchPosition], bleuScoresFear[batchPosition], modelScoresFear[batchPosition],
1, realBleu, distinctNbest, avgRefLength, rank, epoch, "");
vector<const Word*> fear = outputFear[0];
decoder->cleanup(chartDecoding);
ref_length = decoder->getClosestReferenceLength(*sid, fear.size());
avg_ref_length += ref_length;
avg_ref_length /= 2;
//fear_length_ratio = (float)fear.size()/ref_length;
//fearSize = (int)fear.size();
cerr << endl;
for (size_t i = 0; i < fear.size(); ++i)
delete fear[i];
// count sparse features occurring in fear translation
featureValuesFear[batchPosition][0].IncrementSparseFearFeatures();
// Bleu-related example selection
bool skip = false;
bleuRatioHopeFear = bleuScoresHope[batchPosition][0] / bleuScoresFear[batchPosition][0];
if (minBleuRatio != -1 && bleuRatioHopeFear < minBleuRatio)
skip = true;
if(maxBleuRatio != -1 && bleuRatioHopeFear > maxBleuRatio)
skip = true;
// sanity check
if (historyBleu || simpleHistoryBleu) {
if (bleuScores[batchPosition][0] > bleuScoresHope[batchPosition][0] &&
modelScores[batchPosition][0] > modelScoresHope[batchPosition][0]) {
if (abs(bleuScores[batchPosition][0] - bleuScoresHope[batchPosition][0]) > epsilon &&
abs(modelScores[batchPosition][0] - modelScoresHope[batchPosition][0]) > epsilon) {
cerr << "Rank " << rank << ", epoch " << epoch << ", ERROR: MODEL translation better than HOPE translation." << endl;
skip = true;
}
}
if (bleuScoresFear[batchPosition][0] > bleuScores[batchPosition][0] &&
modelScoresFear[batchPosition][0] > modelScores[batchPosition][0]) {
if (abs(bleuScoresFear[batchPosition][0] - bleuScores[batchPosition][0]) > epsilon &&
abs(modelScoresFear[batchPosition][0] - modelScores[batchPosition][0]) > epsilon) {
cerr << "Rank " << rank << ", epoch " << epoch << ", ERROR: FEAR translation better than MODEL translation." << endl;
skip = true;
}
}
}
if (bleuScoresFear[batchPosition][0] > bleuScoresHope[batchPosition][0]) {
if (abs(bleuScoresFear[batchPosition][0] - bleuScoresHope[batchPosition][0]) > epsilon) {
// check if it's an error or a warning
skip = true;
if (modelScoresFear[batchPosition][0] > modelScoresHope[batchPosition][0] && abs(modelScoresFear[batchPosition][0] - modelScoresHope[batchPosition][0]) > epsilon) {
cerr << "Rank " << rank << ", epoch " << epoch << ", ERROR: FEAR translation better than HOPE translation. (abs-diff: " << abs(bleuScoresFear[batchPosition][0] - bleuScoresHope[batchPosition][0]) << ")" <<endl;
} else {
cerr << "Rank " << rank << ", epoch " << epoch << ", WARNING: FEAR translation has better Bleu than HOPE translation. (abs-diff: " << abs(bleuScoresFear[batchPosition][0] - bleuScoresHope[batchPosition][0]) << ")" <<endl;
}
}
}
if (skip) {
cerr << "Rank " << rank << ", epoch " << epoch << ", skip example (" << hope_length_ratio << ", " << bleuRatioHopeFear << ").. " << endl;
featureValuesHope[batchPosition].clear();
featureValuesFear[batchPosition].clear();
bleuScoresHope[batchPosition].clear();
bleuScoresFear[batchPosition].clear();
if (historyBleu || simpleHistoryBleu || debug_model) {
featureValues[batchPosition].clear();
bleuScores[batchPosition].clear();
}
} else {
examples_in_batch++;
// needed for history
if (historyBleu || simpleHistoryBleu) {
inputLengths.push_back(current_input_length);
ref_ids.push_back(*sid);
oneBests.push_back(bestModel);
}
}
}
if (model_hope_fear) {
cerr << "Rank " << rank << ", epoch " << epoch << ", " << n << "best hope translations" << endl;
size_t oraclePos = featureValues[batchPosition].size();
decoder->getNBest(input, *sid, n, 1.0, bleuWeight_hope,
featureValues[batchPosition], bleuScores[batchPosition], modelScores[batchPosition],
0, realBleu, distinctNbest, avgRefLength, rank, epoch, "");
//vector<const Word*> oracle = outputHope[0];
// needed for history
inputLengths.push_back(current_input_length);
ref_ids.push_back(*sid);
decoder->cleanup(chartDecoding);
//ref_length = decoder->getClosestReferenceLength(*sid, oracle.size());
//float hope_length_ratio = (float)oracle.size()/ref_length;
cerr << endl;
oracleFeatureValues.push_back(featureValues[batchPosition][oraclePos]);
oracleBleuScores.push_back(bleuScores[batchPosition][oraclePos]);
oracleModelScores.push_back(modelScores[batchPosition][oraclePos]);
// MODEL
cerr << "Rank " << rank << ", epoch " << epoch << ", " << n << "best wrt model score" << endl;
if (historyBleu || simpleHistoryBleu) {
vector< vector<const Word*> > outputModel = decoder->getNBest(input, *sid, n, 0.0,
bleuWeight, featureValues[batchPosition], bleuScores[batchPosition],
modelScores[batchPosition], 1, realBleu, distinctNbest, avgRefLength, rank, epoch, "");
vector<const Word*> bestModel = outputModel[0];
oneBests.push_back(bestModel);
inputLengths.push_back(current_input_length);
ref_ids.push_back(*sid);
} else {
decoder->getNBest(input, *sid, n, 0.0, bleuWeight,
featureValues[batchPosition], bleuScores[batchPosition], modelScores[batchPosition],
0, realBleu, distinctNbest, avgRefLength, rank, epoch, "");
}
decoder->cleanup(chartDecoding);
//ref_length = decoder->getClosestReferenceLength(*sid, bestModel.size());
//float model_length_ratio = (float)bestModel.size()/ref_length;
cerr << endl;
// FEAR
cerr << "Rank " << rank << ", epoch " << epoch << ", " << n << "best fear translations" << endl;
decoder->getNBest(input, *sid, n, -1.0, bleuWeight_fear,
featureValues[batchPosition], bleuScores[batchPosition], modelScores[batchPosition],
0, realBleu, distinctNbest, avgRefLength, rank, epoch, "");
decoder->cleanup(chartDecoding);
//ref_length = decoder->getClosestReferenceLength(*sid, fear.size());
//float fear_length_ratio = (float)fear.size()/ref_length;
examples_in_batch++;
}
if (kbest) {
// MODEL
cerr << "Rank " << rank << ", epoch " << epoch << ", " << n << "best wrt model score" << endl;
if (historyBleu || simpleHistoryBleu) {
vector< vector<const Word*> > outputModel = decoder->getNBest(input, *sid, n, 0.0,
bleuWeight, featureValues[batchPosition], bleuScores[batchPosition],
modelScores[batchPosition], 1, realBleu, distinctNbest, avgRefLength, rank, epoch, "");
vector<const Word*> bestModel = outputModel[0];
oneBests.push_back(bestModel);
inputLengths.push_back(current_input_length);
ref_ids.push_back(*sid);
} else {
decoder->getNBest(input, *sid, n, 0.0, bleuWeight,
featureValues[batchPosition], bleuScores[batchPosition],
modelScores[batchPosition], 0, realBleu, distinctNbest, avgRefLength, rank, epoch, "");
}
decoder->cleanup(chartDecoding);
//ref_length = decoder->getClosestReferenceLength(*sid, bestModel.size());
//float model_length_ratio = (float)bestModel.size()/ref_length;
cerr << endl;
examples_in_batch++;
HypothesisQueue queueHope(hope_n);
HypothesisQueue queueFear(fear_n);
cerr << endl;
if (most_violated || all_violated) {
float bleuHope = -1000;
float bleuFear = 1000;
int indexHope = -1;
int indexFear = -1;
vector<float> bleuHopeList;
vector<float> bleuFearList;
vector<float> indexHopeList;
vector<float> indexFearList;
if (most_violated)
cerr << "Rank " << rank << ", epoch " << epoch << ", pick pair with most violated constraint" << endl;
else if (all_violated)
cerr << "Rank " << rank << ", epoch " << epoch << ", pick all pairs with violated constraints";
else
cerr << "Rank " << rank << ", epoch " << epoch << ", pick all pairs with hope";
// find best hope, then find fear that violates our constraint most
for (size_t i=0; i<bleuScores[batchPosition].size(); ++i) {
if (abs(bleuScores[batchPosition][i] - bleuHope) < epsilon) { // equal bleu scores
if (modelScores[batchPosition][i] > modelScores[batchPosition][indexHope]) {
if (abs(modelScores[batchPosition][i] - modelScores[batchPosition][indexHope]) > epsilon) {
// better model score
bleuHope = bleuScores[batchPosition][i];
indexHope = i;
}
}
} else if (bleuScores[batchPosition][i] > bleuHope) { // better than current best
bleuHope = bleuScores[batchPosition][i];
indexHope = i;
}
}
float currentViolation = 0;
for (size_t i=0; i<bleuScores[batchPosition].size(); ++i) {
float bleuDiff = bleuHope - bleuScores[batchPosition][i];
float modelDiff = modelScores[batchPosition][indexHope] - modelScores[batchPosition][i];
if ((bleuDiff > epsilon) && (modelDiff < bleuDiff)) {
float diff = bleuDiff - modelDiff;
if (diff > epsilon) {
if (all_violated) {
cerr << ".. adding pair";
bleuHopeList.push_back(bleuHope);
bleuFearList.push_back(bleuScores[batchPosition][i]);
indexHopeList.push_back(indexHope);
indexFearList.push_back(i);
} else if (most_violated && diff > currentViolation) {
currentViolation = diff;
bleuFear = bleuScores[batchPosition][i];
indexFear = i;
cerr << "Rank " << rank << ", epoch " << epoch << ", current violation: " << currentViolation << " (" << modelDiff << " >= " << bleuDiff << ")" << endl;
}
}
}
}
if (most_violated) {
if (currentViolation > 0) {
cerr << "Rank " << rank << ", epoch " << epoch << ", adding pair with violation " << currentViolation << endl;
cerr << "Rank " << rank << ", epoch " << epoch << ", hope: " << bleuHope << " (" << indexHope << "), fear: " << bleuFear << " (" << indexFear << ")" << endl;
bleuScoresHope[batchPosition].push_back(bleuHope);
bleuScoresFear[batchPosition].push_back(bleuFear);
featureValuesHope[batchPosition].push_back(featureValues[batchPosition][indexHope]);
featureValuesFear[batchPosition].push_back(featureValues[batchPosition][indexFear]);
float modelScoreHope = modelScores[batchPosition][indexHope];
float modelScoreFear = modelScores[batchPosition][indexFear];
if (most_violated_reg) {
// reduce model score difference by factor ~0.5
float reg = currentViolation/4;
modelScoreHope += abs(reg);
modelScoreFear -= abs(reg);
float newViolation = (bleuHope - bleuFear) - (modelScoreHope - modelScoreFear);
cerr << "Rank " << rank << ", epoch " << epoch << ", regularized violation: " << newViolation << endl;
}
modelScoresHope[batchPosition].push_back(modelScoreHope);
modelScoresFear[batchPosition].push_back(modelScoreFear);
featureValues[batchPosition][indexHope].IncrementSparseHopeFeatures();
featureValues[batchPosition][indexFear].IncrementSparseFearFeatures();
} else {
cerr << "Rank " << rank << ", epoch " << epoch << ", no violated constraint found." << endl;
skip_example = 1;
}
} else cerr << endl;
}
if (max_bleu_diff) {
cerr << "Rank " << rank << ", epoch " << epoch << ", pick pair with max Bleu diff from list: " << bleuScores[batchPosition].size() << endl;
for (size_t i=0; i<bleuScores[batchPosition].size(); ++i) {
float hopeScore = bleuScores[batchPosition][i];
if (modelPlusBleu) hopeScore += modelScores[batchPosition][i];
BleuIndexPair hope(hopeScore, i);
queueHope.Push(hope);
float fearScore = -1*(bleuScores[batchPosition][i]);
if (modelPlusBleu) fearScore += modelScores[batchPosition][i];
BleuIndexPair fear(fearScore, i);
queueFear.Push(fear);
}
skip_example = 0;
}
cerr << endl;
vector<BleuIndexPair> hopeList, fearList;
for (size_t i=0; i<hope_n && !queueHope.Empty(); ++i) hopeList.push_back(queueHope.Pop());
for (size_t i=0; i<fear_n && !queueFear.Empty(); ++i) fearList.push_back(queueFear.Pop());
for (size_t i=0; i<hopeList.size(); ++i) {
//float bleuHope = hopeList[i].first;
size_t indexHope = hopeList[i].second;
float bleuHope = bleuScores[batchPosition][indexHope];
for (size_t j=0; j<fearList.size(); ++j) {
//float bleuFear = -1*(fearList[j].first);
size_t indexFear = fearList[j].second;
float bleuFear = bleuScores[batchPosition][indexFear];
cerr << "Rank " << rank << ", epoch " << epoch << ", hope: " << bleuHope << " (" << indexHope << "), fear: " << bleuFear << " (" << indexFear << ")" << endl;
bleuScoresHope[batchPosition].push_back(bleuHope);
bleuScoresFear[batchPosition].push_back(bleuFear);
featureValuesHope[batchPosition].push_back(featureValues[batchPosition][indexHope]);
featureValuesFear[batchPosition].push_back(featureValues[batchPosition][indexFear]);
float modelScoreHope = modelScores[batchPosition][indexHope];
float modelScoreFear = modelScores[batchPosition][indexFear];
modelScoresHope[batchPosition].push_back(modelScoreHope);
modelScoresFear[batchPosition].push_back(modelScoreFear);
featureValues[batchPosition][indexHope].IncrementSparseHopeFeatures();
featureValues[batchPosition][indexFear].IncrementSparseFearFeatures();
}
}
if (!makePairs)
cerr << "Rank " << rank << ", epoch " << epoch << "summing up hope and fear vectors, no pairs" << endl;
}
// next input sentence
++sid;
++actualBatchSize;
++shardPosition;
} // end of batch loop
if (examples_in_batch == 0 || (kbest && skip_example)) {
cerr << "Rank " << rank << ", epoch " << epoch << ", batch is empty." << endl;
} else {
vector<vector<float> > losses(actualBatchSize);
if (model_hope_fear) {
// Set loss for each sentence as BLEU(oracle) - BLEU(hypothesis)
for (size_t batchPosition = 0; batchPosition < actualBatchSize; ++batchPosition) {
for (size_t j = 0; j < bleuScores[batchPosition].size(); ++j) {
losses[batchPosition].push_back(oracleBleuScores[batchPosition] - bleuScores[batchPosition][j]);
}
}
}
// set weight for bleu feature to 0 before optimizing
vector<FeatureFunction*>::const_iterator iter;
const vector<FeatureFunction*> &featureFunctions2 = FeatureFunction::GetFeatureFunctions();
for (iter = featureFunctions2.begin(); iter != featureFunctions2.end(); ++iter) {
if ((*iter)->GetScoreProducerDescription() == "BleuScoreFeature") {
mosesWeights.Assign(*iter, 0);
break;
}
}
// scale LM feature (to avoid rapid changes)
if (scale_lm) {
cerr << "scale lm" << endl;
const std::vector<const StatefulFeatureFunction*> &statefulFFs = StatefulFeatureFunction::GetStatefulFeatureFunctions();
for (size_t i = 0; i < statefulFFs.size(); ++i) {
const StatefulFeatureFunction *ff = statefulFFs[i];
const LanguageModel *lm = dynamic_cast<const LanguageModel*>(ff);
if (lm) {
// scale down score
if (model_hope_fear) {
scaleFeatureScore(lm, scale_lm_factor, featureValues, rank, epoch);
} else {
scaleFeatureScore(lm, scale_lm_factor, featureValuesHope, rank, epoch);
scaleFeatureScore(lm, scale_lm_factor, featureValuesFear, rank, epoch);
}
}
}
}
// scale WP
if (scale_wp) {
// scale up weight
WordPenaltyProducer &wp = WordPenaltyProducer::InstanceNonConst();
// scale down score
if (model_hope_fear) {
scaleFeatureScore(&wp, scale_wp_factor, featureValues, rank, epoch);
} else {
scaleFeatureScore(&wp, scale_wp_factor, featureValuesHope, rank, epoch);
scaleFeatureScore(&wp, scale_wp_factor, featureValuesFear, rank, epoch);
}
}
// print out the feature values
if (print_feature_values) {
cerr << "\nRank " << rank << ", epoch " << epoch << ", feature values: " << endl;
if (model_hope_fear) printFeatureValues(featureValues);
else {
cerr << "hope: " << endl;
printFeatureValues(featureValuesHope);
cerr << "fear: " << endl;
printFeatureValues(featureValuesFear);
}
}
// apply learning rates to feature vectors before optimization
if (feature_confidence) {
cerr << "Rank " << rank << ", epoch " << epoch << ", apply feature learning rates with decays " << decay_core << "/" << decay_sparse << ": " << featureLearningRates << endl;
if (model_hope_fear) {
applyPerFeatureLearningRates(featureValues, featureLearningRates, sparse_r0);
} else {
applyPerFeatureLearningRates(featureValuesHope, featureLearningRates, sparse_r0);
applyPerFeatureLearningRates(featureValuesFear, featureLearningRates, sparse_r0);
}
} else {
// apply fixed learning rates
cerr << "Rank " << rank << ", epoch " << epoch << ", apply fixed learning rates, core: " << core_r0 << ", sparse: " << sparse_r0 << endl;
if (core_r0 != 1.0 || sparse_r0 != 1.0) {
if (model_hope_fear) {
applyLearningRates(featureValues, core_r0, sparse_r0);
} else {
applyLearningRates(featureValuesHope, core_r0, sparse_r0);
applyLearningRates(featureValuesFear, core_r0, sparse_r0);
}
}
}
// Run optimiser on batch:
VERBOSE(1, "\nRank " << rank << ", epoch " << epoch << ", run optimiser:" << endl);
size_t update_status = 1;
ScoreComponentCollection weightUpdate;
if (perceptron_update) {
vector<vector<float> > dummy1;
update_status = optimiser->updateWeightsHopeFear( weightUpdate, featureValuesHope,
featureValuesFear, dummy1, dummy1, dummy1, dummy1, learning_rate, rank, epoch);
} else if (hope_fear) {
if (bleuScoresHope[0][0] >= min_oracle_bleu) {
if (hope_n == 1 && fear_n ==1 && batchSize == 1 && !hildreth) {
update_status = ((MiraOptimiser*) optimiser)->updateWeightsAnalytically(weightUpdate,
featureValuesHope[0][0], featureValuesFear[0][0], bleuScoresHope[0][0],
bleuScoresFear[0][0], modelScoresHope[0][0], modelScoresFear[0][0], learning_rate, rank, epoch);
} else
update_status = optimiser->updateWeightsHopeFear(weightUpdate, featureValuesHope,
featureValuesFear, bleuScoresHope, bleuScoresFear, modelScoresHope,
modelScoresFear, learning_rate, rank, epoch);
} else
update_status = 1;
} else if (kbest) {
if (batchSize == 1 && featureValuesHope[0].size() == 1 && !hildreth) {
cerr << "Rank " << rank << ", epoch " << epoch << ", model score hope: " << modelScoresHope[0][0] << endl;
cerr << "Rank " << rank << ", epoch " << epoch << ", model score fear: " << modelScoresFear[0][0] << endl;
update_status = ((MiraOptimiser*) optimiser)->updateWeightsAnalytically(
weightUpdate, featureValuesHope[0][0], featureValuesFear[0][0],
bleuScoresHope[0][0], bleuScoresFear[0][0], modelScoresHope[0][0],
modelScoresFear[0][0], learning_rate, rank, epoch);
} else {
cerr << "Rank " << rank << ", epoch " << epoch << ", model score hope: " << modelScoresHope[0][0] << endl;
cerr << "Rank " << rank << ", epoch " << epoch << ", model score fear: " << modelScoresFear[0][0] << endl;
update_status = optimiser->updateWeightsHopeFear(weightUpdate, featureValuesHope,
featureValuesFear, bleuScoresHope, bleuScoresFear, modelScoresHope,
modelScoresFear, learning_rate, rank, epoch);
}
} else {
// model_hope_fear
update_status = ((MiraOptimiser*) optimiser)->updateWeights(weightUpdate,
featureValues, losses, bleuScores, modelScores, oracleFeatureValues,
oracleBleuScores, oracleModelScores, learning_rate, rank, epoch);
}
// sumStillViolatedConstraints += update_status;
if (update_status == 0) { // if weights were updated
// apply weight update
if (debug)
cerr << "Rank " << rank << ", epoch " << epoch << ", update: " << weightUpdate << endl;
if (feature_confidence) {
// update confidence counts based on weight update
confidenceCounts.UpdateConfidenceCounts(weightUpdate, signed_counts);
// update feature learning rates
featureLearningRates.UpdateLearningRates(decay_core, decay_sparse, confidenceCounts, core_r0, sparse_r0);
}
// apply weight update to Moses weights
mosesWeights.PlusEquals(weightUpdate);
if (normaliseWeights)
mosesWeights.L1Normalise();
cumulativeWeights.PlusEquals(mosesWeights);
if (sparseAverage) {
ScoreComponentCollection binary;
binary.SetToBinaryOf(mosesWeights);
cumulativeWeightsBinary.PlusEquals(binary);
}
++numberOfUpdates;
++numberOfUpdatesThisEpoch;
if (averageWeights) {
ScoreComponentCollection averageWeights(cumulativeWeights);
if (accumulateWeights) {
averageWeights.DivideEquals(numberOfUpdates);
} else {
averageWeights.DivideEquals(numberOfUpdatesThisEpoch);
}
mosesWeights = averageWeights;
}
// set new Moses weights
decoder->setWeights(mosesWeights);
//cerr << "Rank " << rank << ", epoch " << epoch << ", new weights: " << mosesWeights << endl;
}
// update history (for approximate document Bleu)
if (historyBleu || simpleHistoryBleu) {
for (size_t i = 0; i < oneBests.size(); ++i)
cerr << "Rank " << rank << ", epoch " << epoch << ", update history with 1best length: " << oneBests[i].size() << " ";
decoder->updateHistory(oneBests, inputLengths, ref_ids, rank, epoch);
deleteTranslations(oneBests);
}
} // END TRANSLATE AND UPDATE BATCH
// size of all shards except for the last one
size_t generalShardSize;
if (trainWithMultipleFolds)
generalShardSize = order.size()/coresPerFold;
else
generalShardSize = order.size()/size;
size_t mixing_base = mixingFrequency == 0 ? 0 : generalShardSize / mixingFrequency;
size_t dumping_base = weightDumpFrequency == 0 ? 0 : generalShardSize / weightDumpFrequency;
bool mix = evaluateModulo(shardPosition, mixing_base, actualBatchSize);
// mix weights?
if (mix) {
#ifdef MPI_ENABLE
cerr << "Rank " << rank << ", epoch " << epoch << ", mixing weights.. " << endl;
// collect all weights in mixedWeights and divide by number of processes
mpi::reduce(world, mosesWeights, mixedWeights, SCCPlus(), 0);
// mix confidence counts
//mpi::reduce(world, confidenceCounts, mixedConfidenceCounts, SCCPlus(), 0);
ScoreComponentCollection totalBinary;
if (sparseAverage) {
ScoreComponentCollection binary;
binary.SetToBinaryOf(mosesWeights);
mpi::reduce(world, binary, totalBinary, SCCPlus(), 0);
}
if (rank == 0) {
// divide by number of processes
if (sparseNoAverage)
mixedWeights.CoreDivideEquals(size); // average only core weights
else if (sparseAverage)
mixedWeights.DivideEquals(totalBinary);
else
mixedWeights.DivideEquals(size);
// divide confidence counts
//mixedConfidenceCounts.DivideEquals(size);
// normalise weights after averaging
if (normaliseWeights) {
mixedWeights.L1Normalise();
}
++weightMixingThisEpoch;
if (pruneZeroWeights) {
size_t pruned = mixedWeights.PruneZeroWeightFeatures();
cerr << "Rank " << rank << ", epoch " << epoch << ", "
<< pruned << " zero-weighted features pruned from mixedWeights." << endl;
pruned = cumulativeWeights.PruneZeroWeightFeatures();
cerr << "Rank " << rank << ", epoch " << epoch << ", "
<< pruned << " zero-weighted features pruned from cumulativeWeights." << endl;
}
if (featureCutoff != -1 && weightMixingThisEpoch == mixingFrequency) {
size_t pruned = mixedWeights.PruneSparseFeatures(featureCutoff);
cerr << "Rank " << rank << ", epoch " << epoch << ", "
<< pruned << " features pruned from mixedWeights." << endl;
pruned = cumulativeWeights.PruneSparseFeatures(featureCutoff);
cerr << "Rank " << rank << ", epoch " << epoch << ", "
<< pruned << " features pruned from cumulativeWeights." << endl;
}
if (weightMixingThisEpoch == mixingFrequency || reg_on_every_mix) {
if (l1_regularize) {
size_t pruned;
if (l1_reg_sparse)
pruned = mixedWeights.SparseL1Regularize(l1_lambda);
else
pruned = mixedWeights.L1Regularize(l1_lambda);
cerr << "Rank " << rank << ", epoch " << epoch << ", "
<< "l1-reg. on mixedWeights with lambda=" << l1_lambda << ", pruned: " << pruned << endl;
}
if (l2_regularize) {
if (l2_reg_sparse)
mixedWeights.SparseL2Regularize(l2_lambda);
else
mixedWeights.L2Regularize(l2_lambda);
cerr << "Rank " << rank << ", epoch " << epoch << ", "
<< "l2-reg. on mixedWeights with lambda=" << l2_lambda << endl;
}
}
}
// broadcast average weights from process 0
mpi::broadcast(world, mixedWeights, 0);
decoder->setWeights(mixedWeights);
mosesWeights = mixedWeights;
// broadcast summed confidence counts
//mpi::broadcast(world, mixedConfidenceCounts, 0);
//confidenceCounts = mixedConfidenceCounts;
#endif
#ifndef MPI_ENABLE
//cerr << "\nRank " << rank << ", no mixing, weights: " << mosesWeights << endl;
mixedWeights = mosesWeights;
#endif
} // end mixing
// Dump weights?
if (trainWithMultipleFolds || weightEpochDump == weightDumpFrequency) {
// dump mixed weights at end of every epoch to enable continuing a crashed experiment
// (for jackknife every time the weights are mixed)
ostringstream filename;
if (epoch < 10)
filename << weightDumpStem << "_mixed_0" << epoch;
else
filename << weightDumpStem << "_mixed_" << epoch;
if (weightDumpFrequency > 1)
filename << "_" << weightEpochDump;
mixedWeights.Save(filename.str());
cerr << "Dumping mixed weights during epoch " << epoch << " to " << filename.str() << endl << endl;
}
if (dumpMixedWeights) {
if (mix && rank == 0 && !weightDumpStem.empty()) {
// dump mixed weights instead of average weights
ostringstream filename;
if (epoch < 10)
filename << weightDumpStem << "_0" << epoch;
else
filename << weightDumpStem << "_" << epoch;
if (weightDumpFrequency > 1)
filename << "_" << weightEpochDump;
cerr << "Dumping mixed weights during epoch " << epoch << " to " << filename.str() << endl << endl;
mixedWeights.Save(filename.str());
++weightEpochDump;
}
} else {
if (evaluateModulo(shardPosition, dumping_base, actualBatchSize)) {
cerr << "Rank " << rank << ", epoch " << epoch << ", dump weights.. (pos: " << shardPosition << ", base: " << dumping_base << ")" << endl;
ScoreComponentCollection tmpAverageWeights(cumulativeWeights);
bool proceed = false;
if (accumulateWeights) {
if (numberOfUpdates > 0) {
tmpAverageWeights.DivideEquals(numberOfUpdates);
proceed = true;
}
} else {
if (numberOfUpdatesThisEpoch > 0) {
if (sparseNoAverage) // average only core weights
tmpAverageWeights.CoreDivideEquals(numberOfUpdatesThisEpoch);
else if (sparseAverage)
tmpAverageWeights.DivideEquals(cumulativeWeightsBinary);
else
tmpAverageWeights.DivideEquals(numberOfUpdatesThisEpoch);
proceed = true;
}
}
if (proceed) {
#ifdef MPI_ENABLE
// average across processes
mpi::reduce(world, tmpAverageWeights, mixedAverageWeights, SCCPlus(), 0);
ScoreComponentCollection totalBinary;
if (sparseAverage) {
ScoreComponentCollection binary;
binary.SetToBinaryOf(mosesWeights);
mpi::reduce(world, binary, totalBinary, SCCPlus(), 0);
}
#endif
#ifndef MPI_ENABLE
mixedAverageWeights = tmpAverageWeights;
//FIXME: What do to for non-mpi version
ScoreComponentCollection totalBinary;
#endif
if (rank == 0 && !weightDumpStem.empty()) {
// divide by number of processes
if (sparseNoAverage)
mixedAverageWeights.CoreDivideEquals(size); // average only core weights
else if (sparseAverage)
mixedAverageWeights.DivideEquals(totalBinary);
else
mixedAverageWeights.DivideEquals(size);
// normalise weights after averaging
if (normaliseWeights) {
mixedAverageWeights.L1Normalise();
}
// dump final average weights
ostringstream filename;
if (epoch < 10) {
filename << weightDumpStem << "_0" << epoch;
} else {
filename << weightDumpStem << "_" << epoch;
}
if (weightDumpFrequency > 1) {
filename << "_" << weightEpochDump;
}
/*if (accumulateWeights) {
cerr << "\nMixed average weights (cumulative) during epoch " << epoch << ": " << mixedAverageWeights << endl;
} else {
cerr << "\nMixed average weights during epoch " << epoch << ": " << mixedAverageWeights << endl;
}*/
cerr << "Dumping mixed average weights during epoch " << epoch << " to " << filename.str() << endl << endl;
mixedAverageWeights.Save(filename.str());
++weightEpochDump;
if (weightEpochDump == weightDumpFrequency) {
if (l1_regularize) {
size_t pruned = mixedAverageWeights.SparseL1Regularize(l1_lambda);
cerr << "Rank " << rank << ", epoch " << epoch << ", "
<< "l1-reg. on mixedAverageWeights with lambda=" << l1_lambda << ", pruned: " << pruned << endl;
}
if (l2_regularize) {
mixedAverageWeights.SparseL2Regularize(l2_lambda);
cerr << "Rank " << rank << ", epoch " << epoch << ", "
<< "l2-reg. on mixedAverageWeights with lambda=" << l2_lambda << endl;
}
if (l1_regularize || l2_regularize) {
filename << "_reg";
cerr << "Dumping regularized mixed average weights during epoch " << epoch << " to " << filename.str() << endl << endl;
mixedAverageWeights.Save(filename.str());
}
}
if (weightEpochDump == weightDumpFrequency && printFeatureCounts) {
// print out all features with counts
stringstream s1, s2;
s1 << "sparse_feature_hope_counts" << "_" << epoch;
s2 << "sparse_feature_fear_counts" << "_" << epoch;
ofstream sparseFeatureCountsHope(s1.str().c_str());
ofstream sparseFeatureCountsFear(s2.str().c_str());
mixedAverageWeights.PrintSparseHopeFeatureCounts(sparseFeatureCountsHope);
mixedAverageWeights.PrintSparseFearFeatureCounts(sparseFeatureCountsFear);
sparseFeatureCountsHope.close();
sparseFeatureCountsFear.close();
}
}
}
}// end dumping
} // end if dump
} // end of shard loop, end of this epoch
cerr << "Rank " << rank << ", epoch " << epoch << ", end of epoch.." << endl;
if (historyBleu || simpleHistoryBleu) {
cerr << "Bleu feature history after epoch " << epoch << endl;
decoder->printBleuFeatureHistory(cerr);
}
// cerr << "Rank " << rank << ", epoch " << epoch << ", sum of violated constraints: " << sumStillViolatedConstraints << endl;
// Check whether there were any weight updates during this epoch
size_t sumUpdates;
size_t *sendbuf_uint, *recvbuf_uint;
sendbuf_uint = (size_t *) malloc(sizeof(size_t));
recvbuf_uint = (size_t *) malloc(sizeof(size_t));
#ifdef MPI_ENABLE
sendbuf_uint[0] = numberOfUpdatesThisEpoch;
recvbuf_uint[0] = 0;
MPI_Reduce(sendbuf_uint, recvbuf_uint, 1, MPI_UNSIGNED, MPI_SUM, 0, world);
sumUpdates = recvbuf_uint[0];
#endif
#ifndef MPI_ENABLE
sumUpdates = numberOfUpdatesThisEpoch;
#endif
if (rank == 0 && sumUpdates == 0) {
cerr << "\nNo weight updates during this epoch.. stopping." << endl;
stop = true;
#ifdef MPI_ENABLE
mpi::broadcast(world, stop, 0);
#endif
}
if (!stop) {
// Test if weights have converged
if (weightConvergence) {
bool reached = true;
if (rank == 0 && (epoch >= 2)) {
ScoreComponentCollection firstDiff, secondDiff;
if (dumpMixedWeights) {
firstDiff = mixedWeights;
firstDiff.MinusEquals(mixedWeightsPrevious);
secondDiff = mixedWeights;
secondDiff.MinusEquals(mixedWeightsBeforePrevious);
} else {
firstDiff = mixedAverageWeights;
firstDiff.MinusEquals(mixedAverageWeightsPrevious);
secondDiff = mixedAverageWeights;
secondDiff.MinusEquals(mixedAverageWeightsBeforePrevious);
}
VERBOSE(1, "Average weight changes since previous epoch: " << firstDiff << " (max: " << firstDiff.GetLInfNorm() << ")" << endl);
VERBOSE(1, "Average weight changes since before previous epoch: " << secondDiff << " (max: " << secondDiff.GetLInfNorm() << ")" << endl << endl);
// check whether stopping criterion has been reached
// (both difference vectors must have all weight changes smaller than min_weight_change)
if (firstDiff.GetLInfNorm() >= min_weight_change)
reached = false;
if (secondDiff.GetLInfNorm() >= min_weight_change)
reached = false;
if (reached) {
// stop MIRA
stop = true;
cerr << "\nWeights have converged after epoch " << epoch << ".. stopping MIRA." << endl;
ScoreComponentCollection dummy;
ostringstream endfilename;
endfilename << "stopping";
dummy.Save(endfilename.str());
}
}
mixedWeightsBeforePrevious = mixedWeightsPrevious;
mixedWeightsPrevious = mixedWeights;
mixedAverageWeightsBeforePrevious = mixedAverageWeightsPrevious;
mixedAverageWeightsPrevious = mixedAverageWeights;
#ifdef MPI_ENABLE
mpi::broadcast(world, stop, 0);
#endif
} //end if (weightConvergence)
}
} // end of epoch loop
#ifdef MPI_ENABLE
MPI_Finalize();
#endif
time(&now);
cerr << "Rank " << rank << ", " << ctime(&now);
if (rank == 0) {
ScoreComponentCollection dummy;
ostringstream endfilename;
endfilename << "finished";
dummy.Save(endfilename.str());
}
delete decoder;
exit(0);
}
bool loadSentences(const string& filename, vector<string>& sentences)
{
ifstream in(filename.c_str());
if (!in)
return false;
string line;
while (getline(in, line))
sentences.push_back(line);
return true;
}
bool evaluateModulo(size_t shard_position, size_t mix_or_dump_base, size_t actual_batch_size)
{
if (mix_or_dump_base == 0) return 0;
if (actual_batch_size > 1) {
bool mix_or_dump = false;
size_t numberSubtracts = actual_batch_size;
do {
if (shard_position % mix_or_dump_base == 0) {
mix_or_dump = true;
break;
}
--shard_position;
--numberSubtracts;
} while (numberSubtracts > 0);
return mix_or_dump;
} else {
return ((shard_position % mix_or_dump_base) == 0);
}
}
void printFeatureValues(vector<vector<ScoreComponentCollection> > &featureValues)
{
for (size_t i = 0; i < featureValues.size(); ++i) {
for (size_t j = 0; j < featureValues[i].size(); ++j) {
cerr << featureValues[i][j] << endl;
}
}
cerr << endl;
}
void deleteTranslations(vector<vector<const Word*> > &translations)
{
for (size_t i = 0; i < translations.size(); ++i) {
for (size_t j = 0; j < translations[i].size(); ++j) {
delete translations[i][j];
}
}
}
void decodeHopeOrFear(size_t rank, size_t size, size_t decode, string filename, vector<string> &inputSentences, MosesDecoder* decoder, size_t n, float bleuWeight)
{
if (decode == 1)
cerr << "Rank " << rank << ", decoding dev input set according to hope objective.. " << endl;
else if (decode == 2)
cerr << "Rank " << rank << ", decoding dev input set according to fear objective.. " << endl;
else
cerr << "Rank " << rank << ", decoding dev input set according to normal objective.. " << endl;
// Create shards according to the number of processes used
vector<size_t> order;
for (size_t i = 0; i < inputSentences.size(); ++i)
order.push_back(i);
vector<size_t> shard;
float shardSize = (float) (order.size()) / size;
size_t shardStart = (size_t) (shardSize * rank);
size_t shardEnd = (size_t) (shardSize * (rank + 1));
if (rank == size - 1) {
shardEnd = inputSentences.size();
shardSize = shardEnd - shardStart;
}
VERBOSE(1, "Rank " << rank << ", shard start: " << shardStart << " Shard end: " << shardEnd << endl);
VERBOSE(1, "Rank " << rank << ", shard size: " << shardSize << endl);
shard.resize(shardSize);
copy(order.begin() + shardStart, order.begin() + shardEnd, shard.begin());
// open files for writing
stringstream fname;
fname << filename << ".rank" << rank;
filename = fname.str();
ostringstream filename_nbest;
filename_nbest << filename << "." << n << "best";
ofstream out(filename.c_str());
ofstream nbest_out((filename_nbest.str()).c_str());
if (!out) {
ostringstream msg;
msg << "Unable to open " << fname.str();
throw runtime_error(msg.str());
}
if (!nbest_out) {
ostringstream msg;
msg << "Unable to open " << filename_nbest;
throw runtime_error(msg.str());
}
for (size_t i = 0; i < shard.size(); ++i) {
size_t sid = shard[i];
string& input = inputSentences[sid];
vector<vector<ScoreComponentCollection> > dummyFeatureValues;
vector<vector<float> > dummyBleuScores;
vector<vector<float> > dummyModelScores;
vector<ScoreComponentCollection> newFeatureValues;
vector<float> newScores;
dummyFeatureValues.push_back(newFeatureValues);
dummyBleuScores.push_back(newScores);
dummyModelScores.push_back(newScores);
float factor = 0.0;
if (decode == 1) factor = 1.0;
if (decode == 2) factor = -1.0;
cerr << "Rank " << rank << ", translating sentence " << sid << endl;
bool realBleu = false;
vector< vector<const Word*> > nbestOutput = decoder->getNBest(input, sid, n, factor, bleuWeight, dummyFeatureValues[0],
dummyBleuScores[0], dummyModelScores[0], n, realBleu, true, false, rank, 0, "");
cerr << endl;
decoder->cleanup(StaticData::Instance().IsChart());
for (size_t i = 0; i < nbestOutput.size(); ++i) {
vector<const Word*> output = nbestOutput[i];
stringstream translation;
for (size_t k = 0; k < output.size(); ++k) {
Word* w = const_cast<Word*>(output[k]);
translation << w->GetString(0);
translation << " ";
}
if (i == 0)
out << translation.str() << endl;
nbest_out << sid << " ||| " << translation.str() << " ||| " << dummyFeatureValues[0][i] <<
" ||| " << dummyModelScores[0][i] << " ||| sBleu=" << dummyBleuScores[0][i] << endl;
}
}
out.close();
nbest_out.close();
cerr << "Closing files " << filename << " and " << filename_nbest.str() << endl;
#ifdef MPI_ENABLE
MPI_Finalize();
#endif
time_t now;
time(&now);
cerr << "Rank " << rank << ", " << ctime(&now);
delete decoder;
exit(0);
}
void applyLearningRates(vector<vector<ScoreComponentCollection> > &featureValues, float core_r0, float sparse_r0)
{
for (size_t i=0; i<featureValues.size(); ++i) // each item in batch
for (size_t j=0; j<featureValues[i].size(); ++j) // each item in nbest
featureValues[i][j].MultiplyEquals(core_r0, sparse_r0);
}
void applyPerFeatureLearningRates(vector<vector<ScoreComponentCollection> > &featureValues, ScoreComponentCollection featureLearningRates, float sparse_r0)
{
for (size_t i=0; i<featureValues.size(); ++i) // each item in batch
for (size_t j=0; j<featureValues[i].size(); ++j) // each item in nbest
featureValues[i][j].MultiplyEqualsBackoff(featureLearningRates, sparse_r0);
}
void scaleFeatureScore(const FeatureFunction *sp, float scaling_factor, vector<vector<ScoreComponentCollection> > &featureValues, size_t rank, size_t epoch)
{
string name = sp->GetScoreProducerDescription();
// scale down score
float featureScore;
for (size_t i=0; i<featureValues.size(); ++i) { // each item in batch
for (size_t j=0; j<featureValues[i].size(); ++j) { // each item in nbest
featureScore = featureValues[i][j].GetScoreForProducer(sp);
featureValues[i][j].Assign(sp, featureScore*scaling_factor);
//cerr << "Rank " << rank << ", epoch " << epoch << ", " << name << " score scaled from " << featureScore << " to " << featureScore/scaling_factor << endl;
}
}
}
void scaleFeatureScores(const FeatureFunction *sp, float scaling_factor, vector<vector<ScoreComponentCollection> > &featureValues, size_t rank, size_t epoch)
{
string name = sp->GetScoreProducerDescription();
// scale down score
for (size_t i=0; i<featureValues.size(); ++i) { // each item in batch
for (size_t j=0; j<featureValues[i].size(); ++j) { // each item in nbest
vector<float> featureScores = featureValues[i][j].GetScoresForProducer(sp);
for (size_t k=0; k<featureScores.size(); ++k)
featureScores[k] *= scaling_factor;
featureValues[i][j].Assign(sp, featureScores);
//cerr << "Rank " << rank << ", epoch " << epoch << ", " << name << " score scaled from " << featureScore << " to " << featureScore/scaling_factor << endl;
}
}
}