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https://github.com/moses-smt/mosesdecoder.git
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da712f0eeb
git-svn-id: https://mosesdecoder.svn.sourceforge.net/svnroot/mosesdecoder/branches/mira-mtm5@3751 1f5c12ca-751b-0410-a591-d2e778427230
504 lines
19 KiB
C++
504 lines
19 KiB
C++
/***********************************************************************
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Moses - factored phrase-based language decoder
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Copyright (C) 2010 University of Edinburgh
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This library is free software; you can redistribute it and/or
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modify it under the terms of the GNU Lesser General Public
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License as published by the Free Software Foundation; either
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version 2.1 of the License, or (at your option) any later version.
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This library is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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Lesser General Public License for more details.
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You should have received a copy of the GNU Lesser General Public
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License along with this library; if not, write to the Free Software
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Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
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***********************************************************************/
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#include <algorithm>
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#include <cstdlib>
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#include <ctime>
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#include <string>
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#include <vector>
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#include <boost/program_options.hpp>
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#ifdef MPI_ENABLE
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#include <boost/mpi.hpp>
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namespace mpi = boost::mpi;
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#endif
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#include "FeatureVector.h"
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#include "StaticData.h"
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#include "ChartTrellisPathList.h"
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#include "ChartTrellisPath.h"
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#include "ScoreComponentCollection.h"
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#include "Decoder.h"
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#include "Optimiser.h"
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using namespace Mira;
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using namespace std;
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using namespace Moses;
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namespace po = boost::program_options;
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void OutputNBestList(const MosesChart::TrellisPathList &nBestList, const TranslationSystem* system, long translationId);
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bool loadSentences(const string& filename, vector<string>& sentences) {
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ifstream in(filename.c_str());
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if (!in) return false;
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string line;
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while(getline(in,line)) {
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sentences.push_back(line);
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}
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return true;
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}
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struct RandomIndex {
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ptrdiff_t operator() (ptrdiff_t max) {
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return static_cast<ptrdiff_t>(rand() % max);
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}
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};
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int main(int argc, char** argv) {
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size_t rank = 0; size_t size = 1;
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#ifdef MPI_ENABLE
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mpi::environment env(argc,argv);
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mpi::communicator world;
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rank = world.rank();
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size = world.size();
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#endif
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cerr << "Rank: " << rank << " Size: " << size << endl;
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bool help;
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int verbosity;
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string mosesConfigFile;
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string inputFile;
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vector<string> referenceFiles;
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size_t epochs;
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string learner;
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bool shuffle;
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bool hildreth;
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size_t mixFrequency;
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size_t weightDumpFrequency;
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string weightDumpStem;
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float marginScaleFactor;
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size_t n;
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size_t batchSize;
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bool distinctNbest;
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bool onlyViolatedConstraints;
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bool accumulateWeights;
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bool useScaledReference;
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bool scaleByInputLength;
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bool increaseBP;
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bool regulariseHildrethUpdates;
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float clipping;
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bool fixedClipping;
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po::options_description desc("Allowed options");
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desc.add_options()
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("help",po::value( &help )->zero_tokens()->default_value(false), "Print this help message and exit")
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("config,f",po::value<string>(&mosesConfigFile),"Moses ini file")
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("verbosity,v", po::value<int>(&verbosity)->default_value(0), "Verbosity level")
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("input-file,i",po::value<string>(&inputFile),"Input file containing tokenised source")
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("reference-files,r", po::value<vector<string> >(&referenceFiles), "Reference translation files for training")
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("epochs,e", po::value<size_t>(&epochs)->default_value(1), "Number of epochs")
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("learner,l", po::value<string>(&learner)->default_value("mira"), "Learning algorithm")
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("mix-frequency", po::value<size_t>(&mixFrequency)->default_value(1), "How often per epoch to mix weights, when using mpi")
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("weight-dump-stem", po::value<string>(&weightDumpStem)->default_value("weights"), "Stem of filename to use for dumping weights")
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("weight-dump-frequency", po::value<size_t>(&weightDumpFrequency)->default_value(1), "How often per epoch to dump weights")
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("shuffle", po::value<bool>(&shuffle)->default_value(false), "Shuffle input sentences before processing")
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("hildreth", po::value<bool>(&hildreth)->default_value(true), "Use Hildreth's optimisation algorithm")
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("margin-scale-factor,m", po::value<float>(&marginScaleFactor)->default_value(1.0), "Margin scale factor, regularises the update by scaling the enforced margin")
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("nbest,n", po::value<size_t>(&n)->default_value(10), "Number of translations in nbest list")
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("batch-size,b", po::value<size_t>(&batchSize)->default_value(1), "Size of batch that is send to optimiser for weight adjustments")
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("distinct-nbest", po::value<bool>(&distinctNbest)->default_value(false), "Use nbest list with distinct translations in inference step")
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("only-violated-constraints", po::value<bool>(&onlyViolatedConstraints)->default_value(false), "Add only violated constraints to the optimisation problem")
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("accumulate-weights", po::value<bool>(&accumulateWeights)->default_value(false), "Accumulate and average weights over all epochs")
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("use-scaled-reference", po::value<bool>(&useScaledReference)->default_value(true), "Use scaled reference length for comparing target and reference length of phrases")
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("scale-by-input-length", po::value<bool>(&scaleByInputLength)->default_value(true), "Scale the BLEU score by a history of the input lengths")
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("increase-BP", po::value<bool>(&increaseBP)->default_value(false), "Increase penalty for short translations")
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("regularise-hildreth-updates", po::value<bool>(®ulariseHildrethUpdates)->default_value(false), "Regularise Hildreth updates with the value set for clipping")
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("clipping", po::value<float>(&clipping)->default_value(0.01f), "Set a threshold to regularise updates")
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("fixed-clipping", po::value<bool>(&fixedClipping)->default_value(false), "Use a fixed clipping threshold with SMO (instead of adaptive)");
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po::options_description cmdline_options;
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cmdline_options.add(desc);
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po::variables_map vm;
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po::store(po::command_line_parser(argc,argv).
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options(cmdline_options).run(), vm);
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po::notify(vm);
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if (help) {
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std::cout << "Usage: " + string(argv[0]) + " -f mosesini-file -i input-file -r reference-file(s) [options]" << std::endl;
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std::cout << desc << std::endl;
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return 0;
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}
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if (mosesConfigFile.empty()) {
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cerr << "Error: No moses ini file specified" << endl;
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return 1;
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}
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if (inputFile.empty()) {
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cerr << "Error: No input file specified" << endl;
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return 1;
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}
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if (!referenceFiles.size()) {
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cerr << "Error: No reference files specified" << endl;
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return 1;
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}
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// load input and references
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vector<string> inputSentences;
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if (!loadSentences(inputFile, inputSentences)) {
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cerr << "Error: Failed to load input sentences from " << inputFile << endl;
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return 1;
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}
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vector< vector<string> > referenceSentences(referenceFiles.size());
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for (size_t i = 0; i < referenceFiles.size(); ++i) {
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if (!loadSentences(referenceFiles[i], referenceSentences[i])) {
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cerr << "Error: Failed to load reference sentences from " << referenceFiles[i] << endl;
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return 1;
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}
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if (referenceSentences[i].size() != inputSentences.size()) {
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cerr << "Error: Input file length (" << inputSentences.size() <<
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") != (" << referenceSentences[i].size() << ") length of reference file " << i <<
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endl;
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return 1;
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}
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}
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// initialise Moses
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initMoses(mosesConfigFile, verbosity);//, argc, argv);
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MosesDecoder* decoder = new MosesDecoder(referenceSentences, useScaledReference, scaleByInputLength, increaseBP);
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ScoreComponentCollection startWeights = decoder->getWeights();
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startWeights.L1Normalise();
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decoder->setWeights(startWeights);
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// Optionally shuffle the sentences
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vector<size_t> order;
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if (rank == 0) {
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for (size_t i = 0; i < inputSentences.size(); ++i) {
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order.push_back(i);
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}
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if (shuffle) {
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cerr << "Shuffling input sentences.." << endl;
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RandomIndex rindex;
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random_shuffle(order.begin(), order.end(), rindex);
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}
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}
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#ifdef MPI_ENABLE
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mpi::broadcast(world, order, 0);
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#endif
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// Create the shards according to the number of processes used
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vector<size_t> shard;
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float shardSize = (float)(order.size()) / size;
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VERBOSE(1, "Shard size: " << shardSize << endl);
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size_t shardStart = (size_t)(shardSize * rank);
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size_t shardEnd = (size_t)(shardSize * (rank+1));
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if (rank == size-1) shardEnd = order.size();
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VERBOSE(1, "Rank: " << rank << " Shard start: " << shardStart << " Shard end: " << shardEnd << endl);
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shard.resize(shardSize);
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copy(order.begin() + shardStart, order.begin() + shardEnd, shard.begin());
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Optimiser* optimiser = NULL;
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cerr << "Nbest list size: " << n << endl;
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cerr << "Distinct translations in nbest list? " << distinctNbest << endl;
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if (learner == "mira") {
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cerr << "Optimising using Mira" << endl;
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optimiser = new MiraOptimiser(n, hildreth, marginScaleFactor, onlyViolatedConstraints, clipping, fixedClipping, regulariseHildrethUpdates);
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if (hildreth) {
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cerr << "Using Hildreth's optimisation algorithm.." << endl;
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}
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else {
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cerr << "Using some sort of SMO.. " << endl;
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}
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cerr << "Margin scale factor: " << marginScaleFactor << endl;
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cerr << "Add only violated constraints? " << onlyViolatedConstraints << endl;
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} else if (learner == "perceptron") {
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cerr << "Optimising using Perceptron" << endl;
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optimiser = new Perceptron();
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} else {
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cerr << "Error: Unknown optimiser: " << learner << endl;
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}
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//Main loop:
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ScoreComponentCollection cumulativeWeights; // collect weights per epoch to produce an average
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size_t iterations = 0;
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size_t iterationsThisEpoch = 0;
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time_t now = time(0); // get current time
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struct tm* tm = localtime(&now); // get struct filled out
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cerr << "Start date/time: " << tm->tm_mon+1 << "/" << tm->tm_mday << "/" << tm->tm_year + 1900
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<< ", " << tm->tm_hour << ":" << tm->tm_min << ":" << tm->tm_sec << endl;
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// the result of accumulating and averaging weights over one epoch and possibly several processes
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ScoreComponentCollection averageTotalWeights;
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// TODO: scaling of feature values for probabilistic features
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for (size_t epoch = 0; epoch < epochs; ++epoch) {
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cerr << "\nEpoch " << epoch << endl;
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// Sum up weights over one epoch, final average uses weights from last epoch
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iterationsThisEpoch = 0;
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if (!accumulateWeights) {
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cumulativeWeights.ZeroAll();
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}
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// number of weight dumps this epoch
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size_t weightEpochDump = 0;
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size_t shardPosition = 0;
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vector<size_t>::const_iterator sid = shard.begin();
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while (sid != shard.end()) {
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// feature values for hypotheses i,j (matrix: batchSize x 3*n x featureValues)
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vector<vector<ScoreComponentCollection > > featureValues;
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vector<vector<float> > bleuScores;
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// BATCHING: produce nbest lists for all input sentences in batch
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vector<size_t> oraclePositions;
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vector<float> oracleBleuScores;
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vector< vector< const Word*> > oracles;
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vector<ScoreComponentCollection> oracleFeatureValues;
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vector<size_t> inputLengths;
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vector<size_t> ref_ids;
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size_t actualBatchSize = 0;
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for (size_t batchPosition = 0; batchPosition < batchSize && sid != shard.end(); ++batchPosition) {
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const string& input = inputSentences[*sid];
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const vector<string>& refs = referenceSentences[*sid];
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cerr << "\nBatch position " << batchPosition << endl;
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cerr << "Input sentence " << *sid << ": \"" << input << "\"" << endl;
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vector<ScoreComponentCollection> newFeatureValues;
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vector<float> newBleuScores;
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featureValues.push_back(newFeatureValues);
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bleuScores.push_back(newBleuScores);
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// MODEL
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cerr << "Run decoder to get nbest wrt model score" << endl;
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vector<const Word*> bestModel = decoder->getNBest(input,
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*sid,
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n,
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0.0,
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1.0,
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featureValues[batchPosition],
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bleuScores[batchPosition],
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true,
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distinctNbest);
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inputLengths.push_back(decoder->getCurrentInputLength());
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ref_ids.push_back(*sid);
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decoder->cleanup();
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for (size_t i = 0; i < bestModel.size(); ++i) {
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cerr << *(bestModel[i]) << " ";
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}
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cerr << endl;
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cerr << "model length: " << bestModel.size() << " Bleu: " << bleuScores[batchPosition][0] << endl;
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// HOPE
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cerr << "Run decoder to get nbest hope translations" << endl;
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size_t oraclePos = featureValues[batchPosition].size();
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oraclePositions.push_back(oraclePos);
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vector<const Word*> oracle = decoder->getNBest(input,
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*sid,
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n,
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1.0,
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1.0,
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featureValues[batchPosition],
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bleuScores[batchPosition],
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true,
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distinctNbest);
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decoder->cleanup();
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oracles.push_back(oracle);
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for (size_t i = 0; i < oracle.size(); ++i) {
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//oracles[batchPosition].push_back(oracle[i]);
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cerr << *(oracle[i]) << " ";
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}
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cerr << endl;
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cerr << "oracle length: " << oracle.size() << " Bleu: " << bleuScores[batchPosition][oraclePos] << endl;
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oracleFeatureValues.push_back(featureValues[batchPosition][oraclePos]);
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float oracleBleuScore = bleuScores[batchPosition][oraclePos];
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oracleBleuScores.push_back(oracleBleuScore);
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// FEAR
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cerr << "Run decoder to get nbest fear translations" << endl;
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size_t fearPos = featureValues[batchPosition].size();
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vector<const Word*> fear = decoder->getNBest(input,
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*sid,
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n,
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-1.0,
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1.0,
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featureValues[batchPosition],
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bleuScores[batchPosition],
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true,
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distinctNbest);
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decoder->cleanup();
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for (size_t i = 0; i < fear.size(); ++i) {
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cerr << *(fear[i]) << " ";
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}
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cerr << endl;
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cerr << "fear length: " << fear.size() << " Bleu: " << bleuScores[batchPosition][fearPos] << endl;
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for (size_t i = 0; i < bestModel.size(); ++i) {
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delete bestModel[i];
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}
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for (size_t i = 0; i < fear.size(); ++i) {
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delete fear[i];
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}
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// next input sentence
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++sid;
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++actualBatchSize;
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++shardPosition;
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}
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// Set loss for each sentence as BLEU(oracle) - BLEU(hypothesis)
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vector< vector<float> > losses(actualBatchSize);
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for (size_t batchPosition = 0; batchPosition < actualBatchSize; ++batchPosition) {
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for (size_t j = 0; j < bleuScores[batchPosition].size(); ++j) {
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losses[batchPosition].push_back(oracleBleuScores[batchPosition] - bleuScores[batchPosition][j]);
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}
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}
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// get weight vector and set weight for bleu feature to 0
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ScoreComponentCollection mosesWeights = decoder->getWeights();
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const vector<const ScoreProducer*> featureFunctions = StaticData::Instance().GetTranslationSystem (TranslationSystem::DEFAULT).GetFeatureFunctions();
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mosesWeights.Assign(featureFunctions.back(), 0);
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if (!hildreth && typeid(*optimiser) == typeid(MiraOptimiser)) {
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((MiraOptimiser*)optimiser)->setOracleIndices(oraclePositions);
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}
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// run optimiser on batch
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cerr << "\nRun optimiser.." << endl;
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ScoreComponentCollection oldWeights(mosesWeights);
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int constraintChange = optimiser->updateWeights(mosesWeights, featureValues, losses, oracleFeatureValues);
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// update moses weights
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mosesWeights.L1Normalise();
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decoder->setWeights(mosesWeights);
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// update history (for approximate document bleu)
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decoder->updateHistory(oracles, inputLengths, ref_ids);
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// clean up oracle translations after updating history
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for (size_t i = 0; i < oracles.size(); ++i) {
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for (size_t j = 0; j < oracles[i].size(); ++j) {
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delete oracles[i][j];
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}
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}
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cumulativeWeights.PlusEquals(mosesWeights);
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// sanity check: compare margin created by old weights against new weights
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float lossMinusMargin_old = 0;
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float lossMinusMargin_new = 0;
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for (size_t batchPosition = 0; batchPosition < actualBatchSize; ++batchPosition) {
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for (size_t j = 0; j < featureValues[batchPosition].size(); ++j) {
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ScoreComponentCollection featureDiff(oracleFeatureValues[batchPosition]);
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featureDiff.MinusEquals(featureValues[batchPosition][j]);
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// old weights
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float margin = featureDiff.InnerProduct(oldWeights);
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lossMinusMargin_old += (losses[batchPosition][j] - margin);
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// new weights
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margin = featureDiff.InnerProduct(mosesWeights);
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lossMinusMargin_new += (losses[batchPosition][j] - margin);
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}
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}
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cerr << "\nConstraint change: " << constraintChange << endl;
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cerr << "Summed (loss - margin) with old weights: " << lossMinusMargin_old << endl;
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cerr << "Summed (loss - margin) with new weights: " << lossMinusMargin_new << endl;
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if (lossMinusMargin_new > lossMinusMargin_old) {
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cerr << "Worsening: " << lossMinusMargin_new - lossMinusMargin_old << endl;
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if (constraintChange < 0) {
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cerr << "Something is going wrong here.." << endl;
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}
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}
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++iterations;
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++iterationsThisEpoch;
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// mix weights?
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#ifdef MPI_ENABLE
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if (shardPosition % (shard.size() / mixFrequency) == 0) {
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ScoreComponentCollection averageWeights;
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VERBOSE(1, "\nRank: " << rank << " \nBefore mixing: " << mosesWeights << endl);
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// collect all weights in averageWeights and divide by number of processes
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mpi::reduce(world, mosesWeights, averageWeights, SCCPlus(), 0);
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if (rank == 0) {
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averageWeights.DivideEquals(size);
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VERBOSE(1, "After mixing: " << averageWeights << endl);
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// normalise weights after averaging
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averageWeights.L1Normalise();
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}
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// broadcast average weights from process 0
|
|
mpi::broadcast(world, averageWeights, 0);
|
|
decoder->setWeights(averageWeights);
|
|
}
|
|
#endif
|
|
|
|
// dump weights?
|
|
if (shardPosition % (shard.size() / weightDumpFrequency) == 0) {
|
|
// compute average weights per process over iterations
|
|
ScoreComponentCollection totalWeights(cumulativeWeights);
|
|
if (accumulateWeights)
|
|
totalWeights.DivideEquals(iterations);
|
|
else
|
|
totalWeights.DivideEquals(iterationsThisEpoch);
|
|
|
|
// average across processes
|
|
#ifdef MPI_ENABLE
|
|
mpi::reduce(world, totalWeights, averageTotalWeights, SCCPlus(), 0);
|
|
if (rank == 0) {
|
|
// average and normalise weights
|
|
averageTotalWeights.DivideEquals(size);
|
|
averageTotalWeights.L1Normalise();
|
|
}
|
|
#endif
|
|
#ifndef MPI_ENABLE
|
|
// or use weights from single process
|
|
averageTotalWeights = totalWeights;
|
|
#endif
|
|
if (!weightDumpStem.empty()) {
|
|
ostringstream filename;
|
|
filename << weightDumpStem << "_" << epoch;
|
|
if (weightDumpFrequency > 1) {
|
|
filename << "_" << weightEpochDump;
|
|
}
|
|
|
|
VERBOSE(1, "Dumping weights for epoch " << epoch << " to " << filename.str() << endl);
|
|
averageTotalWeights.Save(filename.str());
|
|
++weightEpochDump;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
/*#ifdef MPI_ENABLE
|
|
mpi::finalize();
|
|
#endif*/
|
|
|
|
cerr << "Average total weights: " << averageTotalWeights << endl;
|
|
|
|
now = time(0); // get current time
|
|
tm = localtime(&now); // get struct filled out
|
|
cerr << "End date/time: " << tm->tm_mon+1 << "/" << tm->tm_mday << "/" << tm->tm_year + 1900
|
|
<< ", " << tm->tm_hour << ":" << tm->tm_min << ":" << tm->tm_sec << endl;
|
|
|
|
delete decoder;
|
|
exit(0);
|
|
}
|
|
|