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mosesdecoder/moses/StaticData.cpp
Hieu Hoang 341969e61d Merge ../mosesdecoder into hieu
2014-06-10 18:04:55 +01:00

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// $Id$
// vim:tabstop=2
/***********************************************************************
Moses - factored phrase-based language decoder
Copyright (C) 2006 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 <string>
#include "TypeDef.h"
#include "moses/FF/WordPenaltyProducer.h"
#include "moses/FF/UnknownWordPenaltyProducer.h"
#include "moses/FF/InputFeature.h"
#include "DecodeStepTranslation.h"
#include "DecodeStepGeneration.h"
#include "GenerationDictionary.h"
#include "StaticData.h"
#include "Util.h"
#include "FactorCollection.h"
#include "Timer.h"
#include "UserMessage.h"
#include "TranslationOption.h"
#include "DecodeGraph.h"
#include "InputFileStream.h"
#include "ScoreComponentCollection.h"
#include "DecodeGraph.h"
#include "TranslationModel/PhraseDictionary.h"
#include "TranslationModel/PhraseDictionaryTreeAdaptor.h"
#ifdef WITH_THREADS
#include <boost/thread.hpp>
#endif
using namespace std;
namespace Moses
{
bool g_mosesDebug = false;
StaticData StaticData::s_instance;
StaticData::StaticData()
:m_sourceStartPosMattersForRecombination(false)
,m_inputType(SentenceInput)
,m_detailedTranslationReportingFilePath()
,m_detailedTreeFragmentsTranslationReportingFilePath()
,m_onlyDistinctNBest(false)
,m_needAlignmentInfo(false)
,m_factorDelimiter("|") // default delimiter between factors
,m_lmEnableOOVFeature(false)
,m_isAlwaysCreateDirectTranslationOption(false)
,m_currentWeightSetting("default")
,m_treeStructure(NULL)
{
m_xmlBrackets.first="<";
m_xmlBrackets.second=">";
// memory pools
Phrase::InitializeMemPool();
}
StaticData::~StaticData()
{
RemoveAllInColl(m_decodeGraphs);
/*
const std::vector<FeatureFunction*> &producers = FeatureFunction::GetFeatureFunctions();
for(size_t i=0;i<producers.size();++i) {
FeatureFunction *ff = producers[i];
delete ff;
}
*/
// memory pools
Phrase::FinalizeMemPool();
}
bool StaticData::LoadDataStatic(Parameter *parameter, const std::string &execPath)
{
s_instance.SetExecPath(execPath);
return s_instance.LoadData(parameter);
}
bool StaticData::LoadData(Parameter *parameter)
{
ResetUserTime();
m_parameter = parameter;
// verbose level
m_verboseLevel = 1;
if (m_parameter->GetParam("verbose").size() == 1) {
m_verboseLevel = Scan<size_t>( m_parameter->GetParam("verbose")[0]);
}
// to cube or not to cube
m_searchAlgorithm = (m_parameter->GetParam("search-algorithm").size() > 0) ?
(SearchAlgorithm) Scan<size_t>(m_parameter->GetParam("search-algorithm")[0]) : Normal;
if (IsChart())
LoadChartDecodingParameters();
// input type has to be specified BEFORE loading the phrase tables!
if(m_parameter->GetParam("inputtype").size())
m_inputType= (InputTypeEnum) Scan<int>(m_parameter->GetParam("inputtype")[0]);
std::string s_it = "text input";
if (m_inputType == 1) {
s_it = "confusion net";
}
if (m_inputType == 2) {
s_it = "word lattice";
}
VERBOSE(2,"input type is: "<<s_it<<"\n");
if(m_parameter->GetParam("recover-input-path").size()) {
m_recoverPath = Scan<bool>(m_parameter->GetParam("recover-input-path")[0]);
if (m_recoverPath && m_inputType == SentenceInput) {
TRACE_ERR("--recover-input-path should only be used with confusion net or word lattice input!\n");
m_recoverPath = false;
}
}
// factor delimiter
if (m_parameter->GetParam("factor-delimiter").size() > 0) {
m_factorDelimiter = m_parameter->GetParam("factor-delimiter")[0];
if (m_factorDelimiter == "none")
m_factorDelimiter = "";
}
SetBooleanParameter( &m_continuePartialTranslation, "continue-partial-translation", false );
SetBooleanParameter( &m_outputHypoScore, "output-hypo-score", false );
//word-to-word alignment
// alignments
SetBooleanParameter( &m_PrintAlignmentInfo, "print-alignment-info", false );
if (m_PrintAlignmentInfo) {
m_needAlignmentInfo = true;
}
if(m_parameter->GetParam("sort-word-alignment").size()) {
m_wordAlignmentSort = (WordAlignmentSort) Scan<size_t>(m_parameter->GetParam("sort-word-alignment")[0]);
}
SetBooleanParameter( &m_PrintAlignmentInfoNbest, "print-alignment-info-in-n-best", false );
if (m_PrintAlignmentInfoNbest) {
m_needAlignmentInfo = true;
}
if (m_parameter->GetParam("alignment-output-file").size() > 0) {
m_alignmentOutputFile = Scan<std::string>(m_parameter->GetParam("alignment-output-file")[0]);
m_needAlignmentInfo = true;
}
// n-best
if (m_parameter->GetParam("n-best-list").size() >= 2) {
m_nBestFilePath = m_parameter->GetParam("n-best-list")[0];
m_nBestSize = Scan<size_t>( m_parameter->GetParam("n-best-list")[1] );
m_onlyDistinctNBest=(m_parameter->GetParam("n-best-list").size()>2
&& m_parameter->GetParam("n-best-list")[2]=="distinct");
} else if (m_parameter->GetParam("n-best-list").size() == 1) {
UserMessage::Add(string("wrong format for switch -n-best-list file size"));
return false;
} else {
m_nBestSize = 0;
}
if (m_parameter->GetParam("n-best-factor").size() > 0) {
m_nBestFactor = Scan<size_t>( m_parameter->GetParam("n-best-factor")[0]);
} else {
m_nBestFactor = 20;
}
//lattice samples
if (m_parameter->GetParam("lattice-samples").size() ==2 ) {
m_latticeSamplesFilePath = m_parameter->GetParam("lattice-samples")[0];
m_latticeSamplesSize = Scan<size_t>(m_parameter->GetParam("lattice-samples")[1]);
} else if (m_parameter->GetParam("lattice-samples").size() != 0 ) {
UserMessage::Add(string("wrong format for switch -lattice-samples file size"));
return false;
} else {
m_latticeSamplesSize = 0;
}
// word graph
if (m_parameter->GetParam("output-word-graph").size() == 2)
m_outputWordGraph = true;
else
m_outputWordGraph = false;
// search graph
if (m_parameter->GetParam("output-search-graph").size() > 0) {
if (m_parameter->GetParam("output-search-graph").size() != 1) {
UserMessage::Add(string("ERROR: wrong format for switch -output-search-graph file"));
return false;
}
m_outputSearchGraph = true;
}
// ... in extended format
else if (m_parameter->GetParam("output-search-graph-extended").size() > 0) {
if (m_parameter->GetParam("output-search-graph-extended").size() != 1) {
UserMessage::Add(string("ERROR: wrong format for switch -output-search-graph-extended file"));
return false;
}
m_outputSearchGraph = true;
m_outputSearchGraphExtended = true;
} else {
m_outputSearchGraph = false;
}
if (m_parameter->GetParam("output-search-graph-slf").size() > 0) {
m_outputSearchGraphSLF = true;
} else {
m_outputSearchGraphSLF = false;
}
if (m_parameter->GetParam("output-search-graph-hypergraph").size() > 0) {
m_outputSearchGraphHypergraph = true;
} else {
m_outputSearchGraphHypergraph = false;
}
#ifdef HAVE_PROTOBUF
if (m_parameter->GetParam("output-search-graph-pb").size() > 0) {
if (m_parameter->GetParam("output-search-graph-pb").size() != 1) {
UserMessage::Add(string("ERROR: wrong format for switch -output-search-graph-pb path"));
return false;
}
m_outputSearchGraphPB = true;
} else
m_outputSearchGraphPB = false;
#endif
SetBooleanParameter( &m_unprunedSearchGraph, "unpruned-search-graph", false );
SetBooleanParameter( &m_includeLHSInSearchGraph, "include-lhs-in-search-graph", false );
if (m_parameter->isParamSpecified("output-unknowns")) {
if (m_parameter->GetParam("output-unknowns").size() == 1) {
m_outputUnknownsFile =Scan<string>(m_parameter->GetParam("output-unknowns")[0]);
} else {
UserMessage::Add(string("need to specify exactly one file name for unknowns"));
return false;
}
}
// include feature names in the n-best list
SetBooleanParameter( &m_labeledNBestList, "labeled-n-best-list", true );
// include word alignment in the n-best list
SetBooleanParameter( &m_nBestIncludesSegmentation, "include-segmentation-in-n-best", false );
// printing source phrase spans
SetBooleanParameter( &m_reportSegmentation, "report-segmentation", false );
SetBooleanParameter( &m_reportSegmentationEnriched, "report-segmentation-enriched", false );
// print all factors of output translations
SetBooleanParameter( &m_reportAllFactors, "report-all-factors", false );
// print all factors of output translations
SetBooleanParameter( &m_reportAllFactorsNBest, "report-all-factors-in-n-best", false );
//input factors
const vector<string> &inputFactorVector = m_parameter->GetParam("input-factors");
for(size_t i=0; i<inputFactorVector.size(); i++) {
m_inputFactorOrder.push_back(Scan<FactorType>(inputFactorVector[i]));
}
if(m_inputFactorOrder.empty()) {
UserMessage::Add(string("no input factor specified in config file"));
return false;
}
//output factors
const vector<string> &outputFactorVector = m_parameter->GetParam("output-factors");
for(size_t i=0; i<outputFactorVector.size(); i++) {
m_outputFactorOrder.push_back(Scan<FactorType>(outputFactorVector[i]));
}
if(m_outputFactorOrder.empty()) {
// default. output factor 0
m_outputFactorOrder.push_back(0);
}
//source word deletion
SetBooleanParameter( &m_wordDeletionEnabled, "phrase-drop-allowed", false );
//Disable discarding
SetBooleanParameter(&m_disableDiscarding, "disable-discarding", false);
//Print All Derivations
SetBooleanParameter( &m_printAllDerivations , "print-all-derivations", false );
// additional output
if (m_parameter->isParamSpecified("translation-details")) {
const vector<string> &args = m_parameter->GetParam("translation-details");
if (args.size() == 1) {
m_detailedTranslationReportingFilePath = args[0];
} else {
UserMessage::Add(string("the translation-details option requires exactly one filename argument"));
return false;
}
}
if (m_parameter->isParamSpecified("tree-translation-details")) {
const vector<string> &args = m_parameter->GetParam("tree-translation-details");
if (args.size() == 1) {
m_detailedTreeFragmentsTranslationReportingFilePath = args[0];
} else {
UserMessage::Add(string("the tree-translation-details option requires exactly one filename argument"));
return false;
}
}
//DIMw
if (m_parameter->isParamSpecified("translation-all-details")) {
const vector<string> &args = m_parameter->GetParam("translation-all-details");
if (args.size() == 1) {
m_detailedAllTranslationReportingFilePath = args[0];
} else {
UserMessage::Add(string("the translation-all-details option requires exactly one filename argument"));
return false;
}
}
// reordering constraints
m_maxDistortion = (m_parameter->GetParam("distortion-limit").size() > 0) ?
Scan<int>(m_parameter->GetParam("distortion-limit")[0])
: -1;
SetBooleanParameter( &m_reorderingConstraint, "monotone-at-punctuation", false );
// settings for pruning
m_maxHypoStackSize = (m_parameter->GetParam("stack").size() > 0)
? Scan<size_t>(m_parameter->GetParam("stack")[0]) : DEFAULT_MAX_HYPOSTACK_SIZE;
m_minHypoStackDiversity = 0;
if (m_parameter->GetParam("stack-diversity").size() > 0) {
if (m_maxDistortion > 15) {
UserMessage::Add("stack diversity > 0 is not allowed for distortion limits larger than 15");
return false;
}
if (m_inputType == WordLatticeInput) {
UserMessage::Add("stack diversity > 0 is not allowed for lattice input");
return false;
}
m_minHypoStackDiversity = Scan<size_t>(m_parameter->GetParam("stack-diversity")[0]);
}
m_beamWidth = (m_parameter->GetParam("beam-threshold").size() > 0) ?
TransformScore(Scan<float>(m_parameter->GetParam("beam-threshold")[0]))
: TransformScore(DEFAULT_BEAM_WIDTH);
m_earlyDiscardingThreshold = (m_parameter->GetParam("early-discarding-threshold").size() > 0) ?
TransformScore(Scan<float>(m_parameter->GetParam("early-discarding-threshold")[0]))
: TransformScore(DEFAULT_EARLY_DISCARDING_THRESHOLD);
m_translationOptionThreshold = (m_parameter->GetParam("translation-option-threshold").size() > 0) ?
TransformScore(Scan<float>(m_parameter->GetParam("translation-option-threshold")[0]))
: TransformScore(DEFAULT_TRANSLATION_OPTION_THRESHOLD);
m_maxNoTransOptPerCoverage = (m_parameter->GetParam("max-trans-opt-per-coverage").size() > 0)
? Scan<size_t>(m_parameter->GetParam("max-trans-opt-per-coverage")[0]) : DEFAULT_MAX_TRANS_OPT_SIZE;
m_maxNoPartTransOpt = (m_parameter->GetParam("max-partial-trans-opt").size() > 0)
? Scan<size_t>(m_parameter->GetParam("max-partial-trans-opt")[0]) : DEFAULT_MAX_PART_TRANS_OPT_SIZE;
m_maxPhraseLength = (m_parameter->GetParam("max-phrase-length").size() > 0)
? Scan<size_t>(m_parameter->GetParam("max-phrase-length")[0]) : DEFAULT_MAX_PHRASE_LENGTH;
m_cubePruningPopLimit = (m_parameter->GetParam("cube-pruning-pop-limit").size() > 0)
? Scan<size_t>(m_parameter->GetParam("cube-pruning-pop-limit")[0]) : DEFAULT_CUBE_PRUNING_POP_LIMIT;
m_cubePruningDiversity = (m_parameter->GetParam("cube-pruning-diversity").size() > 0)
? Scan<size_t>(m_parameter->GetParam("cube-pruning-diversity")[0]) : DEFAULT_CUBE_PRUNING_DIVERSITY;
SetBooleanParameter(&m_cubePruningLazyScoring, "cube-pruning-lazy-scoring", false);
// early distortion cost
SetBooleanParameter( &m_useEarlyDistortionCost, "early-distortion-cost", false );
// unknown word processing
SetBooleanParameter( &m_dropUnknown, "drop-unknown", false );
SetBooleanParameter( &m_markUnknown, "mark-unknown", false );
SetBooleanParameter( &m_lmEnableOOVFeature, "lmodel-oov-feature", false);
// minimum Bayes risk decoding
SetBooleanParameter( &m_mbr, "minimum-bayes-risk", false );
m_mbrSize = (m_parameter->GetParam("mbr-size").size() > 0) ?
Scan<size_t>(m_parameter->GetParam("mbr-size")[0]) : 200;
m_mbrScale = (m_parameter->GetParam("mbr-scale").size() > 0) ?
Scan<float>(m_parameter->GetParam("mbr-scale")[0]) : 1.0f;
//lattice mbr
SetBooleanParameter( &m_useLatticeMBR, "lminimum-bayes-risk", false );
if (m_useLatticeMBR && m_mbr) {
cerr << "Errror: Cannot use both n-best mbr and lattice mbr together" << endl;
exit(1);
}
//mira training
SetBooleanParameter( &m_mira, "mira", false );
// lattice MBR
if (m_useLatticeMBR) m_mbr = true;
m_lmbrPruning = (m_parameter->GetParam("lmbr-pruning-factor").size() > 0) ?
Scan<size_t>(m_parameter->GetParam("lmbr-pruning-factor")[0]) : 30;
m_lmbrThetas = Scan<float>(m_parameter->GetParam("lmbr-thetas"));
SetBooleanParameter( &m_useLatticeHypSetForLatticeMBR, "lattice-hypo-set", false );
m_lmbrPrecision = (m_parameter->GetParam("lmbr-p").size() > 0) ?
Scan<float>(m_parameter->GetParam("lmbr-p")[0]) : 0.8f;
m_lmbrPRatio = (m_parameter->GetParam("lmbr-r").size() > 0) ?
Scan<float>(m_parameter->GetParam("lmbr-r")[0]) : 0.6f;
m_lmbrMapWeight = (m_parameter->GetParam("lmbr-map-weight").size() >0) ?
Scan<float>(m_parameter->GetParam("lmbr-map-weight")[0]) : 0.0f;
//consensus decoding
SetBooleanParameter( &m_useConsensusDecoding, "consensus-decoding", false );
if (m_useConsensusDecoding && m_mbr) {
cerr<< "Error: Cannot use consensus decoding together with mbr" << endl;
exit(1);
}
if (m_useConsensusDecoding) m_mbr=true;
SetBooleanParameter( &m_defaultNonTermOnlyForEmptyRange, "default-non-term-for-empty-range-only", false );
// Compact phrase table and reordering model
SetBooleanParameter( &m_minphrMemory, "minphr-memory", false );
SetBooleanParameter( &m_minlexrMemory, "minlexr-memory", false );
m_timeout_threshold = (m_parameter->GetParam("time-out").size() > 0) ?
Scan<size_t>(m_parameter->GetParam("time-out")[0]) : -1;
m_timeout = (GetTimeoutThreshold() == (size_t)-1) ? false : true;
m_lmcache_cleanup_threshold = (m_parameter->GetParam("clean-lm-cache").size() > 0) ?
Scan<size_t>(m_parameter->GetParam("clean-lm-cache")[0]) : 1;
m_threadCount = 1;
const std::vector<std::string> &threadInfo = m_parameter->GetParam("threads");
if (!threadInfo.empty()) {
if (threadInfo[0] == "all") {
#ifdef WITH_THREADS
m_threadCount = boost::thread::hardware_concurrency();
if (!m_threadCount) {
UserMessage::Add("-threads all specified but Boost doesn't know how many cores there are");
return false;
}
#else
UserMessage::Add("-threads all specified but moses not built with thread support");
return false;
#endif
} else {
m_threadCount = Scan<int>(threadInfo[0]);
if (m_threadCount < 1) {
UserMessage::Add("Specify at least one thread.");
return false;
}
#ifndef WITH_THREADS
if (m_threadCount > 1) {
UserMessage::Add(std::string("Error: Thread count of ") + threadInfo[0] + " but moses not built with thread support");
return false;
}
#endif
}
}
m_startTranslationId = (m_parameter->GetParam("start-translation-id").size() > 0) ?
Scan<long>(m_parameter->GetParam("start-translation-id")[0]) : 0;
// use of xml in input
if (m_parameter->GetParam("xml-input").size() == 0) m_xmlInputType = XmlPassThrough;
else if (m_parameter->GetParam("xml-input")[0]=="exclusive") m_xmlInputType = XmlExclusive;
else if (m_parameter->GetParam("xml-input")[0]=="inclusive") m_xmlInputType = XmlInclusive;
else if (m_parameter->GetParam("xml-input")[0]=="constraint") m_xmlInputType = XmlConstraint;
else if (m_parameter->GetParam("xml-input")[0]=="ignore") m_xmlInputType = XmlIgnore;
else if (m_parameter->GetParam("xml-input")[0]=="pass-through") m_xmlInputType = XmlPassThrough;
else {
UserMessage::Add("invalid xml-input value, must be pass-through, exclusive, inclusive, constraint, or ignore");
return false;
}
// specify XML tags opening and closing brackets for XML option
if (m_parameter->GetParam("xml-brackets").size() > 0) {
std::vector<std::string> brackets = Tokenize(m_parameter->GetParam("xml-brackets")[0]);
if(brackets.size()!=2) {
cerr << "invalid xml-brackets value, must specify exactly 2 blank-delimited strings for XML tags opening and closing brackets" << endl;
exit(1);
}
m_xmlBrackets.first= brackets[0];
m_xmlBrackets.second=brackets[1];
VERBOSE(1,"XML tags opening and closing brackets for XML input are: "
<< m_xmlBrackets.first << " and " << m_xmlBrackets.second << endl);
}
if (m_parameter->GetParam("placeholder-factor").size() > 0) {
m_placeHolderFactor = Scan<FactorType>(m_parameter->GetParam("placeholder-factor")[0]);
} else {
m_placeHolderFactor = NOT_FOUND;
}
std::map<std::string, std::string> featureNameOverride = OverrideFeatureNames();
// all features
map<string, int> featureIndexMap;
const vector<string> &features = m_parameter->GetParam("feature");
for (size_t i = 0; i < features.size(); ++i) {
const string &line = Trim(features[i]);
VERBOSE(1,"line=" << line << endl);
if (line.empty())
continue;
vector<string> toks = Tokenize(line);
string &feature = toks[0];
std::map<std::string, std::string>::const_iterator iter = featureNameOverride.find(feature);
if (iter == featureNameOverride.end()) {
// feature name not override
m_registry.Construct(feature, line);
}
else {
// replace feature name with new name
string newName = iter->second;
feature = newName;
string newLine = Join(" ", toks);
m_registry.Construct(newName, newLine);
}
}
NoCache();
OverrideFeatures();
if (!m_parameter->isParamSpecified("show-weights")) {
LoadFeatureFunctions();
}
if (!LoadDecodeGraphs()) return false;
if (!CheckWeights()) {
return false;
}
//Add any other features here.
//Load extra feature weights
vector<string> extraWeightConfig = m_parameter->GetParam("weight-file");
if (extraWeightConfig.size()) {
if (extraWeightConfig.size() != 1) {
UserMessage::Add("One argument should be supplied for weight-file");
return false;
}
ScoreComponentCollection extraWeights;
if (!extraWeights.Load(extraWeightConfig[0])) {
UserMessage::Add("Unable to load weights from " + extraWeightConfig[0]);
return false;
}
m_allWeights.PlusEquals(extraWeights);
}
//Load sparse features from config (overrules weight file)
LoadSparseWeightsFromConfig();
// alternate weight settings
if (m_parameter->GetParam("alternate-weight-setting").size() > 0) {
if (!LoadAlternateWeightSettings()) {
return false;
}
}
return true;
}
void StaticData::SetBooleanParameter( bool *parameter, string parameterName, bool defaultValue )
{
// default value if nothing is specified
*parameter = defaultValue;
if (! m_parameter->isParamSpecified( parameterName ) ) {
return;
}
// if parameter is just specified as, e.g. "-parameter" set it true
if (m_parameter->GetParam( parameterName ).size() == 0) {
*parameter = true;
}
// if paramter is specified "-parameter true" or "-parameter false"
else if (m_parameter->GetParam( parameterName ).size() == 1) {
*parameter = Scan<bool>( m_parameter->GetParam( parameterName )[0]);
}
}
void StaticData::SetWeight(const FeatureFunction* sp, float weight)
{
m_allWeights.Resize();
m_allWeights.Assign(sp,weight);
}
void StaticData::SetWeights(const FeatureFunction* sp, const std::vector<float>& weights)
{
m_allWeights.Resize();
m_allWeights.Assign(sp,weights);
}
void StaticData::LoadNonTerminals()
{
string defaultNonTerminals;
if (m_parameter->GetParam("non-terminals").size() == 0) {
defaultNonTerminals = "X";
} else {
vector<std::string> tokens = Tokenize(m_parameter->GetParam("non-terminals")[0]);
defaultNonTerminals = tokens[0];
}
FactorCollection &factorCollection = FactorCollection::Instance();
m_inputDefaultNonTerminal.SetIsNonTerminal(true);
const Factor *sourceFactor = factorCollection.AddFactor(Input, 0, defaultNonTerminals, true);
m_inputDefaultNonTerminal.SetFactor(0, sourceFactor);
m_outputDefaultNonTerminal.SetIsNonTerminal(true);
const Factor *targetFactor = factorCollection.AddFactor(Output, 0, defaultNonTerminals, true);
m_outputDefaultNonTerminal.SetFactor(0, targetFactor);
// for unknwon words
if (m_parameter->GetParam("unknown-lhs").size() == 0) {
UnknownLHSEntry entry(defaultNonTerminals, 0.0f);
m_unknownLHS.push_back(entry);
} else {
const string &filePath = m_parameter->GetParam("unknown-lhs")[0];
InputFileStream inStream(filePath);
string line;
while(getline(inStream, line)) {
vector<string> tokens = Tokenize(line);
UTIL_THROW_IF2(tokens.size() != 2,
"Incorrect unknown LHS format: " << line);
UnknownLHSEntry entry(tokens[0], Scan<float>(tokens[1]));
m_unknownLHS.push_back(entry);
// const Factor *targetFactor =
factorCollection.AddFactor(Output, 0, tokens[0], true);
}
}
}
void StaticData::LoadChartDecodingParameters()
{
LoadNonTerminals();
// source label overlap
if (m_parameter->GetParam("source-label-overlap").size() > 0) {
m_sourceLabelOverlap = (SourceLabelOverlap) Scan<int>(m_parameter->GetParam("source-label-overlap")[0]);
} else {
m_sourceLabelOverlap = SourceLabelOverlapAdd;
}
m_ruleLimit = (m_parameter->GetParam("rule-limit").size() > 0)
? Scan<size_t>(m_parameter->GetParam("rule-limit")[0]) : DEFAULT_MAX_TRANS_OPT_SIZE;
}
bool StaticData::LoadDecodeGraphs()
{
const vector<string> &mappingVector = m_parameter->GetParam("mapping");
const vector<size_t> &maxChartSpans = Scan<size_t>(m_parameter->GetParam("max-chart-span"));
const vector<PhraseDictionary*>& pts = PhraseDictionary::GetColl();
const vector<GenerationDictionary*>& gens = GenerationDictionary::GetColl();
const std::vector<FeatureFunction*> *featuresRemaining = &FeatureFunction::GetFeatureFunctions();
DecodeStep *prev = 0;
size_t prevDecodeGraphInd = 0;
for(size_t i=0; i<mappingVector.size(); i++) {
vector<string> token = Tokenize(mappingVector[i]);
size_t decodeGraphInd;
DecodeType decodeType;
size_t index;
if (token.size() == 2) {
decodeGraphInd = 0;
decodeType = token[0] == "T" ? Translate : Generate;
index = Scan<size_t>(token[1]);
} else if (token.size() == 3) {
// For specifying multiple translation model
decodeGraphInd = Scan<size_t>(token[0]);
//the vectorList index can only increment by one
UTIL_THROW_IF2(decodeGraphInd != prevDecodeGraphInd && decodeGraphInd != prevDecodeGraphInd + 1,
"Malformed mapping");
if (decodeGraphInd > prevDecodeGraphInd) {
prev = NULL;
}
if (prevDecodeGraphInd < decodeGraphInd) {
featuresRemaining = &FeatureFunction::GetFeatureFunctions();
}
decodeType = token[1] == "T" ? Translate : Generate;
index = Scan<size_t>(token[2]);
} else {
UTIL_THROW(util::Exception, "Malformed mapping");
}
DecodeStep* decodeStep = NULL;
switch (decodeType) {
case Translate:
if(index>=pts.size()) {
stringstream strme;
strme << "No phrase dictionary with index "
<< index << " available!";
UTIL_THROW(util::Exception, strme.str());
}
decodeStep = new DecodeStepTranslation(pts[index], prev, *featuresRemaining);
break;
case Generate:
if(index>=gens.size()) {
stringstream strme;
strme << "No generation dictionary with index "
<< index << " available!";
UTIL_THROW(util::Exception, strme.str());
}
decodeStep = new DecodeStepGeneration(gens[index], prev, *featuresRemaining);
break;
case InsertNullFertilityWord:
UTIL_THROW(util::Exception, "Please implement NullFertilityInsertion.");
break;
}
featuresRemaining = &decodeStep->GetFeaturesRemaining();
UTIL_THROW_IF2(decodeStep == NULL, "Null decode step");
if (m_decodeGraphs.size() < decodeGraphInd + 1) {
DecodeGraph *decodeGraph;
if (IsChart()) {
size_t maxChartSpan = (decodeGraphInd < maxChartSpans.size()) ? maxChartSpans[decodeGraphInd] : DEFAULT_MAX_CHART_SPAN;
VERBOSE(1,"max-chart-span: " << maxChartSpans[decodeGraphInd] << endl);
decodeGraph = new DecodeGraph(m_decodeGraphs.size(), maxChartSpan);
} else {
decodeGraph = new DecodeGraph(m_decodeGraphs.size());
}
m_decodeGraphs.push_back(decodeGraph); // TODO max chart span
}
m_decodeGraphs[decodeGraphInd]->Add(decodeStep);
prev = decodeStep;
prevDecodeGraphInd = decodeGraphInd;
}
// set maximum n-gram size for backoff approach to decoding paths
// default is always use subsequent paths (value = 0)
// if specified, record maxmimum unseen n-gram size
const vector<string> &backoffVector = m_parameter->GetParam("decoding-graph-backoff");
for(size_t i=0; i<m_decodeGraphs.size() && i<backoffVector.size(); i++) {
DecodeGraph &decodeGraph = *m_decodeGraphs[i];
if (i < backoffVector.size()) {
decodeGraph.SetBackoff(Scan<size_t>(backoffVector[i]));
}
}
return true;
}
void StaticData::ReLoadParameter()
{
UTIL_THROW(util::Exception, "completely redo. Too many hardcoded ff"); // TODO completely redo. Too many hardcoded ff
/*
m_verboseLevel = 1;
if (m_parameter->GetParam("verbose").size() == 1) {
m_verboseLevel = Scan<size_t>( m_parameter->GetParam("verbose")[0]);
}
// check whether "weight-u" is already set
if (m_parameter->isParamShortNameSpecified("u")) {
if (m_parameter->GetParamShortName("u").size() < 1 ) {
PARAM_VEC w(1,"1.0");
m_parameter->OverwriteParamShortName("u", w);
}
}
//loop over all ScoreProducer to update weights
std::vector<const ScoreProducer*>::const_iterator iterSP;
for (iterSP = transSystem.GetFeatureFunctions().begin() ; iterSP != transSystem.GetFeatureFunctions().end() ; ++iterSP) {
std::string paramShortName = (*iterSP)->GetScoreProducerWeightShortName();
vector<float> Weights = Scan<float>(m_parameter->GetParamShortName(paramShortName));
if (paramShortName == "d") { //basic distortion model takes the first weight
if ((*iterSP)->GetScoreProducerDescription() == "Distortion") {
Weights.resize(1); //take only the first element
} else { //lexicalized reordering model takes the other
Weights.erase(Weights.begin()); //remove the first element
}
// std::cerr << "this is the Distortion Score Producer -> " << (*iterSP)->GetScoreProducerDescription() << std::cerr;
// std::cerr << "this is the Distortion Score Producer; it has " << (*iterSP)->GetNumScoreComponents() << " weights"<< std::cerr;
// std::cerr << Weights << std::endl;
} else if (paramShortName == "tm") {
continue;
}
SetWeights(*iterSP, Weights);
}
// std::cerr << "There are " << m_phraseDictionary.size() << " m_phraseDictionaryfeatures" << std::endl;
const vector<float> WeightsTM = Scan<float>(m_parameter->GetParamShortName("tm"));
// std::cerr << "WeightsTM: " << WeightsTM << std::endl;
const vector<float> WeightsLM = Scan<float>(m_parameter->GetParamShortName("lm"));
// std::cerr << "WeightsLM: " << WeightsLM << std::endl;
size_t index_WeightTM = 0;
for(size_t i=0; i<transSystem.GetPhraseDictionaries().size(); ++i) {
PhraseDictionaryFeature &phraseDictionaryFeature = *m_phraseDictionary[i];
// std::cerr << "phraseDictionaryFeature.GetNumScoreComponents():" << phraseDictionaryFeature.GetNumScoreComponents() << std::endl;
// std::cerr << "phraseDictionaryFeature.GetNumInputScores():" << phraseDictionaryFeature.GetNumInputScores() << std::endl;
vector<float> tmp_weights;
for(size_t j=0; j<phraseDictionaryFeature.GetNumScoreComponents(); ++j)
tmp_weights.push_back(WeightsTM[index_WeightTM++]);
// std::cerr << tmp_weights << std::endl;
SetWeights(&phraseDictionaryFeature, tmp_weights);
}
*/
}
void StaticData::ReLoadBleuScoreFeatureParameter(float weight)
{
assert(false);
/*
//loop over ScoreProducers to update weights of BleuScoreFeature
std::vector<const ScoreProducer*>::const_iterator iterSP;
for (iterSP = transSystem.GetFeatureFunctions().begin() ; iterSP != transSystem.GetFeatureFunctions().end() ; ++iterSP) {
std::string paramShortName = (*iterSP)->GetScoreProducerWeightShortName();
if (paramShortName == "bl") {
SetWeight(*iterSP, weight);
break;
}
}
*/
}
// ScoreComponentCollection StaticData::GetAllWeightsScoreComponentCollection() const {}
// in ScoreComponentCollection.h
void StaticData::SetExecPath(const std::string &path)
{
/*
namespace fs = boost::filesystem;
fs::path full_path( fs::initial_path<fs::path>() );
full_path = fs::system_complete( fs::path( path ) );
//Without file name
m_binPath = full_path.parent_path().string();
*/
// NOT TESTED
size_t pos = path.rfind("/");
if (pos != string::npos) {
m_binPath = path.substr(0, pos);
}
VERBOSE(1,m_binPath << endl);
}
const string &StaticData::GetBinDirectory() const
{
return m_binPath;
}
float StaticData::GetWeightWordPenalty() const
{
float weightWP = GetWeight(&WordPenaltyProducer::Instance());
//VERBOSE(1, "Read weightWP from translation sytem: " << weightWP << std::endl);
return weightWP;
}
float StaticData::GetWeightUnknownWordPenalty() const
{
return GetWeight(&UnknownWordPenaltyProducer::Instance());
}
void StaticData::InitializeForInput(const InputType& source) const
{
const std::vector<FeatureFunction*> &producers = FeatureFunction::GetFeatureFunctions();
for(size_t i=0; i<producers.size(); ++i) {
FeatureFunction &ff = *producers[i];
if (! IsFeatureFunctionIgnored(ff)) {
Timer iTime;
iTime.start();
ff.InitializeForInput(source);
VERBOSE(3,"InitializeForInput( " << ff.GetScoreProducerDescription() << " ) = " << iTime << endl);
}
}
}
void StaticData::CleanUpAfterSentenceProcessing(const InputType& source) const
{
const std::vector<FeatureFunction*> &producers = FeatureFunction::GetFeatureFunctions();
for(size_t i=0; i<producers.size(); ++i) {
FeatureFunction &ff = *producers[i];
if (! IsFeatureFunctionIgnored(ff)) {
ff.CleanUpAfterSentenceProcessing(source);
}
}
}
void StaticData::LoadFeatureFunctions()
{
const std::vector<FeatureFunction*> &ffs
= FeatureFunction::GetFeatureFunctions();
std::vector<FeatureFunction*>::const_iterator iter;
for (iter = ffs.begin(); iter != ffs.end(); ++iter) {
FeatureFunction *ff = *iter;
bool doLoad = true;
// if (PhraseDictionary *ffCast = dynamic_cast<PhraseDictionary*>(ff)) {
if (dynamic_cast<PhraseDictionary*>(ff)) {
doLoad = false;
}
if (doLoad) {
VERBOSE(1, "Loading " << ff->GetScoreProducerDescription() << endl);
ff->Load();
}
}
const std::vector<PhraseDictionary*> &pts = PhraseDictionary::GetColl();
for (size_t i = 0; i < pts.size(); ++i) {
PhraseDictionary *pt = pts[i];
VERBOSE(1, "Loading " << pt->GetScoreProducerDescription() << endl);
pt->Load();
}
CheckLEGACYPT();
}
bool StaticData::CheckWeights() const
{
set<string> weightNames = m_parameter->GetWeightNames();
set<string> featureNames;
const std::vector<FeatureFunction*> &ffs = FeatureFunction::GetFeatureFunctions();
for (size_t i = 0; i < ffs.size(); ++i) {
const FeatureFunction &ff = *ffs[i];
const string &descr = ff.GetScoreProducerDescription();
featureNames.insert(descr);
set<string>::iterator iter = weightNames.find(descr);
if (iter == weightNames.end()) {
cerr << "Can't find weights for feature function " << descr << endl;
} else {
weightNames.erase(iter);
}
}
//sparse features
if (!weightNames.empty()) {
set<string>::iterator iter;
for (iter = weightNames.begin(); iter != weightNames.end(); ) {
string fname = (*iter).substr(0, (*iter).find("_"));
VERBOSE(1,fname << "\n");
if (featureNames.find(fname) != featureNames.end()) {
weightNames.erase(iter++);
}
else {
++iter;
}
}
}
if (!weightNames.empty()) {
cerr << "The following weights have no feature function. Maybe incorrectly spelt weights: ";
set<string>::iterator iter;
for (iter = weightNames.begin(); iter != weightNames.end(); ++iter) {
cerr << *iter << ",";
}
return false;
}
return true;
}
void StaticData::LoadSparseWeightsFromConfig() {
set<string> featureNames;
const std::vector<FeatureFunction*> &ffs = FeatureFunction::GetFeatureFunctions();
for (size_t i = 0; i < ffs.size(); ++i) {
const FeatureFunction &ff = *ffs[i];
const string &descr = ff.GetScoreProducerDescription();
featureNames.insert(descr);
}
std::map<std::string, std::vector<float> > weights = m_parameter->GetAllWeights();
std::map<std::string, std::vector<float> >::iterator iter;
for (iter = weights.begin(); iter != weights.end(); ++iter) {
// this indicates that it is sparse feature
if (featureNames.find(iter->first) == featureNames.end()) {
UTIL_THROW_IF2(iter->second.size() != 1, "ERROR: only one weight per sparse feature allowed: " << iter->first);
m_allWeights.Assign(iter->first, iter->second[0]);
}
}
}
/**! Read in settings for alternative weights */
bool StaticData::LoadAlternateWeightSettings()
{
if (m_threadCount > 1) {
cerr << "ERROR: alternative weight settings currently not supported with multi-threading.";
return false;
}
const vector<string> &weightSpecification = m_parameter->GetParam("alternate-weight-setting");
// get mapping from feature names to feature functions
map<string,FeatureFunction*> nameToFF;
const std::vector<FeatureFunction*> &ffs = FeatureFunction::GetFeatureFunctions();
for (size_t i = 0; i < ffs.size(); ++i) {
nameToFF[ ffs[i]->GetScoreProducerDescription() ] = ffs[i];
}
// copy main weight setting as default
m_weightSetting["default"] = new ScoreComponentCollection( m_allWeights );
// go through specification in config file
string currentId = "";
bool hasErrors = false;
for (size_t i=0; i<weightSpecification.size(); ++i) {
// identifier line (with optional additional specifications)
if (weightSpecification[i].find("id=") == 0) {
vector<string> tokens = Tokenize(weightSpecification[i]);
vector<string> args = Tokenize(tokens[0], "=");
currentId = args[1];
VERBOSE(1,"alternate weight setting " << currentId << endl);
UTIL_THROW_IF2(m_weightSetting.find(currentId) != m_weightSetting.end(),
"Duplicate alternate weight id: " << currentId);
m_weightSetting[ currentId ] = new ScoreComponentCollection;
// other specifications
for(size_t j=1; j<tokens.size(); j++) {
vector<string> args = Tokenize(tokens[j], "=");
// sparse weights
if (args[0] == "weight-file") {
if (args.size() != 2) {
UserMessage::Add("One argument should be supplied for weight-file");
return false;
}
ScoreComponentCollection extraWeights;
if (!extraWeights.Load(args[1])) {
UserMessage::Add("Unable to load weights from " + args[1]);
return false;
}
m_weightSetting[ currentId ]->PlusEquals(extraWeights);
}
// ignore feature functions
else if (args[0] == "ignore-ff") {
set< string > *ffNameSet = new set< string >;
m_weightSettingIgnoreFF[ currentId ] = *ffNameSet;
vector<string> featureFunctionName = Tokenize(args[1], ",");
for(size_t k=0; k<featureFunctionName.size(); k++) {
// check if a valid nane
map<string,FeatureFunction*>::iterator ffLookUp
= nameToFF.find(featureFunctionName[k]);
if (ffLookUp == nameToFF.end()) {
cerr << "ERROR: alternate weight setting " << currentId
<< " specifies to ignore feature function " << featureFunctionName[k]
<< " but there is no such feature function" << endl;
hasErrors = true;
} else {
m_weightSettingIgnoreFF[ currentId ].insert( featureFunctionName[k] );
}
}
}
}
}
// weight lines
else {
UTIL_THROW_IF2(currentId.empty(), "No alternative weights specified");
vector<string> tokens = Tokenize(weightSpecification[i]);
UTIL_THROW_IF2(tokens.size() < 2
, "Incorrect format for alternate weights: " << weightSpecification[i]);
// get name and weight values
string name = tokens[0];
name = name.substr(0, name.size() - 1); // remove trailing "="
vector<float> weights(tokens.size() - 1);
for (size_t i = 1; i < tokens.size(); ++i) {
float weight = Scan<float>(tokens[i]);
weights[i - 1] = weight;
}
// check if a valid nane
map<string,FeatureFunction*>::iterator ffLookUp = nameToFF.find(name);
if (ffLookUp == nameToFF.end()) {
cerr << "ERROR: alternate weight setting " << currentId
<< " specifies weight(s) for " << name
<< " but there is no such feature function" << endl;
hasErrors = true;
} else {
m_weightSetting[ currentId ]->Assign( nameToFF[name], weights);
}
}
}
UTIL_THROW_IF2(hasErrors, "Errors loading alternate weights");
return true;
}
void StaticData::NoCache()
{
bool noCache;
SetBooleanParameter( &noCache, "no-cache", false );
if (noCache) {
const std::vector<PhraseDictionary*> &pts = PhraseDictionary::GetColl();
for (size_t i = 0; i < pts.size(); ++i) {
PhraseDictionary &pt = *pts[i];
pt.SetParameter("cache-size", "0");
}
}
}
std::map<std::string, std::string> StaticData::OverrideFeatureNames()
{
std::map<std::string, std::string> ret;
const PARAM_VEC &params = m_parameter->GetParam("feature-name-overwrite");
if (params.size()) {
UTIL_THROW_IF2(params.size() != 1, "Only provide 1 line in the section [feature-name-overwrite]");
vector<string> toks = Tokenize(params[0]);
UTIL_THROW_IF2(toks.size() % 2 != 0, "Format of -feature-name-overwrite must be [old-name new-name]*");
for (size_t i = 0; i < toks.size(); i += 2) {
const string &oldName = toks[i];
const string &newName = toks[i+1];
ret[oldName] = newName;
}
}
return ret;
}
void StaticData::OverrideFeatures()
{
const PARAM_VEC &params = m_parameter->GetParam("feature-overwrite");
for (size_t i = 0; i < params.size(); ++i) {
const string &str = params[i];
vector<string> toks = Tokenize(str);
UTIL_THROW_IF2(toks.size() <= 1, "Incorrect format for feature override: " << str);
FeatureFunction &ff = FeatureFunction::FindFeatureFunction(toks[0]);
for (size_t j = 1; j < toks.size(); ++j) {
const string &keyValStr = toks[j];
vector<string> keyVal = Tokenize(keyValStr, "=");
UTIL_THROW_IF2(keyVal.size() != 2, "Incorrect format for parameter override: " << keyValStr);
VERBOSE(1, "Override " << ff.GetScoreProducerDescription() << " "
<< keyVal[0] << "=" << keyVal[1] << endl);
ff.SetParameter(keyVal[0], keyVal[1]);
}
}
}
void StaticData::CheckLEGACYPT()
{
const std::vector<PhraseDictionary*> &pts = PhraseDictionary::GetColl();
for (size_t i = 0; i < pts.size(); ++i) {
const PhraseDictionary *phraseDictionary = pts[i];
if (dynamic_cast<const PhraseDictionaryTreeAdaptor*>(phraseDictionary) != NULL) {
m_useLegacyPT = true;
return;
}
}
m_useLegacyPT = false;
}
void StaticData::ResetWeights(const std::string &denseWeights, const std::string &sparseFile)
{
m_allWeights = ScoreComponentCollection();
// dense weights
string name("");
vector<float> weights;
vector<string> toks = Tokenize(denseWeights);
for (size_t i = 0; i < toks.size(); ++i) {
const string &tok = toks[i];
if (tok.substr(tok.size() - 1, 1) == "=") {
// start of new feature
if (name != "") {
// save previous ff
const FeatureFunction &ff = FeatureFunction::FindFeatureFunction(name);
m_allWeights.Assign(&ff, weights);
weights.clear();
}
name = tok.substr(0, tok.size() - 1);
} else {
// a weight for curr ff
float weight = Scan<float>(toks[i]);
weights.push_back(weight);
}
}
const FeatureFunction &ff = FeatureFunction::FindFeatureFunction(name);
m_allWeights.Assign(&ff, weights);
// sparse weights
InputFileStream sparseStrme(sparseFile);
string line;
while (getline(sparseStrme, line)) {
vector<string> toks = Tokenize(line);
UTIL_THROW_IF2(toks.size() != 2, "Incorrect sparse weight format. Should be FFName_spareseName weight");
vector<string> names = Tokenize(toks[0], "_");
UTIL_THROW_IF2(names.size() != 2, "Incorrect sparse weight name. Should be FFName_spareseName");
const FeatureFunction &ff = FeatureFunction::FindFeatureFunction(names[0]);
m_allWeights.Assign(&ff, names[1], Scan<float>(toks[1]));
}
}
} // namespace
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