mirror of
https://github.com/moses-smt/mosesdecoder.git
synced 2024-12-27 05:55:02 +03:00
446 lines
18 KiB
C++
446 lines
18 KiB
C++
#include "Optimiser.h"
|
|
#include "Hildreth.h"
|
|
#include "moses/StaticData.h"
|
|
|
|
using namespace Moses;
|
|
using namespace std;
|
|
|
|
namespace Mira
|
|
{
|
|
|
|
size_t MiraOptimiser::updateWeights(
|
|
ScoreComponentCollection& weightUpdate,
|
|
const vector<vector<ScoreComponentCollection> >& featureValues,
|
|
const vector<vector<float> >& losses,
|
|
const vector<vector<float> >& bleuScores,
|
|
const vector<vector<float> >& modelScores,
|
|
const vector<ScoreComponentCollection>& oracleFeatureValues,
|
|
const vector<float> oracleBleuScores,
|
|
const vector<float> oracleModelScores,
|
|
float learning_rate,
|
|
size_t rank,
|
|
size_t epoch)
|
|
{
|
|
|
|
// vector of feature values differences for all created constraints
|
|
vector<ScoreComponentCollection> featureValueDiffs;
|
|
vector<float> lossMinusModelScoreDiffs;
|
|
vector<float> all_losses;
|
|
|
|
// most violated constraint in batch
|
|
ScoreComponentCollection max_batch_featureValueDiff;
|
|
|
|
// Make constraints for new hypothesis translations
|
|
float epsilon = 0.0001;
|
|
int violatedConstraintsBefore = 0;
|
|
float oldDistanceFromOptimum = 0;
|
|
// iterate over input sentences (1 (online) or more (batch))
|
|
for (size_t i = 0; i < featureValues.size(); ++i) {
|
|
//size_t sentenceId = sentenceIds[i];
|
|
// iterate over hypothesis translations for one input sentence
|
|
for (size_t j = 0; j < featureValues[i].size(); ++j) {
|
|
ScoreComponentCollection featureValueDiff = oracleFeatureValues[i];
|
|
featureValueDiff.MinusEquals(featureValues[i][j]);
|
|
|
|
// cerr << "Rank " << rank << ", epoch " << epoch << ", feature value diff: " << featureValueDiff << endl;
|
|
if (featureValueDiff.GetL1Norm() == 0) {
|
|
cerr << "Rank " << rank << ", epoch " << epoch << ", features equal --> skip" << endl;
|
|
continue;
|
|
}
|
|
|
|
float loss = losses[i][j];
|
|
|
|
// check if constraint is violated
|
|
bool violated = false;
|
|
// float modelScoreDiff = featureValueDiff.InnerProduct(currWeights);
|
|
float modelScoreDiff = oracleModelScores[i] - modelScores[i][j];
|
|
float diff = 0;
|
|
|
|
if (loss > modelScoreDiff)
|
|
diff = loss - modelScoreDiff;
|
|
cerr << "Rank " << rank << ", epoch " << epoch << ", constraint: " << modelScoreDiff << " >= " << loss << " (current violation: " << diff << ")" << endl;
|
|
if (diff > epsilon)
|
|
violated = true;
|
|
|
|
if (m_normaliseMargin) {
|
|
modelScoreDiff = (2*m_sigmoidParam/(1 + exp(-modelScoreDiff))) - m_sigmoidParam;
|
|
loss = (2*m_sigmoidParam/(1 + exp(-loss))) - m_sigmoidParam;
|
|
diff = 0;
|
|
if (loss > modelScoreDiff) {
|
|
diff = loss - modelScoreDiff;
|
|
}
|
|
cerr << "Rank " << rank << ", epoch " << epoch << ", normalised constraint: " << modelScoreDiff << " >= " << loss << " (current violation: " << diff << ")" << endl;
|
|
}
|
|
|
|
if (m_scale_margin) {
|
|
diff *= oracleBleuScores[i];
|
|
cerr << "Rank " << rank << ", epoch " << epoch << ", scaling margin with oracle bleu score " << oracleBleuScores[i] << endl;
|
|
}
|
|
|
|
featureValueDiffs.push_back(featureValueDiff);
|
|
lossMinusModelScoreDiffs.push_back(diff);
|
|
all_losses.push_back(loss);
|
|
if (violated) {
|
|
++violatedConstraintsBefore;
|
|
oldDistanceFromOptimum += diff;
|
|
}
|
|
}
|
|
}
|
|
|
|
// run optimisation: compute alphas for all given constraints
|
|
vector<float> alphas;
|
|
ScoreComponentCollection summedUpdate;
|
|
if (violatedConstraintsBefore > 0) {
|
|
cerr << "Rank " << rank << ", epoch " << epoch << ", number of constraints passed to optimizer: " <<
|
|
featureValueDiffs.size() << " (of which violated: " << violatedConstraintsBefore << ")" << endl;
|
|
if (m_slack != 0) {
|
|
alphas = Hildreth::optimise(featureValueDiffs, lossMinusModelScoreDiffs, m_slack);
|
|
} else {
|
|
alphas = Hildreth::optimise(featureValueDiffs, lossMinusModelScoreDiffs);
|
|
}
|
|
|
|
// Update the weight vector according to the alphas and the feature value differences
|
|
// * w' = w' + SUM alpha_i * (h_i(oracle) - h_i(hypothesis))
|
|
for (size_t k = 0; k < featureValueDiffs.size(); ++k) {
|
|
float alpha = alphas[k];
|
|
cerr << "Rank " << rank << ", epoch " << epoch << ", alpha: " << alpha << endl;
|
|
ScoreComponentCollection update(featureValueDiffs[k]);
|
|
update.MultiplyEquals(alpha);
|
|
|
|
// sum updates
|
|
summedUpdate.PlusEquals(update);
|
|
}
|
|
} else {
|
|
cerr << "Rank " << rank << ", epoch " << epoch << ", no constraint violated for this batch" << endl;
|
|
// return 0;
|
|
return 1;
|
|
}
|
|
|
|
// apply learning rate
|
|
if (learning_rate != 1) {
|
|
cerr << "Rank " << rank << ", epoch " << epoch << ", apply learning rate " << learning_rate << " to update." << endl;
|
|
summedUpdate.MultiplyEquals(learning_rate);
|
|
}
|
|
|
|
// scale update by BLEU of oracle (for batch size 1 only)
|
|
if (oracleBleuScores.size() == 1) {
|
|
if (m_scale_update) {
|
|
cerr << "Rank " << rank << ", epoch " << epoch << ", scaling summed update with oracle bleu score " << oracleBleuScores[0] << endl;
|
|
summedUpdate.MultiplyEquals(oracleBleuScores[0]);
|
|
}
|
|
}
|
|
|
|
// cerr << "Rank " << rank << ", epoch " << epoch << ", update: " << summedUpdate << endl;
|
|
weightUpdate.PlusEquals(summedUpdate);
|
|
|
|
// Sanity check: are there still violated constraints after optimisation?
|
|
/* int violatedConstraintsAfter = 0;
|
|
float newDistanceFromOptimum = 0;
|
|
for (size_t i = 0; i < featureValueDiffs.size(); ++i) {
|
|
float modelScoreDiff = featureValueDiffs[i].InnerProduct(currWeights);
|
|
float loss = all_losses[i];
|
|
float diff = loss - modelScoreDiff;
|
|
if (diff > epsilon) {
|
|
++violatedConstraintsAfter;
|
|
newDistanceFromOptimum += diff;
|
|
}
|
|
}
|
|
VERBOSE(1, "Rank " << rank << ", epoch " << epoch << ", violated constraint before: " << violatedConstraintsBefore << ", after: " << violatedConstraintsAfter << ", change: " << violatedConstraintsBefore - violatedConstraintsAfter << endl);
|
|
VERBOSE(1, "Rank " << rank << ", epoch " << epoch << ", error before: " << oldDistanceFromOptimum << ", after: " << newDistanceFromOptimum << ", change: " << oldDistanceFromOptimum - newDistanceFromOptimum << endl);*/
|
|
// return violatedConstraintsAfter;
|
|
return 0;
|
|
}
|
|
|
|
size_t MiraOptimiser::updateWeightsHopeFear(
|
|
Moses::ScoreComponentCollection& weightUpdate,
|
|
const std::vector< std::vector<Moses::ScoreComponentCollection> >& featureValuesHope,
|
|
const std::vector< std::vector<Moses::ScoreComponentCollection> >& featureValuesFear,
|
|
const std::vector<std::vector<float> >& bleuScoresHope,
|
|
const std::vector<std::vector<float> >& bleuScoresFear,
|
|
const std::vector<std::vector<float> >& modelScoresHope,
|
|
const std::vector<std::vector<float> >& modelScoresFear,
|
|
float learning_rate,
|
|
size_t rank,
|
|
size_t epoch,
|
|
int updatePosition)
|
|
{
|
|
|
|
// vector of feature values differences for all created constraints
|
|
vector<ScoreComponentCollection> featureValueDiffs;
|
|
vector<float> lossMinusModelScoreDiffs;
|
|
vector<float> modelScoreDiffs;
|
|
vector<float> all_losses;
|
|
|
|
// most violated constraint in batch
|
|
ScoreComponentCollection max_batch_featureValueDiff;
|
|
|
|
// Make constraints for new hypothesis translations
|
|
float epsilon = 0.0001;
|
|
int violatedConstraintsBefore = 0;
|
|
float oldDistanceFromOptimum = 0;
|
|
|
|
// iterate over input sentences (1 (online) or more (batch))
|
|
for (size_t i = 0; i < featureValuesHope.size(); ++i) {
|
|
if (updatePosition != -1) {
|
|
if (i < updatePosition)
|
|
continue;
|
|
else if (i > updatePosition)
|
|
break;
|
|
}
|
|
|
|
// Pick all pairs[j,j] of hope and fear translations for one input sentence
|
|
for (size_t j = 0; j < featureValuesHope[i].size(); ++j) {
|
|
ScoreComponentCollection featureValueDiff = featureValuesHope[i][j];
|
|
featureValueDiff.MinusEquals(featureValuesFear[i][j]);
|
|
//cerr << "Rank " << rank << ", epoch " << epoch << ", feature value diff: " << featureValueDiff << endl;
|
|
if (featureValueDiff.GetL1Norm() == 0) {
|
|
cerr << "Rank " << rank << ", epoch " << epoch << ", features equal --> skip" << endl;
|
|
continue;
|
|
}
|
|
|
|
float loss = bleuScoresHope[i][j] - bleuScoresFear[i][j];
|
|
|
|
// check if constraint is violated
|
|
bool violated = false;
|
|
//float modelScoreDiff = featureValueDiff.InnerProduct(currWeights);
|
|
float modelScoreDiff = modelScoresHope[i][j] - modelScoresFear[i][j];
|
|
float diff = 0;
|
|
if (loss > modelScoreDiff)
|
|
diff = loss - modelScoreDiff;
|
|
cerr << "Rank " << rank << ", epoch " << epoch << ", constraint: " << modelScoreDiff << " >= " << loss << " (current violation: " << diff << ")" << endl;
|
|
|
|
if (diff > epsilon)
|
|
violated = true;
|
|
|
|
if (m_normaliseMargin) {
|
|
modelScoreDiff = (2*m_sigmoidParam/(1 + exp(-modelScoreDiff))) - m_sigmoidParam;
|
|
loss = (2*m_sigmoidParam/(1 + exp(-loss))) - m_sigmoidParam;
|
|
diff = 0;
|
|
if (loss > modelScoreDiff) {
|
|
diff = loss - modelScoreDiff;
|
|
}
|
|
cerr << "Rank " << rank << ", epoch " << epoch << ", normalised constraint: " << modelScoreDiff << " >= " << loss << " (current violation: " << diff << ")" << endl;
|
|
}
|
|
|
|
if (m_scale_margin) {
|
|
diff *= bleuScoresHope[i][j];
|
|
cerr << "Rank " << rank << ", epoch " << epoch << ", scaling margin with oracle bleu score " << bleuScoresHope[i][j] << endl;
|
|
}
|
|
|
|
featureValueDiffs.push_back(featureValueDiff);
|
|
lossMinusModelScoreDiffs.push_back(diff);
|
|
modelScoreDiffs.push_back(modelScoreDiff);
|
|
all_losses.push_back(loss);
|
|
if (violated) {
|
|
++violatedConstraintsBefore;
|
|
oldDistanceFromOptimum += diff;
|
|
}
|
|
}
|
|
}
|
|
|
|
// run optimisation: compute alphas for all given constraints
|
|
vector<float> alphas;
|
|
ScoreComponentCollection summedUpdate;
|
|
if (violatedConstraintsBefore > 0) {
|
|
cerr << "Rank " << rank << ", epoch " << epoch << ", number of constraints passed to optimizer: " <<
|
|
featureValueDiffs.size() << " (of which violated: " << violatedConstraintsBefore << ")" << endl;
|
|
if (m_slack != 0) {
|
|
alphas = Hildreth::optimise(featureValueDiffs, lossMinusModelScoreDiffs, m_slack);
|
|
} else {
|
|
alphas = Hildreth::optimise(featureValueDiffs, lossMinusModelScoreDiffs);
|
|
}
|
|
|
|
// Update the weight vector according to the alphas and the feature value differences
|
|
// * w' = w' + SUM alpha_i * (h_i(oracle) - h_i(hypothesis))
|
|
for (size_t k = 0; k < featureValueDiffs.size(); ++k) {
|
|
float alpha = alphas[k];
|
|
cerr << "Rank " << rank << ", epoch " << epoch << ", alpha: " << alpha << endl;
|
|
if (alpha != 0) {
|
|
// apply boosting factor
|
|
if (m_boost && modelScoreDiffs[k] <= 0) {
|
|
// factor between 1.5 and 3 (for Bleu scores between 5 and 20, the factor is within the boundaries)
|
|
float factor = min(1.5, log2(bleuScoresHope[0][0])); // TODO: make independent of number of oracles!!
|
|
factor = min(3.0f, factor);
|
|
alpha = alpha * factor;
|
|
cerr << "Rank " << rank << ", epoch " << epoch << ", apply boosting factor " << factor << " to update." << endl;
|
|
}
|
|
|
|
ScoreComponentCollection update(featureValueDiffs[k]);
|
|
update.MultiplyEquals(alpha);
|
|
|
|
// sum updates
|
|
summedUpdate.PlusEquals(update);
|
|
}
|
|
}
|
|
} else {
|
|
cerr << "Rank " << rank << ", epoch " << epoch << ", no constraint violated for this batch" << endl;
|
|
// return 0;
|
|
return 1;
|
|
}
|
|
|
|
// apply learning rate
|
|
if (learning_rate != 1) {
|
|
cerr << "Rank " << rank << ", epoch " << epoch << ", apply learning rate " << learning_rate << " to update." << endl;
|
|
summedUpdate.MultiplyEquals(learning_rate);
|
|
}
|
|
|
|
// scale update by BLEU of oracle (for batch size 1 only)
|
|
if (featureValuesHope.size() == 1) {
|
|
if (m_scale_update) {
|
|
cerr << "Rank " << rank << ", epoch " << epoch << ", scaling summed update with oracle bleu score " << bleuScoresHope[0][0] << endl;
|
|
summedUpdate.MultiplyEquals(bleuScoresHope[0][0]);
|
|
}
|
|
}
|
|
|
|
//cerr << "Rank " << rank << ", epoch " << epoch << ", update: " << summedUpdate << endl;
|
|
weightUpdate.PlusEquals(summedUpdate);
|
|
|
|
// Sanity check: are there still violated constraints after optimisation?
|
|
/* int violatedConstraintsAfter = 0;
|
|
float newDistanceFromOptimum = 0;
|
|
for (size_t i = 0; i < featureValueDiffs.size(); ++i) {
|
|
float modelScoreDiff = featureValueDiffs[i].InnerProduct(currWeights);
|
|
float loss = all_losses[i];
|
|
float diff = loss - modelScoreDiff;
|
|
if (diff > epsilon) {
|
|
++violatedConstraintsAfter;
|
|
newDistanceFromOptimum += diff;
|
|
}
|
|
}
|
|
VERBOSE(1, "Rank " << rank << ", epoch " << epoch << ", violated constraint before: " << violatedConstraintsBefore << ", after: " << violatedConstraintsAfter << ", change: " << violatedConstraintsBefore - violatedConstraintsAfter << endl);
|
|
VERBOSE(1, "Rank " << rank << ", epoch " << epoch << ", error before: " << oldDistanceFromOptimum << ", after: " << newDistanceFromOptimum << ", change: " << oldDistanceFromOptimum - newDistanceFromOptimum << endl);*/
|
|
// return violatedConstraintsAfter;
|
|
return 0;
|
|
}
|
|
|
|
size_t MiraOptimiser::updateWeightsAnalytically(
|
|
ScoreComponentCollection& weightUpdate,
|
|
ScoreComponentCollection& featureValuesHope,
|
|
ScoreComponentCollection& featureValuesFear,
|
|
float bleuScoreHope,
|
|
float bleuScoreFear,
|
|
float modelScoreHope,
|
|
float modelScoreFear,
|
|
float learning_rate,
|
|
size_t rank,
|
|
size_t epoch)
|
|
{
|
|
|
|
float epsilon = 0.0001;
|
|
float oldDistanceFromOptimum = 0;
|
|
bool constraintViolatedBefore = false;
|
|
|
|
// cerr << "Rank " << rank << ", epoch " << epoch << ", hope: " << featureValuesHope << endl;
|
|
// cerr << "Rank " << rank << ", epoch " << epoch << ", fear: " << featureValuesFear << endl;
|
|
ScoreComponentCollection featureValueDiff = featureValuesHope;
|
|
featureValueDiff.MinusEquals(featureValuesFear);
|
|
if (featureValueDiff.GetL1Norm() == 0) {
|
|
cerr << "Rank " << rank << ", epoch " << epoch << ", features equal --> skip" << endl;
|
|
return 1;
|
|
}
|
|
|
|
// cerr << "Rank " << rank << ", epoch " << epoch << ", hope - fear: " << featureValueDiff << endl;
|
|
// float modelScoreDiff = featureValueDiff.InnerProduct(currWeights);
|
|
float modelScoreDiff = modelScoreHope - modelScoreFear;
|
|
float loss = bleuScoreHope - bleuScoreFear;
|
|
float diff = 0;
|
|
if (loss > modelScoreDiff)
|
|
diff = loss - modelScoreDiff;
|
|
cerr << "Rank " << rank << ", epoch " << epoch << ", constraint: " << modelScoreDiff << " >= " << loss << " (current violation: " << diff << ")" << endl;
|
|
|
|
if (m_normaliseMargin) {
|
|
modelScoreDiff = (2*m_sigmoidParam/(1 + exp(-modelScoreDiff))) - m_sigmoidParam;
|
|
loss = (2*m_sigmoidParam/(1 + exp(-loss))) - m_sigmoidParam;
|
|
if (loss > modelScoreDiff)
|
|
diff = loss - modelScoreDiff;
|
|
cerr << "Rank " << rank << ", epoch " << epoch << ", normalised constraint: " << modelScoreDiff << " >= " << loss << " (current violation: " << diff << ")" << endl;
|
|
}
|
|
|
|
if (diff > epsilon) {
|
|
// squash it between 0 and 1
|
|
//diff = tanh(diff);
|
|
//diff = (2/(1 + pow(2,-diff))) - 1;
|
|
/* if (m_normaliseMargin) {
|
|
diff = (2/(1 + exp(-diff))) - 1;
|
|
cerr << "Rank " << rank << ", epoch " << epoch << ", new margin: " << diff << endl;
|
|
}*/
|
|
|
|
// constraint violated
|
|
oldDistanceFromOptimum += diff;
|
|
constraintViolatedBefore = true;
|
|
|
|
// compute alpha for given constraint: (loss - model score diff) / || feature value diff ||^2
|
|
// featureValueDiff.GetL2Norm() * featureValueDiff.GetL2Norm() == featureValueDiff.InnerProduct(featureValueDiff)
|
|
// from Crammer&Singer 2006: alpha = min {C , l_t/ ||x||^2}
|
|
float squaredNorm = featureValueDiff.GetL2Norm() * featureValueDiff.GetL2Norm();
|
|
|
|
float alpha = diff / squaredNorm;
|
|
cerr << "Rank " << rank << ", epoch " << epoch << ", unclipped alpha: " << alpha << endl;
|
|
if (m_slack > 0 ) {
|
|
if (alpha > m_slack) {
|
|
alpha = m_slack;
|
|
} else if (alpha < m_slack*(-1)) {
|
|
alpha = m_slack*(-1);
|
|
}
|
|
}
|
|
|
|
// apply learning rate
|
|
if (learning_rate != 1)
|
|
alpha = alpha * learning_rate;
|
|
|
|
if (m_scale_update) {
|
|
cerr << "Rank " << rank << ", epoch " << epoch << ", scaling update with oracle bleu score " << bleuScoreHope << endl;
|
|
alpha *= bleuScoreHope;
|
|
}
|
|
|
|
cerr << "Rank " << rank << ", epoch " << epoch << ", clipped/scaled alpha: " << alpha << endl;
|
|
|
|
// apply boosting factor
|
|
if (m_boost && modelScoreDiff <= 0) {
|
|
// factor between 1.5 and 3 (for Bleu scores between 5 and 20, the factor is within the boundaries)
|
|
float factor = min(1.5, log2(bleuScoreHope));
|
|
factor = min(3.0f, factor);
|
|
alpha = alpha * factor;
|
|
cerr << "Rank " << rank << ", epoch " << epoch << ", boosted alpha: " << alpha << endl;
|
|
}
|
|
|
|
featureValueDiff.MultiplyEquals(alpha);
|
|
weightUpdate.PlusEquals(featureValueDiff);
|
|
// cerr << "Rank " << rank << ", epoch " << epoch << ", update: " << weightUpdate << endl;
|
|
}
|
|
|
|
if (!constraintViolatedBefore) {
|
|
// constraint satisfied, nothing to do
|
|
cerr << "Rank " << rank << ", epoch " << epoch << ", constraint already satisfied" << endl;
|
|
return 1;
|
|
}
|
|
|
|
// sanity check: constraint still violated after optimisation?
|
|
/* ScoreComponentCollection newWeights(currWeights);
|
|
newWeights.PlusEquals(weightUpdate);
|
|
bool constraintViolatedAfter = false;
|
|
float newDistanceFromOptimum = 0;
|
|
featureValueDiff = featureValuesHope;
|
|
featureValueDiff.MinusEquals(featureValuesFear);
|
|
modelScoreDiff = featureValueDiff.InnerProduct(newWeights);
|
|
diff = loss - modelScoreDiff;
|
|
// approximate comparison between floats!
|
|
if (diff > epsilon) {
|
|
constraintViolatedAfter = true;
|
|
newDistanceFromOptimum += (loss - modelScoreDiff);
|
|
}
|
|
|
|
float hopeScore = featureValuesHope.InnerProduct(newWeights);
|
|
float fearScore = featureValuesFear.InnerProduct(newWeights);
|
|
cerr << "New hope score: " << hopeScore << endl;
|
|
cerr << "New fear score: " << fearScore << endl;
|
|
|
|
VERBOSE(0, "Rank " << rank << ", epoch " << epoch << ", check, constraint violated before? " << constraintViolatedBefore << ", after? " << constraintViolatedAfter << endl);
|
|
VERBOSE(0, "Rank " << rank << ", epoch " << epoch << ", check, error before: " << oldDistanceFromOptimum << ", after: " << newDistanceFromOptimum << ", change: " << oldDistanceFromOptimum - newDistanceFromOptimum << endl);
|
|
*/
|
|
return 0;
|
|
}
|
|
|
|
}
|
|
|