Made PhraseObjective thread safe

git-svn-id: https://ws10smt.googlecode.com/svn/trunk@248 ec762483-ff6d-05da-a07a-a48fb63a330f

4 files changed, 48 insertions, 28 deletions

diff --git a/gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java b/gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java
index 1f73764e..a369b319 100644
--- a/gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java
+++ b/gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java
@@ -2,8 +2,6 @@ package phrase;
 

 import gnu.trove.TIntArrayList;

 import org.apache.commons.math.special.Gamma;

-import io.FileUtil;

-import java.io.IOException;

 import java.io.PrintStream;

 import java.util.Arrays;

 import java.util.List;

@@ -11,9 +9,10 @@ import java.util.concurrent.ExecutorService;
 import java.util.concurrent.Executors;

 import java.util.concurrent.LinkedBlockingQueue;

 import java.util.concurrent.atomic.AtomicInteger;

+import java.util.concurrent.atomic.AtomicLong;

 

 import phrase.Corpus.Edge;

-import util.MathUtil;

+

 

 public class PhraseCluster {

 	

@@ -21,7 +20,11 @@ public class PhraseCluster {
 	private int n_phrases, n_words, n_contexts, n_positions;

 	public Corpus c;

 	public ExecutorService pool; 

-	

+

+	double[] lambdaPTCT;

+	double[][] lambdaPT;

+	boolean cacheLambda = true;

+

 	// emit[tag][position][word] = p(word | tag, position in context)

 	double emit[][][];

 	// pi[phrase][tag] = p(tag | phrase)

@@ -232,14 +235,19 @@ public class PhraseCluster {
 	{

 		double [][][]exp_emit=new double[K][n_positions][n_words];

 		double [][]exp_pi=new double[n_phrases][K];

+

+		if (lambdaPT == null && cacheLambda)

+			lambdaPT = new double[n_phrases][];

 		

 		double loglikelihood=0, kl=0, l1lmax=0, primal=0;

 		int failures=0, iterations=0;

+		long start = System.currentTimeMillis();

 		//E

 		for(int phrase=0; phrase<n_phrases; phrase++){

-			PhraseObjective po=new PhraseObjective(this, phrase, scalePT);

+			PhraseObjective po = new PhraseObjective(this, phrase, scalePT, (cacheLambda) ? lambdaPT[phrase] : null);

 			boolean ok = po.optimizeWithProjectedGradientDescent();

 			if (!ok) ++failures;

+			if (cacheLambda) lambdaPT[phrase] = po.getParameters();

 			iterations += po.getNumberUpdateCalls();

 			double [][] q=po.posterior();

 			loglikelihood += po.loglikelihood();

@@ -263,9 +271,10 @@ public class PhraseCluster {
 			}

 		}

 		

+		long end = System.currentTimeMillis();

 		if (failures > 0)

 			System.out.println("WARNING: failed to converge in " + failures + "/" + n_phrases + " cases");

-		System.out.println("\tmean iters:     " + iterations/(double)n_phrases);

+		System.out.println("\tmean iters:     " + iterations/(double)n_phrases + " elapsed time " + (end - start) / 1000.0);

 		System.out.println("\tllh:            " + loglikelihood);

 		System.out.println("\tKL:             " + kl);

 		System.out.println("\tphrase l1lmax:  " + l1lmax);

@@ -295,7 +304,12 @@ public class PhraseCluster {
 		

 		double loglikelihood=0, kl=0, l1lmax=0, primal=0;

 		final AtomicInteger failures = new AtomicInteger(0);

+		final AtomicLong elapsed = new AtomicLong(0l);

 		int iterations=0;

+		long start = System.currentTimeMillis();

+		

+		if (lambdaPT == null && cacheLambda)

+			lambdaPT = new double[n_phrases][];

 

 		//E

 		for(int phrase=0;phrase<n_phrases;phrase++){

@@ -304,9 +318,13 @@ public class PhraseCluster {
 				public void run() {

 					try {

 						//System.out.println("" + Thread.currentThread().getId() + " optimising lambda for " + p);

-						PhraseObjective po = new PhraseObjective(PhraseCluster.this, p, scalePT);

+						long start = System.currentTimeMillis();

+						PhraseObjective po = new PhraseObjective(PhraseCluster.this, p, scalePT, (cacheLambda) ? lambdaPT[p] : null);

 						boolean ok = po.optimizeWithProjectedGradientDescent();

 						if (!ok) failures.incrementAndGet();

+						long end = System.currentTimeMillis();

+						elapsed.addAndGet(end - start);

+

 						//System.out.println("" + Thread.currentThread().getId() + " done optimising lambda for " + p);

 						expectations.put(po);

 						//System.out.println("" + Thread.currentThread().getId() + " added to queue " + p);

@@ -327,6 +345,7 @@ public class PhraseCluster {
 				PhraseObjective po = expectations.take();

 				// process

 				int phrase = po.phrase;

+				if (cacheLambda) lambdaPT[phrase] = po.getParameters();

 				//System.out.println("" + Thread.currentThread().getId() + " taken phrase " + phrase);

 				double [][] q=po.posterior();

 				loglikelihood += po.loglikelihood();

@@ -335,7 +354,6 @@ public class PhraseCluster {
 				primal += po.primal(scalePT);

 				iterations += po.getNumberUpdateCalls();

 

-				

 				List<Edge> edges = c.getEdgesForPhrase(phrase);

 				for(int edge=0;edge<q.length;edge++){

 					Edge e = edges.get(edge);

@@ -356,9 +374,11 @@ public class PhraseCluster {
 			}

 		}

 		

+		long end = System.currentTimeMillis();

+		

 		if (failures.get() > 0)

 			System.out.println("WARNING: failed to converge in " + failures.get() + "/" + n_phrases + " cases");

-		System.out.println("\tmean iters:     " + iterations/(double)n_phrases);

+		System.out.println("\tmean iters:     " + iterations/(double)n_phrases + " walltime " + (end-start)/1000.0 + " threads " + elapsed.get() / 1000.0);

 		System.out.println("\tllh:            " + loglikelihood);

 		System.out.println("\tKL:             " + kl);

 		System.out.println("\tphrase l1lmax:  " + l1lmax);

@@ -376,16 +396,15 @@ public class PhraseCluster {
 		return primal;

 	}

 	

-	double[] lambda;

-

 	public double PREM_phrase_context_constraints(double scalePT, double scaleCT)

 	{	

 		double[][][] exp_emit = new double [K][n_positions][n_words];

 		double[][] exp_pi = new double[n_phrases][K];

 

 		//E step

-		PhraseContextObjective pco = new PhraseContextObjective(this, lambda, pool, scalePT, scaleCT);

-		lambda = pco.optimizeWithProjectedGradientDescent();

+		PhraseContextObjective pco = new PhraseContextObjective(this, lambdaPTCT, pool, scalePT, scaleCT);

+		boolean ok = pco.optimizeWithProjectedGradientDescent();

+		if (cacheLambda) lambdaPTCT = pco.getParameters();

 

 		//now extract expectations

 		List<Corpus.Edge> edges = c.getEdges();

diff --git a/gi/posterior-regularisation/prjava/src/phrase/PhraseContextObjective.java b/gi/posterior-regularisation/prjava/src/phrase/PhraseContextObjective.java
index 7e6c7f60..06a9f8cb 100644
--- a/gi/posterior-regularisation/prjava/src/phrase/PhraseContextObjective.java
+++ b/gi/posterior-regularisation/prjava/src/phrase/PhraseContextObjective.java
@@ -318,7 +318,7 @@ public class PhraseContextObjective extends ProjectedObjective
 		return q[edgeIndex];

 	}

 	

-	public double[] optimizeWithProjectedGradientDescent()

+	public boolean optimizeWithProjectedGradientDescent()

 	{

 		projectionTime = 0;

 		actualProjectionTime = 0;

@@ -354,7 +354,7 @@ public class PhraseContextObjective extends ProjectedObjective
 		System.out.println(" and " + total + " ms: projection " + projectionTime + 

 				" actual " + actualProjectionTime + " objective " + objectiveTime);

 

-		return parameters;

+		return success;

 	}

 	

 	double loglikelihood()

diff --git a/gi/posterior-regularisation/prjava/src/phrase/PhraseObjective.java b/gi/posterior-regularisation/prjava/src/phrase/PhraseObjective.java
index e62b62f4..7c32d9c0 100644
--- a/gi/posterior-regularisation/prjava/src/phrase/PhraseObjective.java
+++ b/gi/posterior-regularisation/prjava/src/phrase/PhraseObjective.java
@@ -25,7 +25,7 @@ public class PhraseObjective extends ProjectedObjective
 	static int ITERATIONS = 100;

 	//private double c1=0.0001; // wolf stuff

 	//private double c2=0.9;

-	private static double lambda[][];

+	//private static double lambda[][];

 	private PhraseCluster c;

 	

 	/**@brief

@@ -64,23 +64,18 @@ public class PhraseObjective extends ProjectedObjective
 	 */

 	public double llh;

 	

-	public PhraseObjective(PhraseCluster cluster, int phraseIdx, double scale){

+	public PhraseObjective(PhraseCluster cluster, int phraseIdx, double scale, double[] lambda){

 		phrase=phraseIdx;

 		c=cluster;

 		data=c.c.getEdgesForPhrase(phrase);

 		n_param=data.size()*c.K;

 		//System.out.println("Num parameters " + n_param + " for phrase #" + phraseIdx);

 		

-		if (lambda==null){

-			lambda=new double[c.c.getNumPhrases()][];

-		}

-		

-		if (lambda[phrase]==null){

-			lambda[phrase]=new double[n_param];

-		}

+		if (lambda==null) 

+			lambda=new double[n_param];

 		

-		parameters=lambda[phrase];

-		newPoint  = new double[n_param];

+		parameters = lambda;

+		newPoint = new double[n_param];

 		gradient = new double[n_param];

 		initP();

 		projection=new SimplexProjection(scale);

@@ -163,8 +158,12 @@ public class PhraseObjective extends ProjectedObjective
 	public double [][]posterior(){

 		return q;

 	}

-		

+	

+	long optimizationTime;

+	

 	public boolean optimizeWithProjectedGradientDescent(){

+		long start = System.currentTimeMillis();

+		

 		LineSearchMethod ls =

 			new ArmijoLineSearchMinimizationAlongProjectionArc

 				(new InterpolationPickFirstStep(INIT_STEP_SIZE));

@@ -188,7 +187,6 @@ public class PhraseObjective extends ProjectedObjective
 		//}else{

 //			System.out.println("Failed to optimize");

 		//}

-		lambda[phrase]=parameters;

 		//	ps.println(Arrays.toString(parameters));

 		

 		//	for(int edge=0;edge<data.getSize();edge++){

diff --git a/gi/posterior-regularisation/prjava/src/phrase/Trainer.java b/gi/posterior-regularisation/prjava/src/phrase/Trainer.java
index 240c4d64..20f6c905 100644
--- a/gi/posterior-regularisation/prjava/src/phrase/Trainer.java
+++ b/gi/posterior-regularisation/prjava/src/phrase/Trainer.java
@@ -31,6 +31,7 @@ public class Trainer
         parser.accepts("alpha-emit").withRequiredArg().ofType(Double.class).defaultsTo(0.1);
         parser.accepts("alpha-pi").withRequiredArg().ofType(Double.class).defaultsTo(0.01);
         parser.accepts("agree");
+        parser.accepts("no-parameter-cache");
         OptionSet options = parser.parse(args);
 
         if (options.has("help") || !options.has("in"))
@@ -96,6 +97,8 @@ public class Trainer
  			cluster = new PhraseCluster(tags, corpus);
  			if (threads > 0) cluster.useThreadPool(threads);
  			if (vb)	cluster.initialiseVB(alphaEmit, alphaPi);
+ 			if (options.has("no-parameter-cache")) 
+ 				cluster.cacheLambda = false;
  		}
 				
 		double last = 0;