/**
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.mahout.clustering.lda.cvb;
import com.google.common.collect.Lists;
import com.google.common.collect.Maps;
import org.apache.hadoop.fs.Path;
import org.apache.mahout.math.Matrix;
import org.apache.mahout.math.MatrixSlice;
import org.apache.mahout.math.SparseRowMatrix;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.VectorIterable;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.io.IOException;
import java.util.Arrays;
import java.util.Iterator;
import java.util.List;
import java.util.Map;
import java.util.concurrent.ArrayBlockingQueue;
import java.util.concurrent.BlockingQueue;
import java.util.concurrent.Callable;
import java.util.concurrent.ThreadPoolExecutor;
import java.util.concurrent.TimeUnit;
/**
* Multithreaded LDA model trainer class, which primarily operates by running a "map/reduce"
* operation, all in memory locally (ie not a hadoop job!) : the "map" operation is to take
* the "read-only" {@link TopicModel} and use it to iteratively learn the p(topic|term, doc)
* distribution for documents (this can be done in parallel across many documents, as the
* "read-only" model is, well, read-only. Then the outputs of this are "reduced" onto the
* "write" model, and these updates are not parallelizable in the same way: individual
* documents can't be added to the same entries in different threads at the same time, but
* updates across many topics to the same term from the same document can be done in parallel,
* so they are.
*
* Because computation is done asynchronously, when iteration is done, it's important to call
* the stop() method, which blocks until work is complete.
*
* Setting the read model and the write model to be the same object may not quite work yet,
* on account of parallelism badness.
*/
public class ModelTrainer {
private static final Logger log = LoggerFactory.getLogger(ModelTrainer.class);
private final int numTopics;
private final int numTerms;
private TopicModel readModel;
private TopicModel writeModel;
private ThreadPoolExecutor threadPool;
private BlockingQueue<Runnable> workQueue;
private final int numTrainThreads;
private final boolean isReadWrite;
public ModelTrainer(TopicModel initialReadModel, TopicModel initialWriteModel,
int numTrainThreads, int numTopics, int numTerms) {
this.readModel = initialReadModel;
this.writeModel = initialWriteModel;
this.numTrainThreads = numTrainThreads;
this.numTopics = numTopics;
this.numTerms = numTerms;
isReadWrite = initialReadModel == initialWriteModel;
}
/**
* WARNING: this constructor may not lead to good behavior. What should be verified is that
* the model updating process does not conflict with model reading. It might work, but then
* again, it might not!
* @param model to be used for both reading (inference) and accumulating (learning)
* @param numTrainThreads
* @param numTopics
* @param numTerms
*/
public ModelTrainer(TopicModel model, int numTrainThreads, int numTopics, int numTerms) {
this(model, model, numTrainThreads, numTopics, numTerms);
}
public TopicModel getReadModel() {
return readModel;
}
public void start() {
log.info("Starting training threadpool with " + numTrainThreads + " threads");
workQueue = new ArrayBlockingQueue<Runnable>(numTrainThreads * 10);
threadPool = new ThreadPoolExecutor(numTrainThreads, numTrainThreads, 0, TimeUnit.SECONDS,
workQueue);
threadPool.allowCoreThreadTimeOut(false);
threadPool.prestartAllCoreThreads();
}
public void train(VectorIterable matrix, VectorIterable docTopicCounts) {
train(matrix, docTopicCounts, 1);
}
public double calculatePerplexity(VectorIterable matrix, VectorIterable docTopicCounts) {
return calculatePerplexity(matrix, docTopicCounts, 0);
}
public double calculatePerplexity(VectorIterable matrix, VectorIterable docTopicCounts,
double testFraction) {
Iterator<MatrixSlice> docIterator = matrix.iterator();
Iterator<MatrixSlice> docTopicIterator = docTopicCounts.iterator();
double perplexity = 0;
double matrixNorm = 0;
while(docIterator.hasNext() && docTopicIterator.hasNext()) {
MatrixSlice docSlice = docIterator.next();
MatrixSlice topicSlice = docTopicIterator.next();
int docId = docSlice.index();
Vector document = docSlice.vector();
Vector topicDist = topicSlice.vector();
if(testFraction == 0 || docId % (1/testFraction) == 0) {
trainSync(document, topicDist, false, 10);
perplexity += readModel.perplexity(document, topicDist);
matrixNorm += document.norm(1);
}
}
return perplexity / matrixNorm;
}
public void train(VectorIterable matrix, VectorIterable docTopicCounts, int numDocTopicIters) {
start();
Iterator<MatrixSlice> docIterator = matrix.iterator();
Iterator<MatrixSlice> docTopicIterator = docTopicCounts.iterator();
long startTime = System.nanoTime();
int i = 0;
double[] times = new double[100];
Map<Vector, Vector> batch = Maps.newHashMap();
int numTokensInBatch = 0;
long batchStart = System.nanoTime();
while(docIterator.hasNext() && docTopicIterator.hasNext()) {
i++;
Vector document = docIterator.next().vector();
Vector topicDist = docTopicIterator.next().vector();
if(isReadWrite) {
if(batch.size() < numTrainThreads) {
batch.put(document, topicDist);
if(log.isDebugEnabled()) {
numTokensInBatch += document.getNumNondefaultElements();
}
} else {
batchTrain(batch, true, numDocTopicIters);
long time = System.nanoTime();
log.debug("trained {} docs with {} tokens, start time {}, end time {}",
new Object[] {numTrainThreads, numTokensInBatch, batchStart, time});
batchStart = time;
numTokensInBatch = 0;
}
} else {
long start = System.nanoTime();
train(document, topicDist, true, numDocTopicIters);
if(log.isDebugEnabled()) {
times[i % times.length] =
(System.nanoTime() - start) /(1.0e6 * document.getNumNondefaultElements());
if(i % 100 == 0) {
long time = System.nanoTime() - startTime;
log.debug("trained " + i + " documents in " + (time / 1.0e6) + "ms");
if(i % 500 == 0) {
Arrays.sort(times);
log.debug("training took median " + times[times.length / 2] + "ms per token-instance");
}
}
}
}
}
stop();
}
public void batchTrain(Map<Vector, Vector> batch, boolean update, int numDocTopicsIters) {
while(true) {
try {
List<TrainerRunnable> runnables = Lists.newArrayList();
for(Map.Entry<Vector, Vector> entry : batch.entrySet()) {
runnables.add(new TrainerRunnable(readModel, null, entry.getKey(),
entry.getValue(), new SparseRowMatrix(numTopics, numTerms, true),
numDocTopicsIters));
}
threadPool.invokeAll(runnables);
if(update) {
for(TrainerRunnable runnable : runnables) {
writeModel.update(runnable.docTopicModel);
}
}
break;
} catch (InterruptedException e) {
log.warn("Interrupted during batch training, retrying!", e);
}
}
}
public void train(Vector document, Vector docTopicCounts, boolean update, int numDocTopicIters) {
while(true) {
try {
workQueue.put(new TrainerRunnable(readModel,
update ? writeModel : null, document, docTopicCounts, new SparseRowMatrix(
numTopics, numTerms, true), numDocTopicIters));
return;
} catch (InterruptedException e) {
log.warn("Interrupted waiting to submit document to work queue: " + document, e);
}
}
}
public void trainSync(Vector document, Vector docTopicCounts, boolean update,
int numDocTopicIters) {
new TrainerRunnable(readModel,
update ? writeModel : null, document, docTopicCounts, new SparseRowMatrix(
numTopics, numTerms, true), numDocTopicIters).run();
}
public double calculatePerplexity(Vector document, Vector docTopicCounts, int numDocTopicIters) {
TrainerRunnable runner = new TrainerRunnable(readModel,
null, document, docTopicCounts, new SparseRowMatrix(
numTopics, numTerms, true), numDocTopicIters);
return runner.call();
}
public void stop() {
long startTime = System.nanoTime();
log.info("Initiating stopping of training threadpool");
try {
threadPool.shutdown();
if(!threadPool.awaitTermination(60, TimeUnit.SECONDS)) {
log.warn("Threadpool timed out on await termination - jobs still running!");
}
long newTime = System.nanoTime();
log.info("threadpool took: " + (newTime - startTime) / 1.0e6 + "ms");
startTime = newTime;
writeModel.awaitTermination();
newTime = System.nanoTime();
log.info("writeModel.awaitTermination() took " + (newTime - startTime) / 1.0e6 + "ms");
TopicModel tmpModel = writeModel;
writeModel = readModel;
readModel = tmpModel;
writeModel.reset();
} catch (InterruptedException e) {
log.error("Interrupted shutting down!", e);
}
}
public void persist(Path outputPath) throws IOException {
readModel.persist(outputPath, true);
}
private static class TrainerRunnable implements Runnable, Callable<Double> {
private final TopicModel readModel;
private final TopicModel writeModel;
private final Vector document;
private final Vector docTopics;
private final Matrix docTopicModel;
private final int numDocTopicIters;
private TrainerRunnable(TopicModel readModel, TopicModel writeModel, Vector document,
Vector docTopics, Matrix docTopicModel, int numDocTopicIters) {
this.readModel = readModel;
this.writeModel = writeModel;
this.document = document;
this.docTopics = docTopics;
this.docTopicModel = docTopicModel;
this.numDocTopicIters = numDocTopicIters;
}
@Override
public void run() {
for(int i = 0; i < numDocTopicIters; i++) {
// synchronous read-only call:
readModel.trainDocTopicModel(document, docTopics, docTopicModel);
}
if(writeModel != null) {
// parallel call which is read-only on the docTopicModel, and write-only on the writeModel
// this method does not return until all rows of the docTopicModel have been submitted
// to write work queues
writeModel.update(docTopicModel);
}
}
@Override
public Double call() {
run();
return readModel.perplexity(document, docTopics);
}
}
}