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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://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.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.models.glove;
import lombok.NonNull;
import org.deeplearning4j.models.glove.count.*;
import org.deeplearning4j.models.sequencevectors.interfaces.SequenceIterator;
import org.deeplearning4j.models.sequencevectors.iterators.FilteredSequenceIterator;
import org.deeplearning4j.models.sequencevectors.iterators.SynchronizedSequenceIterator;
import org.deeplearning4j.models.sequencevectors.sequence.Sequence;
import org.deeplearning4j.models.sequencevectors.sequence.SequenceElement;
import org.deeplearning4j.models.word2vec.wordstore.VocabCache;
import org.deeplearning4j.text.sentenceiterator.BasicLineIterator;
import org.deeplearning4j.text.sentenceiterator.PrefetchingSentenceIterator;
import org.deeplearning4j.text.sentenceiterator.SentenceIterator;
import org.deeplearning4j.text.sentenceiterator.SynchronizedSentenceIterator;
import org.deeplearning4j.util.DL4JFileUtils;
import org.deeplearning4j.util.ThreadUtils;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.primitives.Pair;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.io.File;
import java.io.Serializable;
import java.util.ArrayList;
import java.util.Iterator;
import java.util.List;
import java.util.concurrent.atomic.AtomicBoolean;
import java.util.concurrent.atomic.AtomicLong;
import java.util.concurrent.locks.ReentrantReadWriteLock;
/**
* This class implements building cooccurrence map for abstract training corpus.
* However it's performance rather low, due to exsessive IO that happens in ShadowCopyThread
*
* PLEASE NOTE: Current implementation involves massive IO, and it should be rewritter as soon as ND4j gets sparse arrays support
*
* @author [email protected]
*/
public class AbstractCoOccurrences implements Serializable {
protected boolean symmetric;
protected int windowSize;
protected VocabCache vocabCache;
protected SequenceIterator sequenceIterator;
// please note, we need enough room for ShadowCopy thread, that's why -1 there
protected int workers = Math.max(Runtime.getRuntime().availableProcessors() - 1, 1);
// target file, where text with cooccurrencies should be saved
protected File targetFile;
protected ReentrantReadWriteLock lock = new ReentrantReadWriteLock();
protected long memory_threshold = 0;
private ShadowCopyThread shadowThread;
// private Counter sentenceOccurrences = Util.parallelCounter();
//private CounterMap coOccurrenceCounts = Util.parallelCounterMap();
private volatile CountMap coOccurrenceCounts = new CountMap<>();
//private Counter occurrenceAllocations = Util.parallelCounter();
//private List> coOccurrences;
private AtomicLong processedSequences = new AtomicLong(0);
protected static final Logger logger = LoggerFactory.getLogger(AbstractCoOccurrences.class);
// this method should be private, to avoid non-configured instantiation
private AbstractCoOccurrences() {}
/**
* This method returns cooccurrence distance weights for two SequenceElements
*
* @param element1
* @param element2
* @return distance weight
*/
public double getCoOccurrenceCount(@NonNull T element1, @NonNull T element2) {
return coOccurrenceCounts.getCount(element1, element2);
}
/**
* This method returns estimated memory footrpint, based on current CountMap content
* @return
*/
protected long getMemoryFootprint() {
// TODO: implement this method. It should return approx. memory used by appropriate CountMap
try {
lock.readLock().lock();
return ((long) coOccurrenceCounts.size()) * 24L * 5L;
} finally {
lock.readLock().unlock();
}
}
/**
* This memory returns memory threshold, defined as 1/2 of memory allowed for allocation
* @return
*/
protected long getMemoryThreshold() {
return memory_threshold / 2L;
}
public void fit() {
shadowThread = new ShadowCopyThread();
shadowThread.start();
// we should reset iterator before counting cooccurrences
sequenceIterator.reset();
List threads = new ArrayList<>();
for (int x = 0; x < workers; x++) {
threads.add(x, new CoOccurrencesCalculatorThread(x, new FilteredSequenceIterator<>(
new SynchronizedSequenceIterator<>(sequenceIterator), vocabCache), processedSequences));
threads.get(x).start();
}
for (int x = 0; x < workers; x++) {
try {
threads.get(x).join();
} catch (Exception e) {
throw new RuntimeException(e);
}
}
shadowThread.finish();
logger.info("CoOccurrences map was built.");
}
/**
*
* This method returns iterator with elements pairs and their weights. Resulting iterator is safe to use in multi-threaded environment.
*
* Developer's note: thread safety on received iterator is delegated to PrefetchedSentenceIterator
* @return
*/
public Iterator, Double>> iterator() {
final SentenceIterator iterator;
try {
iterator = new SynchronizedSentenceIterator(
new PrefetchingSentenceIterator.Builder(new BasicLineIterator(targetFile))
.setFetchSize(500000).build());
} catch (Exception e) {
logger.error("Target file was not found on last stage!");
throw new RuntimeException(e);
}
return new Iterator, Double>>() {
/*
iterator should be built on top of current text file with all pairs
*/
@Override
public boolean hasNext() {
return iterator.hasNext();
}
@Override
public Pair, Double> next() {
String line = iterator.nextSentence();
String[] strings = line.split(" ");
T element1 = vocabCache.elementAtIndex(Integer.valueOf(strings[0]));
T element2 = vocabCache.elementAtIndex(Integer.valueOf(strings[1]));
Double weight = Double.valueOf(strings[2]);
return new Pair<>(new Pair<>(element1, element2), weight);
}
@Override
public void remove() {
throw new UnsupportedOperationException("remove() method can't be supported on read-only interface");
}
};
}
public static class Builder {
protected boolean symmetric;
protected int windowSize = 5;
protected VocabCache vocabCache;
protected SequenceIterator sequenceIterator;
protected int workers = Runtime.getRuntime().availableProcessors();
protected File target;
protected long maxmemory = Runtime.getRuntime().maxMemory();
public Builder() {
}
public Builder symmetric(boolean reallySymmetric) {
this.symmetric = reallySymmetric;
return this;
}
public Builder windowSize(int windowSize) {
this.windowSize = windowSize;
return this;
}
public Builder vocabCache(@NonNull VocabCache cache) {
this.vocabCache = cache;
return this;
}
public Builder iterate(@NonNull SequenceIterator iterator) {
this.sequenceIterator = new SynchronizedSequenceIterator<>(iterator);
return this;
}
public Builder workers(int numWorkers) {
this.workers = numWorkers;
return this;
}
/**
* This method allows you to specify maximum memory available for CoOccurrence map builder.
*
* Please note: this option can be considered a debugging method. In most cases setting proper -Xmx argument set to JVM is enough to limit this algorithm.
* Please note: this option won't override -Xmx JVM value.
*
* @param gbytes memory available, in GigaBytes
* @return
*/
public Builder maxMemory(int gbytes) {
if (gbytes > 0) {
this.maxmemory = Math.max(gbytes - 1, 1) * 1024 * 1024 * 1024L;
}
return this;
}
/**
* Path to save cooccurrence map after construction.
* If targetFile is not specified, temporary file will be used.
*
* @param path
* @return
*/
public Builder targetFile(@NonNull String path) {
this.targetFile(new File(path));
return this;
}
/**
* Path to save cooccurrence map after construction.
* If targetFile is not specified, temporary file will be used.
*
* @param file
* @return
*/
public Builder targetFile(@NonNull File file) {
this.target = file;
return this;
}
public AbstractCoOccurrences build() {
AbstractCoOccurrences ret = new AbstractCoOccurrences<>();
ret.sequenceIterator = this.sequenceIterator;
ret.windowSize = this.windowSize;
ret.vocabCache = this.vocabCache;
ret.symmetric = this.symmetric;
ret.workers = this.workers;
if (this.maxmemory < 1) {
this.maxmemory = Runtime.getRuntime().maxMemory();
}
ret.memory_threshold = this.maxmemory;
logger.info("Actual memory limit: [" + this.maxmemory + "]");
// use temp file, if no target file was specified
try {
if (this.target == null) {
this.target = DL4JFileUtils.createTempFile("cooccurrence", "map");
}
this.target.deleteOnExit();
} catch (Exception e) {
throw new RuntimeException(e);
}
ret.targetFile = this.target;
return ret;
}
}
private class CoOccurrencesCalculatorThread extends Thread implements Runnable {
private final SequenceIterator iterator;
private final AtomicLong sequenceCounter;
private int threadId;
public CoOccurrencesCalculatorThread(int threadId, @NonNull SequenceIterator iterator,
@NonNull AtomicLong sequenceCounter) {
this.iterator = iterator;
this.sequenceCounter = sequenceCounter;
this.threadId = threadId;
this.setName("CoOccurrencesCalculatorThread " + threadId);
}
@Override
public void run() {
while (iterator.hasMoreSequences()) {
Sequence sequence = iterator.nextSequence();
List tokens = new ArrayList<>(sequence.asLabels());
// logger.info("Tokens size: " + tokens.size());
for (int x = 0; x < sequence.getElements().size(); x++) {
int wordIdx = vocabCache.indexOf(tokens.get(x));
if (wordIdx < 0) {
continue;
}
String w1 = vocabCache.wordFor(tokens.get(x)).getLabel();
// THIS iS SAFE TO REMOVE, NO CHANCE WE'll HAVE UNK WORD INSIDE SEQUENCE
/*if(w1.equals(Glove.UNK))
continue;
*/
int windowStop = Math.min(x + windowSize + 1, tokens.size());
for (int j = x; j < windowStop; j++) {
int otherWord = vocabCache.indexOf(tokens.get(j));
if (otherWord < 0) {
continue;
}
String w2 = vocabCache.wordFor(tokens.get(j)).getLabel();
if (w2.equals(Glove.DEFAULT_UNK) || otherWord == wordIdx) {
continue;
}
T tokenX = vocabCache.wordFor(tokens.get(x));
T tokenJ = vocabCache.wordFor(tokens.get(j));
double nWeight = 1.0 / (j - x + Nd4j.EPS_THRESHOLD);
while (getMemoryFootprint() >= getMemoryThreshold()) {
shadowThread.invoke();
/*lock.readLock().lock();
int size = coOccurrenceCounts.size();
lock.readLock().unlock();
*/
if (threadId == 0) {
logger.debug("Memory consuimption > threshold: {footrpint: [" + getMemoryFootprint()
+ "], threshold: [" + getMemoryThreshold() + "] }");
}
ThreadUtils.uncheckedSleep(10000);
}
/*
if (getMemoryFootprint() == 0) {
logger.info("Zero size!");
}
*/
try {
lock.readLock().lock();
if (wordIdx < otherWord) {
coOccurrenceCounts.incrementCount(tokenX, tokenJ, nWeight);
if (symmetric) {
coOccurrenceCounts.incrementCount(tokenJ, tokenX, nWeight);
}
} else {
coOccurrenceCounts.incrementCount(tokenJ, tokenX, nWeight);
if (symmetric) {
coOccurrenceCounts.incrementCount(tokenX, tokenJ, nWeight);
}
}
} finally {
lock.readLock().unlock();
}
}
}
sequenceCounter.incrementAndGet();
}
}
}
/**
* This class is designed to provide shadow copy functionality for CoOccurence maps, since with proper corpus size you can't fit such a map into memory
*
*/
private class ShadowCopyThread extends Thread implements Runnable {
private AtomicBoolean isFinished = new AtomicBoolean(false);
private AtomicBoolean isTerminate = new AtomicBoolean(false);
private AtomicBoolean isInvoked = new AtomicBoolean(false);
private AtomicBoolean shouldInvoke = new AtomicBoolean(false);
// file that contains resuts from previous runs
private File[] tempFiles;
private RoundCount counter;
public ShadowCopyThread() {
try {
counter = new RoundCount(1);
tempFiles = new File[2];
tempFiles[0] = DL4JFileUtils.createTempFile("aco", "tmp");
tempFiles[1] = DL4JFileUtils.createTempFile("aco", "tmp");
tempFiles[0].deleteOnExit();
tempFiles[1].deleteOnExit();
} catch (Exception e) {
throw new RuntimeException(e);
}
this.setName("ACO ShadowCopy thread");
}
@Override
public void run() {
/*
Basic idea is pretty simple: run quetly, untill memory gets filled up to some high volume.
As soon as this happens - execute shadow copy.
*/
while (!isFinished.get() && !isTerminate.get()) {
// check used memory. if memory use below threshold - sleep for a while. if above threshold - invoke copier
if (getMemoryFootprint() > getMemoryThreshold() || (shouldInvoke.get() && !isInvoked.get())) {
// we'll just invoke copier, nothing else
shouldInvoke.compareAndSet(true, false);
invokeBlocking();
} else {
/*
commented and left here for future debugging purposes, if needed
//lock.readLock().lock();
//int size = coOccurrenceCounts.size();
//lock.readLock().unlock();
//logger.info("Current memory situation: {size: [" +size+ "], footprint: [" + getMemoryFootprint()+"], threshold: ["+ getMemoryThreshold() +"]}");
*/
ThreadUtils.uncheckedSleep(1000);
}
}
}
/**
* This methods advises shadow copy process to start
*/
public void invoke() {
shouldInvoke.compareAndSet(false, true);
}
/**
* This methods dumps cooccurrence map into save file.
* Please note: this method is synchronized and will block, until complete
*/
public synchronized void invokeBlocking() {
if (getMemoryFootprint() < getMemoryThreshold() && !isFinished.get()) {
return;
}
int numberOfLinesSaved = 0;
isInvoked.set(true);
logger.debug("Memory purge started.");
/*
Basic plan:
1. Open temp file
2. Read that file line by line
3. For each read line do synchronization in memory > new file direction
*/
counter.tick();
CountMap localMap;
try {
// in any given moment there's going to be only 1 WriteLock, due to invokeBlocking() being synchronized call
lock.writeLock().lock();
// obtain local copy of CountMap
localMap = coOccurrenceCounts;
// set new CountMap, and release write lock
coOccurrenceCounts = new CountMap<>();
} finally {
lock.writeLock().unlock();
}
try {
File file = null;
if (!isFinished.get()) {
file = tempFiles[counter.previous()];
} else
file = targetFile;
// PrintWriter pw = new PrintWriter(file);
int linesRead = 0;
logger.debug("Saving to: [" + counter.get() + "], Reading from: [" + counter.previous() + "]");
CoOccurenceReader reader =
new BinaryCoOccurrenceReader<>(tempFiles[counter.previous()], vocabCache, localMap);
CoOccurrenceWriter writer = (isFinished.get()) ? new ASCIICoOccurrenceWriter(targetFile)
: new BinaryCoOccurrenceWriter(tempFiles[counter.get()]);
while (reader.hasMoreObjects()) {
CoOccurrenceWeight line = reader.nextObject();
if (line != null) {
writer.writeObject(line);
numberOfLinesSaved++;
linesRead++;
}
}
reader.finish();
logger.debug("Lines read: [" + linesRead + "]");
//now, we can dump the rest of elements, which were not presented in existing dump
Iterator> iterator = localMap.getPairIterator();
while (iterator.hasNext()) {
Pair pair = iterator.next();
double mWeight = localMap.getCount(pair);
CoOccurrenceWeight object = new CoOccurrenceWeight<>();
object.setElement1(pair.getFirst());
object.setElement2(pair.getSecond());
object.setWeight(mWeight);
writer.writeObject(object);
numberOfLinesSaved++;
// if (numberOfLinesSaved % 100000 == 0) logger.info("Lines saved: [" + numberOfLinesSaved +"]");
}
writer.finish();
/*
SentenceIterator sIterator = new PrefetchingSentenceIterator.Builder(new BasicLineIterator(tempFiles[counter.get()]))
.setFetchSize(500000)
.build();
int linesRead = 0;
while (sIterator.hasNext()) {
//List list = new ArrayList<>(reader.next());
String sentence = sIterator.nextSentence();
if (sentence == null || sentence.isEmpty()) continue;
String[] strings = sentence.split(" ");
// first two elements are integers - vocab indexes
//T element1 = vocabCache.wordFor(vocabCache.wordAtIndex(list.get(0).toInt()));
//T element2 = vocabCache.wordFor(vocabCache.wordAtIndex(list.get(1).toInt()));
T element1 = vocabCache.elementAtIndex(Integer.valueOf(strings[0]));
T element2 = vocabCache.elementAtIndex(Integer.valueOf(strings[1]));
// getting third element, previously stored weight
double sWeight = Double.valueOf(strings[2]); // list.get(2).toDouble();
// now, since we have both elements ready, we can check this pair against inmemory map
double mWeight = localMap.getCount(element1, element2);
if (mWeight <= 0) {
// this means we have no such pair in memory, so we'll do nothing to sWeight
} else {
// since we have new weight value in memory, we should update sWeight value before moving it off memory
sWeight += mWeight;
// original pair can be safely removed from CountMap
localMap.removePair(element1,element2);
}
StringBuilder builder = new StringBuilder().append(element1.getIndex()).append(" ").append(element2.getIndex()).append(" ").append(sWeight);
pw.println(builder.toString());
numberOfLinesSaved++;
linesRead++;
// if (numberOfLinesSaved % 100000 == 0) logger.info("Lines saved: [" + numberOfLinesSaved +"]");
// if (linesRead % 100000 == 0) logger.info("Lines read: [" + linesRead +"]");
}
*/
/*
logger.info("Lines read: [" + linesRead + "]");
//now, we can dump the rest of elements, which were not presented in existing dump
Iterator> iterator = localMap.getPairIterator();
while (iterator.hasNext()) {
Pair pair = iterator.next();
double mWeight = localMap.getCount(pair);
StringBuilder builder = new StringBuilder().append(pair.getFirst().getIndex()).append(" ").append(pair.getFirst().getIndex()).append(" ").append(mWeight);
pw.println(builder.toString());
numberOfLinesSaved++;
// if (numberOfLinesSaved % 100000 == 0) logger.info("Lines saved: [" + numberOfLinesSaved +"]");
}
pw.flush();
pw.close();
*/
// just a hint for gc
localMap = null;
//sIterator.finish();
} catch (Exception e) {
throw new RuntimeException(e);
}
logger.info("Number of word pairs saved so far: [" + numberOfLinesSaved + "]");
isInvoked.set(false);
}
/**
* This method provides soft finish ability for shadow copy process.
* Please note: it's blocking call, since it requires for final merge.
*/
public void finish() {
if (this.isFinished.get()) {
return;
}
this.isFinished.set(true);
invokeBlocking();
}
/**
* This method provides hard fiinish ability for shadow copy process
*/
public void terminate() {
this.isTerminate.set(true);
}
}
}
