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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.word2vec;
import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.models.embeddings.WeightLookupTable;
import org.deeplearning4j.models.embeddings.reader.ModelUtils;
import org.deeplearning4j.models.embeddings.wordvectors.WordVectors;
import org.deeplearning4j.models.word2vec.wordstore.VocabCache;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.compression.AbstractStorage;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.ops.transforms.Transforms;
import java.util.ArrayList;
import java.util.Collection;
import java.util.List;
import java.util.Map;
import java.util.concurrent.ConcurrentHashMap;
/**
* This is special limited Word2Vec implementation, suited for serving as lookup table in concurrent multi-gpu environment
* This implementation DOES NOT load all vectors onto any of gpus, instead of that it holds vectors in, optionally, compressed state in host memory.
* This implementation DOES NOT provide some of original Word2Vec methods, such as wordsNearest or wordsNearestSum.
*
* @author [email protected]
*/
@Slf4j
public class StaticWord2Vec implements WordVectors {
private List> cacheWrtDevice = new ArrayList<>();
private AbstractStorage storage;
private long cachePerDevice = 0L;
private VocabCache vocabCache;
private String unk = null;
private StaticWord2Vec() {
}
@Override
public String getUNK() {
return unk;
}
@Override
public void setUNK(String newUNK) {
this.unk = newUNK;
}
/**
* Init method validates configuration defined using
*/
protected void init() {
if (storage.size() != vocabCache.numWords())
throw new RuntimeException("Number of words in Vocab isn't matching number of stored Vectors. vocab: ["
+ vocabCache.numWords() + "]; storage: [" + storage.size() + "]");
// initializing device cache
for (int i = 0; i < Nd4j.getAffinityManager().getNumberOfDevices(); i++) {
cacheWrtDevice.add(new ConcurrentHashMap());
}
}
/**
* Returns true if the model has this word in the vocab
*
* @param word the word to test for
* @return true if the model has the word in the vocab
*/
@Override
public boolean hasWord(String word) {
return vocabCache.containsWord(word);
}
@Override
public Collection wordsNearest(INDArray words, int top) {
throw new UnsupportedOperationException("Method isn't implemented. Please use usual Word2Vec implementation");
}
@Override
public Collection wordsNearestSum(INDArray words, int top) {
throw new UnsupportedOperationException("Method isn't implemented. Please use usual Word2Vec implementation");
}
/**
* Get the top n words most similar to the given word
* PLEASE NOTE: This method is not available in this implementation.
*
* @param word the word to compare
* @param n the n to get
* @return the top n words
*/
@Override
public Collection wordsNearestSum(String word, int n) {
throw new UnsupportedOperationException("Method isn't implemented. Please use usual Word2Vec implementation");
}
/**
* Words nearest based on positive and negative words
* PLEASE NOTE: This method is not available in this implementation.
*
* @param positive the positive words
* @param negative the negative words
* @param top the top n words
* @return the words nearest the mean of the words
*/
@Override
public Collection wordsNearestSum(Collection positive, Collection negative, int top) {
throw new UnsupportedOperationException("Method isn't implemented. Please use usual Word2Vec implementation");
}
/**
* Accuracy based on questions which are a space separated list of strings
* where the first word is the query word, the next 2 words are negative,
* and the last word is the predicted word to be nearest
* PLEASE NOTE: This method is not available in this implementation.
*
* @param questions the questions to ask
* @return the accuracy based on these questions
*/
@Override
public Map accuracy(List questions) {
throw new UnsupportedOperationException("Method isn't implemented. Please use usual Word2Vec implementation");
}
@Override
public int indexOf(String word) {
return vocabCache.indexOf(word);
}
/**
* Find all words with a similar characters
* in the vocab
* PLEASE NOTE: This method is not available in this implementation.
*
* @param word the word to compare
* @param accuracy the accuracy: 0 to 1
* @return the list of words that are similar in the vocab
*/
@Override
public List similarWordsInVocabTo(String word, double accuracy) {
throw new UnsupportedOperationException("Method isn't implemented. Please use usual Word2Vec implementation");
}
/**
* Get the word vector for a given matrix
*
* @param word the word to get the matrix for
* @return the ndarray for this word
*/
@Override
public double[] getWordVector(String word) {
return getWordVectorMatrix(word).data().asDouble();
}
/**
* Returns the word vector divided by the norm2 of the array
*
* @param word the word to get the matrix for
* @return the looked up matrix
*/
@Override
public INDArray getWordVectorMatrixNormalized(String word) {
return Transforms.unitVec(getWordVectorMatrix(word));
}
/**
* Get the word vector for a given matrix
*
* @param word the word to get the matrix for
* @return the ndarray for this word
*/
@Override
public INDArray getWordVectorMatrix(String word) {
// TODO: add variable UNK here
int idx = 0;
if (hasWord(word))
idx = vocabCache.indexOf(word);
else if (getUNK() != null)
idx = vocabCache.indexOf(getUNK());
else
return null;
int deviceId = Nd4j.getAffinityManager().getDeviceForCurrentThread();
INDArray array = null;
if (cachePerDevice > 0 && cacheWrtDevice.get(deviceId).containsKey(idx))
return cacheWrtDevice.get(Nd4j.getAffinityManager().getDeviceForCurrentThread()).get(idx);
array = storage.get(idx);
if (cachePerDevice > 0) {
// TODO: add cache here
long arrayBytes = array.length() * array.data().getElementSize();
if ((arrayBytes * cacheWrtDevice.get(deviceId).size()) + arrayBytes < cachePerDevice)
cacheWrtDevice.get(deviceId).put(idx, array);
}
return array;
}
/**
* This method returns 2D array, where each row represents corresponding word/label
*
* @param labels
* @return
*/
@Override
public INDArray getWordVectors(Collection labels) {
List words = new ArrayList<>();
for (String label : labels) {
if (hasWord(label) || getUNK() != null)
words.add(getWordVectorMatrix(label));
}
return Nd4j.vstack(words);
}
/**
* This method returns mean vector, built from words/labels passed in
*
* @param labels
* @return
*/
@Override
public INDArray getWordVectorsMean(Collection labels) {
INDArray matrix = getWordVectors(labels);
// TODO: check this (1)
return matrix.mean(1);
}
/**
* Words nearest based on positive and negative words
* PLEASE NOTE: This method is not available in this implementation.
*
* @param positive the positive words
* @param negative the negative words
* @param top the top n words
* @return the words nearest the mean of the words
*/
@Override
public Collection wordsNearest(Collection positive, Collection negative, int top) {
throw new UnsupportedOperationException("Method isn't implemented. Please use usual Word2Vec implementation");
}
/**
* Get the top n words most similar to the given word
* PLEASE NOTE: This method is not available in this implementation.
*
* @param word the word to compare
* @param n the n to get
* @return the top n words
*/
@Override
public Collection wordsNearest(String word, int n) {
throw new UnsupportedOperationException("Method isn't implemented. Please use usual Word2Vec implementation");
}
/**
* Returns the similarity of 2 words
*
* @param label1 the first word
* @param label2 the second word
* @return a normalized similarity (cosine similarity)
*/
@Override
public double similarity(String label1, String label2) {
if (label1 == null || label2 == null) {
log.debug("LABELS: " + label1 + ": " + (label1 == null ? "null" : "exists") + ";" + label2 + " vec2:"
+ (label2 == null ? "null" : "exists"));
return Double.NaN;
}
INDArray vec1 = getWordVectorMatrix(label1).dup();
INDArray vec2 = getWordVectorMatrix(label2).dup();
if (vec1 == null || vec2 == null) {
log.debug(label1 + ": " + (vec1 == null ? "null" : "exists") + ";" + label2 + " vec2:"
+ (vec2 == null ? "null" : "exists"));
return Double.NaN;
}
if (label1.equals(label2))
return 1.0;
vec1 = Transforms.unitVec(vec1);
vec2 = Transforms.unitVec(vec2);
return Transforms.cosineSim(vec1, vec2);
}
/**
* Vocab for the vectors
*
* @return
*/
@Override
public VocabCache vocab() {
return vocabCache;
}
/**
* Lookup table for the vectors
* PLEASE NOTE: This method is not available in this implementation.
*
* @return
*/
@Override
public WeightLookupTable lookupTable() {
throw new UnsupportedOperationException("Method isn't implemented. Please use usual Word2Vec implementation");
}
/**
* Specifies ModelUtils to be used to access model
* PLEASE NOTE: This method has no effect in this implementation.
*
* @param utils
*/
@Override
public void setModelUtils(ModelUtils utils) {
// no-op
}
@Override
public void loadWeightsInto(INDArray array) {
int n = (int)vocabSize();
INDArray zero = null;
for( int i=0; i storage;
private long cachePerDevice = 0L;
private VocabCache vocabCache;
/**
*
* @param storage AbstractStorage implementation, key has to be Integer, index of vocabWords
* @param vocabCache VocabCache implementation, which will be used to lookup word indexes
*/
public Builder(AbstractStorage storage, VocabCache vocabCache) {
this.storage = storage;
this.vocabCache = vocabCache;
}
/**
* This method lets you to define if decompressed values will be cached, to avoid excessive decompressions.
* If bytes == 0 - no cache will be used.
*
* @param bytes
* @return
*/
public Builder setCachePerDevice(long bytes) {
this.cachePerDevice = bytes;
return this;
}
/**
* This method returns Static Word2Vec implementation, which is suitable for tasks like neural nets feeding.
*
* @return
*/
public StaticWord2Vec build() {
StaticWord2Vec word2Vec = new StaticWord2Vec();
word2Vec.cachePerDevice = this.cachePerDevice;
word2Vec.storage = this.storage;
word2Vec.vocabCache = this.vocabCache;
word2Vec.init();
return word2Vec;
}
}
}
