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Deep Java Library (DJL) NLP utilities for Huggingface tokenizers
/*
* Copyright 2022 Amazon.com, Inc. or its affiliates. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance
* with the License. A copy of the License is located at
*
* http://aws.amazon.com/apache2.0/
*
* or in the "license" file accompanying this file. This file 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 ai.djl.huggingface.translator;
import ai.djl.huggingface.tokenizers.Encoding;
import ai.djl.huggingface.tokenizers.HuggingFaceTokenizer;
import ai.djl.ndarray.NDArray;
import ai.djl.ndarray.NDList;
import ai.djl.ndarray.NDManager;
import ai.djl.ndarray.types.DataType;
import ai.djl.translate.ArgumentsUtil;
import ai.djl.translate.Batchifier;
import ai.djl.translate.Translator;
import ai.djl.translate.TranslatorContext;
import java.io.IOException;
import java.util.Map;
/** The translator for Huggingface text embedding model. */
public class TextEmbeddingTranslator implements Translator {
private static final int[] AXIS = {0};
private HuggingFaceTokenizer tokenizer;
private Batchifier batchifier;
private boolean normalize;
private String pooling;
TextEmbeddingTranslator(
HuggingFaceTokenizer tokenizer,
Batchifier batchifier,
String pooling,
boolean normalize) {
this.tokenizer = tokenizer;
this.batchifier = batchifier;
this.pooling = pooling;
this.normalize = normalize;
}
/** {@inheritDoc} */
@Override
public Batchifier getBatchifier() {
return batchifier;
}
/** {@inheritDoc} */
@Override
public NDList processInput(TranslatorContext ctx, String input) {
Encoding encoding = tokenizer.encode(input);
ctx.setAttachment("encoding", encoding);
return encoding.toNDList(ctx.getNDManager(), false);
}
/** {@inheritDoc} */
@Override
public float[] processOutput(TranslatorContext ctx, NDList list) {
Encoding encoding = (Encoding) ctx.getAttachment("encoding");
NDManager manager = ctx.getNDManager();
NDArray embeddings = processEmbedding(manager, list, encoding, pooling);
if (normalize) {
embeddings = embeddings.normalize(2, 0);
}
return embeddings.toFloatArray();
}
/** {@inheritDoc} */
@Override
public TextEmbeddingBatchTranslator toBatchTranslator(Batchifier batchifier) {
tokenizer.enableBatch();
return new TextEmbeddingBatchTranslator(tokenizer, batchifier, pooling, normalize);
}
static NDArray processEmbedding(
NDManager manager, NDList list, Encoding encoding, String pooling) {
NDArray embedding = list.get("last_hidden_state");
long[] attentionMask = encoding.getAttentionMask();
NDArray inputAttentionMask = manager.create(attentionMask).toType(DataType.FLOAT32, true);
switch (pooling) {
case "mean":
return meanPool(embedding, inputAttentionMask, false);
case "mean_sqrt_len":
return meanPool(embedding, inputAttentionMask, true);
case "max":
return maxPool(embedding, inputAttentionMask);
case "weightedmean":
return weightedMeanPool(embedding, inputAttentionMask);
case "cls":
return embedding.get(0);
default:
throw new AssertionError("Unexpected pooling mode: " + pooling);
}
}
private static NDArray meanPool(NDArray embeddings, NDArray attentionMask, boolean sqrt) {
long[] shape = embeddings.getShape().getShape();
attentionMask = attentionMask.expandDims(-1).broadcast(shape);
NDArray inputAttentionMaskSum = attentionMask.sum(AXIS);
NDArray clamp = inputAttentionMaskSum.clip(1e-9, 1e12);
NDArray prod = embeddings.mul(attentionMask);
NDArray sum = prod.sum(AXIS);
if (sqrt) {
return sum.div(clamp.sqrt());
}
return sum.div(clamp);
}
private static NDArray maxPool(NDArray embeddings, NDArray inputAttentionMask) {
long[] shape = embeddings.getShape().getShape();
inputAttentionMask = inputAttentionMask.expandDims(-1).broadcast(shape);
inputAttentionMask = inputAttentionMask.eq(0);
embeddings = embeddings.duplicate();
embeddings.set(inputAttentionMask, -1e9); // Set padding tokens to large negative value
return embeddings.max(AXIS, true);
}
private static NDArray weightedMeanPool(NDArray embeddings, NDArray attentionMask) {
long[] shape = embeddings.getShape().getShape();
NDArray weight = embeddings.getManager().arange(1, shape[0] + 1);
weight = weight.expandDims(-1).broadcast(shape);
attentionMask = attentionMask.expandDims(-1).broadcast(shape).mul(weight);
NDArray maskSum = attentionMask.sum(AXIS);
NDArray embeddingSum = embeddings.mul(attentionMask).sum(AXIS);
return embeddingSum.div(maskSum);
}
/**
* Creates a builder to build a {@code TextEmbeddingTranslator}.
*
* @param tokenizer the tokenizer
* @return a new builder
*/
public static Builder builder(HuggingFaceTokenizer tokenizer) {
return new Builder(tokenizer);
}
/**
* Creates a builder to build a {@code TextEmbeddingTranslator}.
*
* @param tokenizer the tokenizer
* @param arguments the models' arguments
* @return a new builder
*/
public static Builder builder(HuggingFaceTokenizer tokenizer, Map arguments) {
Builder builder = builder(tokenizer);
builder.configure(arguments);
return builder;
}
/** The builder for token classification translator. */
public static final class Builder {
private HuggingFaceTokenizer tokenizer;
private Batchifier batchifier = Batchifier.STACK;
private boolean normalize = true;
private String pooling = "mean";
Builder(HuggingFaceTokenizer tokenizer) {
this.tokenizer = tokenizer;
}
/**
* Sets the {@link Batchifier} for the {@link Translator}.
*
* @param batchifier true to include token types
* @return this builder
*/
public Builder optBatchifier(Batchifier batchifier) {
this.batchifier = batchifier;
return this;
}
/**
* Sets the {@code normalize} for the {@link Translator}.
*
* @param normalize true to normalize the embeddings
* @return this builder
*/
public Builder optNormalize(boolean normalize) {
this.normalize = normalize;
return this;
}
/**
* Sets the pooling for the {@link Translator}.
*
* @param poolingMode the pooling model, one of mean_pool, max_pool and cls
* @return this builder
*/
public Builder optPoolingMode(String poolingMode) {
if (!"mean".equals(poolingMode)
&& !"max".equals(poolingMode)
&& !"cls".equals(poolingMode)
&& !"mean_sqrt_len".equals(poolingMode)
&& !"weightedmean".equals(poolingMode)) {
throw new IllegalArgumentException(
"Invalid pooling model, must be one of [mean, max, cls, mean_sqrt_len,"
+ " weightedmean].");
}
this.pooling = poolingMode;
return this;
}
/**
* Configures the builder with the model arguments.
*
* @param arguments the model arguments
*/
public void configure(Map arguments) {
String batchifierStr = ArgumentsUtil.stringValue(arguments, "batchifier", "stack");
optBatchifier(Batchifier.fromString(batchifierStr));
optNormalize(ArgumentsUtil.booleanValue(arguments, "normalize", true));
optPoolingMode(ArgumentsUtil.stringValue(arguments, "pooling", "mean"));
}
/**
* Builds the translator.
*
* @return the new translator
* @throws IOException if I/O error occurs
*/
public TextEmbeddingTranslator build() throws IOException {
return new TextEmbeddingTranslator(tokenizer, batchifier, pooling, normalize);
}
}
}
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