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Deep Java Library (DJL) NLP utilities for Huggingface tokenizers
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/*
 * 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.modality.Classifications;
import ai.djl.ndarray.NDArray;
import ai.djl.ndarray.NDList;
import ai.djl.ndarray.NDManager;
import ai.djl.translate.ArgumentsUtil;
import ai.djl.translate.Batchifier;
import ai.djl.translate.Translator;
import ai.djl.translate.TranslatorContext;
import ai.djl.util.JsonUtils;

import java.io.IOException;
import java.io.Reader;
import java.nio.file.Files;
import java.nio.file.Path;
import java.util.ArrayList;
import java.util.List;
import java.util.Map;

/** The translator for Huggingface text classification model. */
public class TextClassificationTranslator implements Translator {

    private HuggingFaceTokenizer tokenizer;
    private boolean includeTokenTypes;
    private Batchifier batchifier;
    private PretrainedConfig config;

    TextClassificationTranslator(
            HuggingFaceTokenizer tokenizer, boolean includeTokenTypes, Batchifier batchifier) {
        this.tokenizer = tokenizer;
        this.includeTokenTypes = includeTokenTypes;
        this.batchifier = batchifier;
    }

    /** {@inheritDoc} */
    @Override
    public Batchifier getBatchifier() {
        return batchifier;
    }

    /** {@inheritDoc} */
    @Override
    public void prepare(TranslatorContext ctx) throws IOException {
        Path path = ctx.getModel().getModelPath();
        Path file = path.resolve("config.json");
        try (Reader reader = Files.newBufferedReader(file)) {
            config = JsonUtils.GSON.fromJson(reader, PretrainedConfig.class);
        }
    }

    /** {@inheritDoc} */
    @Override
    public NDList processInput(TranslatorContext ctx, String input) {
        Encoding encoding = tokenizer.encode(input);
        return encoding.toNDList(ctx.getNDManager(), includeTokenTypes);
    }

    /** {@inheritDoc} */
    @Override
    public NDList batchProcessInput(TranslatorContext ctx, List inputs) {
        NDManager manager = ctx.getNDManager();
        Encoding[] encodings = tokenizer.batchEncode(inputs);
        NDList[] batch = new NDList[encodings.length];
        for (int i = 0; i < encodings.length; ++i) {
            batch[i] = encodings[i].toNDList(manager, includeTokenTypes);
        }
        return batchifier.batchify(batch);
    }

    /** {@inheritDoc} */
    @Override
    public Classifications processOutput(TranslatorContext ctx, NDList list) {
        return toClassifications(list);
    }

    /** {@inheritDoc} */
    @Override
    public List batchProcessOutput(TranslatorContext ctx, NDList list) {
        NDList[] batches = batchifier.unbatchify(list);
        List ret = new ArrayList<>(batches.length);
        for (NDList batch : batches) {
            ret.add(toClassifications(batch));
        }
        return ret;
    }

    private Classifications toClassifications(NDList list) {
        NDArray logits = list.get(0);
        int size = config.id2label.size();
        if ("multi_label_classification".equals(config.problemType) || size == 1) {
            logits = logits.getNDArrayInternal().sigmoid();
        } else if ("single_label_classification".equals(config.problemType) || size > 1) {
            logits = logits.softmax(0);
        }
        long[] indices = logits.argSort(-1, false).toLongArray();
        float[] buf = logits.toFloatArray();
        List classes = new ArrayList<>(size);
        List probabilities = new ArrayList<>(size);
        for (long l : indices) {
            int index = Math.toIntExact(l);
            classes.add(config.id2label.get(String.valueOf(index)));
            probabilities.add((double) buf[index]);
        }
        return new Classifications(classes, probabilities);
    }

    /**
     * Creates a builder to build a {@code TextClassificationTranslator}.
     *
     * @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 TextClassificationTranslator}.
     *
     * @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 boolean includeTokenTypes;
        private Batchifier batchifier = Batchifier.STACK;

        Builder(HuggingFaceTokenizer tokenizer) {
            this.tokenizer = tokenizer;
        }

        /**
         * Sets if include token types for the {@link Translator}.
         *
         * @param includeTokenTypes true to include token types
         * @return this builder
         */
        public Builder optIncludeTokenTypes(boolean includeTokenTypes) {
            this.includeTokenTypes = includeTokenTypes;
            return this;
        }

        /**
         * 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;
        }

        /**
         * Configures the builder with the model arguments.
         *
         * @param arguments the model arguments
         */
        public void configure(Map arguments) {
            optIncludeTokenTypes(ArgumentsUtil.booleanValue(arguments, "includeTokenTypes"));
            String batchifierStr = ArgumentsUtil.stringValue(arguments, "batchifier", "stack");
            optBatchifier(Batchifier.fromString(batchifierStr));
        }

        /**
         * Builds the translator.
         *
         * @return the new translator
         * @throws IOException if I/O error occurs
         */
        public TextClassificationTranslator build() throws IOException {
            return new TextClassificationTranslator(tokenizer, includeTokenTypes, batchifier);
        }
    }
}