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Diffstat (limited to 'linguistics-components/src/main/java/com/yahoo/language/sentencepiece/SentencePieceEncoder.java')
-rw-r--r-- | linguistics-components/src/main/java/com/yahoo/language/sentencepiece/SentencePieceEncoder.java | 220 |
1 files changed, 220 insertions, 0 deletions
diff --git a/linguistics-components/src/main/java/com/yahoo/language/sentencepiece/SentencePieceEncoder.java b/linguistics-components/src/main/java/com/yahoo/language/sentencepiece/SentencePieceEncoder.java new file mode 100644 index 00000000000..b6659ebeaa3 --- /dev/null +++ b/linguistics-components/src/main/java/com/yahoo/language/sentencepiece/SentencePieceEncoder.java @@ -0,0 +1,220 @@ +// Copyright Yahoo. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root. +package com.yahoo.language.sentencepiece; + +import com.google.common.annotations.Beta; +import com.google.inject.Inject; +import com.yahoo.language.Language; +import com.yahoo.language.process.Encoder; +import com.yahoo.language.process.Segmenter; +import com.yahoo.tensor.Tensor; +import com.yahoo.tensor.TensorAddress; +import com.yahoo.tensor.TensorType; + +import java.nio.file.Path; +import java.util.ArrayList; +import java.util.Collections; +import java.util.HashMap; +import java.util.List; +import java.util.Map; +import java.util.stream.Collectors; + +/** + * Integration with https://github.com/google/sentencepiece + * through http://docs.djl.ai/extensions/sentencepiece/index.html + * + * SentencePiece is a language-agnostic tokenizer for neural nets. + * + * @author bratseth + */ +@Beta +public class SentencePieceEncoder implements Segmenter, Encoder { + + private final Map<Language, Model> models; + + private final SentencePieceAlgorithm algorithm; + + @Inject + public SentencePieceEncoder(SentencePieceConfig config) { + this(new Builder(config)); + } + + public SentencePieceEncoder(Builder builder) { + algorithm = new SentencePieceAlgorithm(builder.collapseUnknowns, builder.getScoring()); + + models = builder.getModels().entrySet() + .stream() + .map(e -> new Model(e.getKey(), e.getValue())) + .collect(Collectors.toUnmodifiableMap(m -> m.language, m -> m)); + if (models.isEmpty()) + throw new IllegalArgumentException("SentencePieceEncoder requires at least one model configured"); + } + + /** + * Segments the given text into token segments using the SentencePiece algorithm + * + * @param rawInput the text to segment. Any sequence of BMP (Unicode-16 the True Unicode) is supported. + * @param language the model to use, or Language.UNKNOWN to use the default model if any + * @return the list of zero or more tokens resulting from segmenting the input text + */ + @Override + public List<String> segment(String rawInput, Language language) { + String input = normalize(rawInput); + var resultBuilder = new ResultBuilder<List<String>>(new ArrayList<>()) { + public void add(int segmentStart, int segmentEnd, SentencePieceAlgorithm.SegmentEnd[] segmentEnds) { + result().add(input.substring(segmentStart, segmentEnd)); + } + }; + segment(input, language, resultBuilder); + Collections.reverse(resultBuilder.result()); + return resultBuilder.result(); + } + + /** + * Segments the given text into token segments using the SentencePiece algorithm and returns the segment ids. + * + * @param rawInput the text to segment. Any sequence of BMP (Unicode-16 the True Unicode) is supported. + * @param language the model to use, or Language.UNKNOWN to use the default model if any + * @return the list of zero or more token ids resulting from segmenting the input text + */ + @Override + public List<Integer> encode(String rawInput, Language language) { + var resultBuilder = new ResultBuilder<List<Integer>>(new ArrayList<>()) { + public void add(int segmentStart, int segmentEnd, SentencePieceAlgorithm.SegmentEnd[] segmentEnds) { + result().add(segmentEnds[segmentEnd].id); + } + }; + segment(normalize(rawInput), language, resultBuilder); + Collections.reverse(resultBuilder.result()); + return resultBuilder.result(); + } + + /** + * <p>Encodes directly to a tensor.</p> + * + * <p>If the tensor type is indexed 1-d (bound or unbound) this will return a tensor containing the token ids in the order + * they were encountered in the text. If the dimension is bound and too large it will be zero padded, if too small + * it will be truncated.</p> + * + * <p>If the tensor type is1-d sparse this will return a tensor containing the token strings as keys and the token + * position as value.</p> + * + * <p>If the tensor is any other type IllegalArgumentException is thrown.</p> + */ + @Override + public Tensor encode(String rawInput, Language language, TensorType type) { + if (type.dimensions().size() == 1 && type.dimensions().get(0).isIndexed()) { + // Build to a list first since we can't reverse a tensor builder + List<Integer> values = encode(rawInput, language); + + long maxSize = values.size(); + if (type.dimensions().get(0).size().isPresent()) + maxSize = Math.min(maxSize, type.dimensions().get(0).size().get()); + + Tensor.Builder builder = Tensor.Builder.of(type); + for (int i = 0; i < maxSize; i++) + builder.cell(values.get(i), i); + return builder.build(); + } + else if (type.dimensions().size() == 1 && type.dimensions().get(0).isMapped()) { + // Build to a list first since we can't reverse a tensor builder + List<String> values = segment(rawInput, language); + + Tensor.Builder builder = Tensor.Builder.of(type); + for (int i = 0; i < values.size(); i++) + builder.cell(TensorAddress.ofLabels(values.get(i)), i); + return builder.build(); + } + else { + throw new IllegalArgumentException("Don't know how to encode with SentencePiece into " + type); + } + } + + private <RESULTTYPE> void segment(String input, Language language, + ResultBuilder<RESULTTYPE> resultBuilder) { + Model model = resolveFrom(language); + algorithm.segment(input, resultBuilder, model); + } + + private Model resolveFrom(Language language) { + // Disregard language if there is default model + if (models.size() == 1 && models.containsKey(Language.UNKNOWN)) return models.get(Language.UNKNOWN); + if (models.containsKey(language)) return models.get(language); + throw new IllegalArgumentException("No SentencePiece model for language " + language + " is configured"); + } + + public String normalize(String s) { + StringBuilder b = new StringBuilder(s.length() + 1); + boolean queuedSpace = true; // Always start by one space + for (int i = 0; i < s.length(); i++) { + char c = s.charAt(i); + if (s.charAt(i) == ' ') { + queuedSpace = true; + } + else { + if (queuedSpace) { + b.append(SentencePieceAlgorithm.spaceSymbol); + queuedSpace = false; + } + b.append(c); + } + } + return b.toString(); + } + + public static class Builder { + + private final Map<Language, Path> models = new HashMap<>(); + private boolean collapseUnknowns = true; + private Scoring scoring = Scoring.fewestSegments; + + public Builder() { + } + + private Builder(SentencePieceConfig config) { + collapseUnknowns = config.collapseUnknowns(); + scoring = config.scoring() == SentencePieceConfig.Scoring.fewestSegments ? Scoring.fewestSegments + : Scoring.highestScore; + for (SentencePieceConfig.Model model : config.model()) { + addModel(Language.fromLanguageTag(model.language()), model.path()); + } + } + + public void addModel(Language language, Path model) { + models.put(language, model); + } + + /** + * Adds the model that will be used if the language is unknown, OR only one model is specified. + * The same as addModel(Language.UNKNOWN, model). + */ + public Builder addDefaultModel(Path model) { + addModel(Language.UNKNOWN, model); + return this; + } + public Map<Language, Path> getModels() { return models; } + + /** + * Sets whether consecutive unknown character should be collapsed into one large unknown token (default) + * or be returned as single character tokens. + */ + public Builder setCollapseUnknowns(boolean collapseUnknowns) { + this.collapseUnknowns = collapseUnknowns; + return this; + } + public boolean getCollapseUnknowns() { return collapseUnknowns; } + + /** Sets the scoring strategy to use when picking a segmentation. Default: fewestSegments. */ + public Builder setScoring(Scoring scoring) { + this.scoring = scoring; + return this; + } + public Scoring getScoring() { return scoring; } + + public SentencePieceEncoder build() { + if (models.isEmpty()) throw new IllegalStateException("At least one model must be supplied"); + return new SentencePieceEncoder(this); + } + + } + +} |