From 4638249b87378633232fbd5174c80f7bcfd04b38 Mon Sep 17 00:00:00 2001 From: Kristian Rickert Date: Fri, 10 Jul 2026 14:12:15 -0400 Subject: [PATCH 1/9] Add opennlp-subword: pure-Java SentencePiece inference with exact original-text spans New opennlp-extensions module implementing SentencePiece model inference without native code: the ModelProto reader, the model-embedded normalizer (precompiled character map over a Darts-clone double-array trie, whitespace collapsing and escaping, the dummy word-boundary marker), unigram best-path segmentation, BPE agenda merging, byte fallback, and user-defined symbol handling. The public contract is SubwordTokenizer/SubwordPiece; every piece reports the exact UTF-16 span of the caller's original text it came from, and the model normalizer is also exposed as an OffsetAwareNormalizer producing AlignedText. Parity with the reference implementation is asserted, not assumed: five tiny bundled models (unigram, unigram with byte fallback, BPE, identity normalization, whitespace-as-suffix) carry fixtures generated by the sentencepiece Python package over 40 inputs each, checked piece for piece, id for id, span for span, plus each model's embedded self-test samples. An opt-in test (-Dopennlp.subword.eval.dir) runs the same assertions against real downloaded models; T5-small and ALBERT-base-v2 pass exactly, including mixed scripts, emoji ZWJ sequences, BOM, and CRLF inputs. --- opennlp-extensions/opennlp-subword/pom.xml | 58 ++ .../java/opennlp/subword/SubwordPiece.java | 59 ++ .../opennlp/subword/SubwordTokenizer.java | 77 +++ .../subword/sentencepiece/BpeEncoder.java | 216 ++++++++ .../subword/sentencepiece/ByteBuilder.java | 64 +++ .../sentencepiece/DoubleArrayTrie.java | 95 ++++ .../subword/sentencepiece/IntBuilder.java | 56 ++ .../sentencepiece/ModelProtoReader.java | 265 +++++++++ .../subword/sentencepiece/PieceTrie.java | 208 +++++++ .../subword/sentencepiece/Segment.java | 27 + .../SentencePieceNormalizer.java | 320 +++++++++++ .../sentencepiece/SentencePieceTokenizer.java | 509 ++++++++++++++++++ .../subword/sentencepiece/UnigramEncoder.java | 163 ++++++ .../subword/sentencepiece/Utf8Text.java | 109 ++++ .../SentencePieceAlignmentTest.java | 145 +++++ .../SentencePieceModelValidationTest.java | 183 +++++++ .../SentencePieceParityTest.java | 150 ++++++ .../SentencePieceRealModelEvalTest.java | 112 ++++ .../opennlp/subword/sentencepiece/corpus.txt | 66 +++ .../subword/sentencepiece/gen_fixtures.py | 139 +++++ .../sentencepiece/gen_real_fixtures.py | 75 +++ .../sentencepiece/tiny-bpe.fixtures.tsv | 40 ++ .../subword/sentencepiece/tiny-bpe.model | Bin 0 -> 245063 bytes .../tiny-unigram-bytefb.fixtures.tsv | 40 ++ .../sentencepiece/tiny-unigram-bytefb.model | Bin 0 -> 250431 bytes .../tiny-unigram-identity.fixtures.tsv | 40 ++ .../sentencepiece/tiny-unigram-identity.model | Bin 0 -> 5287 bytes .../tiny-unigram-suffix.fixtures.tsv | 40 ++ .../sentencepiece/tiny-unigram-suffix.model | Bin 0 -> 245428 bytes .../sentencepiece/tiny-unigram.fixtures.tsv | 40 ++ .../subword/sentencepiece/tiny-unigram.model | Bin 0 -> 245202 bytes opennlp-extensions/pom.xml | 1 + rat-excludes | 5 + 33 files changed, 3302 insertions(+) create mode 100644 opennlp-extensions/opennlp-subword/pom.xml create mode 100644 opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/SubwordPiece.java create mode 100644 opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/SubwordTokenizer.java create mode 100644 opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/BpeEncoder.java create mode 100644 opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/ByteBuilder.java create mode 100644 opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/DoubleArrayTrie.java create mode 100644 opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/IntBuilder.java create mode 100644 opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/ModelProtoReader.java create mode 100644 opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/PieceTrie.java create mode 100644 opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/Segment.java create mode 100644 opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceNormalizer.java create mode 100644 opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceTokenizer.java create mode 100644 opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/UnigramEncoder.java create mode 100644 opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/Utf8Text.java create mode 100644 opennlp-extensions/opennlp-subword/src/test/java/opennlp/subword/sentencepiece/SentencePieceAlignmentTest.java create mode 100644 opennlp-extensions/opennlp-subword/src/test/java/opennlp/subword/sentencepiece/SentencePieceModelValidationTest.java create mode 100644 opennlp-extensions/opennlp-subword/src/test/java/opennlp/subword/sentencepiece/SentencePieceParityTest.java create mode 100644 opennlp-extensions/opennlp-subword/src/test/java/opennlp/subword/sentencepiece/SentencePieceRealModelEvalTest.java create mode 100644 opennlp-extensions/opennlp-subword/src/test/resources/opennlp/subword/sentencepiece/corpus.txt create mode 100644 opennlp-extensions/opennlp-subword/src/test/resources/opennlp/subword/sentencepiece/gen_fixtures.py create mode 100644 opennlp-extensions/opennlp-subword/src/test/resources/opennlp/subword/sentencepiece/gen_real_fixtures.py create mode 100644 opennlp-extensions/opennlp-subword/src/test/resources/opennlp/subword/sentencepiece/tiny-bpe.fixtures.tsv create mode 100644 opennlp-extensions/opennlp-subword/src/test/resources/opennlp/subword/sentencepiece/tiny-bpe.model create mode 100644 opennlp-extensions/opennlp-subword/src/test/resources/opennlp/subword/sentencepiece/tiny-unigram-bytefb.fixtures.tsv create mode 100644 opennlp-extensions/opennlp-subword/src/test/resources/opennlp/subword/sentencepiece/tiny-unigram-bytefb.model create mode 100644 opennlp-extensions/opennlp-subword/src/test/resources/opennlp/subword/sentencepiece/tiny-unigram-identity.fixtures.tsv create mode 100644 opennlp-extensions/opennlp-subword/src/test/resources/opennlp/subword/sentencepiece/tiny-unigram-identity.model create mode 100644 opennlp-extensions/opennlp-subword/src/test/resources/opennlp/subword/sentencepiece/tiny-unigram-suffix.fixtures.tsv create mode 100644 opennlp-extensions/opennlp-subword/src/test/resources/opennlp/subword/sentencepiece/tiny-unigram-suffix.model create mode 100644 opennlp-extensions/opennlp-subword/src/test/resources/opennlp/subword/sentencepiece/tiny-unigram.fixtures.tsv create mode 100644 opennlp-extensions/opennlp-subword/src/test/resources/opennlp/subword/sentencepiece/tiny-unigram.model diff --git a/opennlp-extensions/opennlp-subword/pom.xml b/opennlp-extensions/opennlp-subword/pom.xml new file mode 100644 index 0000000000..b4d13c755e --- /dev/null +++ b/opennlp-extensions/opennlp-subword/pom.xml @@ -0,0 +1,58 @@ + + + + + + 4.0.0 + + org.apache.opennlp + opennlp-extensions + 3.0.0-SNAPSHOT + + + opennlp-subword + jar + Apache OpenNLP :: Ext :: Subword + + + + org.apache.opennlp + opennlp-api + + + + org.junit.jupiter + junit-jupiter-api + test + + + + org.junit.jupiter + junit-jupiter-engine + test + + + + org.junit.jupiter + junit-jupiter-params + test + + + diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/SubwordPiece.java b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/SubwordPiece.java new file mode 100644 index 0000000000..1791583101 --- /dev/null +++ b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/SubwordPiece.java @@ -0,0 +1,59 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://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. + */ +package opennlp.subword; + +import opennlp.tools.util.Span; + +/** + * One subword unit produced by a {@link SubwordTokenizer}, carrying both the vocabulary view + * (the piece string and its id) and the exact place in the caller's text it came from. + * + *

The piece string is in the tokenizer's internal, normalized form (for example, a leading + * word-boundary marker instead of a space), so it is generally not a substring of the input. + * {@code start} and {@code end} are UTF-16 offsets into the original input text, so the surface + * that produced this piece is {@code text.subSequence(start, end)}. Pieces that carry no surface + * of their own (control symbols, or the fill bytes of a byte-fallback expansion) report an empty + * span, {@code start == end}.

+ * + * @param piece The piece in the vocabulary's normalized form; never null or empty. + * @param id The vocabulary id of the piece. + * @param start The inclusive start offset in the original text. + * @param end The exclusive end offset in the original text; not less than {@code start}. + */ +public record SubwordPiece(String piece, int id, int start, int end) { + + /** + * Instantiates a {@link SubwordPiece}. + * + * @throws IllegalArgumentException Thrown if {@code piece} is null or empty, or the span is + * negative or inverted. + */ + public SubwordPiece { + if (piece == null || piece.isEmpty()) { + throw new IllegalArgumentException("The piece must not be null or empty."); + } + if (start < 0 || end < start) { + throw new IllegalArgumentException( + "The span [" + start + ", " + end + ") must not be negative or inverted."); + } + } + + /** {@return the original-text span of this piece as a {@link Span}} */ + public Span span() { + return new Span(start, end); + } +} diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/SubwordTokenizer.java b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/SubwordTokenizer.java new file mode 100644 index 0000000000..ea31342bf2 --- /dev/null +++ b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/SubwordTokenizer.java @@ -0,0 +1,77 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://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. + */ +package opennlp.subword; + +import java.util.List; + +/** + * Splits text into subword units against a fixed vocabulary, reporting for every unit its + * vocabulary id and the exact span of the original text it covers. + * + *

Subword tokenization is the input layer of modern sequence models: text is decomposed into + * pieces from a trained vocabulary so that any input, including words never seen in training, maps + * to a bounded id space. Unlike a linguistic {@code Tokenizer}, the segmentation is + * vocabulary-driven, and the pieces are in the model's normalized form rather than substrings of + * the input. The offsets carried by each {@link SubwordPiece} are what tie the two worlds + * together: they always refer to the caller's original text.

+ * + *

Implementations are expected to be safe for concurrent use by multiple threads; any + * implementation that is not must document it.

+ */ +public interface SubwordTokenizer { + + /** + * Encodes text into subword pieces. + * + * @param text The text to encode; must not be null. + * @return The pieces in text order; empty when the text contains nothing encodable. + * @throws IllegalArgumentException Thrown if {@code text} is null. + */ + List encode(CharSequence text); + + /** + * Encodes text into vocabulary ids. + * + * @param text The text to encode; must not be null. + * @return The ids in text order; empty when the text contains nothing encodable. + * @throws IllegalArgumentException Thrown if {@code text} is null. + */ + default int[] encodeToIds(CharSequence text) { + final List pieces = encode(text); + final int[] ids = new int[pieces.size()]; + for (int i = 0; i < ids.length; i++) { + ids[i] = pieces.get(i).id(); + } + return ids; + } + + /** + * Encodes text into piece strings in the vocabulary's normalized form. + * + * @param text The text to encode; must not be null. + * @return The pieces in text order; empty when the text contains nothing encodable. + * @throws IllegalArgumentException Thrown if {@code text} is null. + */ + default String[] encodeToPieces(CharSequence text) { + final List pieces = encode(text); + final String[] out = new String[pieces.size()]; + for (int i = 0; i < out.length; i++) { + out[i] = pieces.get(i).piece(); + } + return out; + } +} diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/BpeEncoder.java b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/BpeEncoder.java new file mode 100644 index 0000000000..3cea8edb64 --- /dev/null +++ b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/BpeEncoder.java @@ -0,0 +1,216 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://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. + */ +package opennlp.subword.sentencepiece; + +import java.nio.charset.StandardCharsets; +import java.util.ArrayList; +import java.util.HashMap; +import java.util.List; +import java.util.Map; +import java.util.PriorityQueue; + +/** + * Byte-pair-encoding segmentation: the normalized text starts as single characters (or + * user-defined symbols, which are frozen whole) and adjacent pairs merge greedily, highest piece + * score first, until no adjacent pair forms a vocabulary piece. + * + *

This ports the reference implementation's agenda algorithm: candidate pairs sit in a + * priority queue ordered by score with ties broken towards the leftmost pair, stale entries are + * detected by a length check when popped, and merges that land on a piece marked unused are + * re-segmented back into their constituents afterwards. Only pieces of the normal, user-defined, + * and unused types participate in merges.

+ */ +final class BpeEncoder { + + private static final int MAX_RESEGMENT_DEPTH = 100; + + private final Map pieces; + private final float[] scores; + private final boolean[] unused; + private final boolean[] reserved; + private final int unkId; + private final PieceTrie userDefinedMatcher; + + /** + * Instantiates the encoder. + * + * @param pieces All pieces by content, mapping to their ids. + * @param scores The score of every piece, indexed by id. + * @param unused Whether each id has the unused piece type. + * @param reserved Whether each id is excluded from merging (any type other than + * normal, user-defined, or unused). + * @param unkId The id of the unknown piece. + * @param userDefinedMatcher Longest-match trie over user-defined symbols, or null when the + * model defines none. + */ + BpeEncoder(Map pieces, float[] scores, boolean[] unused, boolean[] reserved, + int unkId, PieceTrie userDefinedMatcher) { + this.pieces = pieces; + this.scores = scores; + this.unused = unused; + this.reserved = reserved; + this.unkId = unkId; + this.userDefinedMatcher = userDefinedMatcher; + } + + // A candidate merge of the symbols at indices left and right; size is the merged byte length + // used to detect staleness after either side has changed. + private record Pair(int left, int right, float score, int size) { + } + + /** + * Segments normalized text. + * + * @param normalized The normalized UTF-8 bytes; must not be null. + * @return The segments covering all bytes, in text order. + */ + List encode(byte[] normalized) { + if (normalized.length == 0) { + return List.of(); + } + + // The symbol list as index-linked ranges of the normalized bytes; merged-away symbols + // become empty ranges. + final IntBuilder fromB = new IntBuilder(normalized.length); + final IntBuilder toB = new IntBuilder(normalized.length); + final List freezeList = new ArrayList<>(); + int position = 0; + while (position < normalized.length) { + int matched = 0; + if (userDefinedMatcher != null) { + matched = longestUserDefinedMatch(normalized, position); + } + final boolean frozen = matched > 0; + final int length = frozen ? matched + : Math.min(SentencePieceNormalizer.utf8Length(normalized[position]), + normalized.length - position); + fromB.append(position); + toB.append(position + length); + freezeList.add(frozen); + position += length; + } + final int symbolCount = freezeList.size(); + final int[] from = fromB.toArray(); + final int[] to = toB.toArray(); + final int[] prev = new int[symbolCount]; + final int[] next = new int[symbolCount]; + final boolean[] freeze = new boolean[symbolCount]; + for (int i = 0; i < symbolCount; i++) { + prev[i] = i - 1; + next[i] = i + 1 < symbolCount ? i + 1 : -1; + freeze[i] = freezeList.get(i); + } + + // Higher score first; equal scores break towards the leftmost pair. + final PriorityQueue agenda = new PriorityQueue<>((a, b) -> { + final int byScore = Float.compare(b.score(), a.score()); + return byScore != 0 ? byScore : Integer.compare(a.left(), b.left()); + }); + // Merged piece content mapped back to its two constituents, for re-segmenting unused pieces. + final Map revMerge = new HashMap<>(); + + for (int left = 0; left + 1 < symbolCount; left++) { + maybeAddPair(normalized, from, to, freeze, left, left + 1, agenda, revMerge); + } + + while (!agenda.isEmpty()) { + final Pair top = agenda.poll(); + // Skips entries made stale by an earlier merge of either side. + if (from[top.left()] == to[top.left()] || from[top.right()] == to[top.right()] + || to[top.left()] - from[top.left()] + to[top.right()] - from[top.right()] + != top.size()) { + continue; + } + + // Replaces the pair with the merged symbol. + to[top.left()] = to[top.right()]; + next[top.left()] = next[top.right()]; + if (next[top.right()] >= 0) { + prev[next[top.right()]] = top.left(); + } + from[top.right()] = to[top.right()]; + + maybeAddPair(normalized, from, to, freeze, prev[top.left()], top.left(), agenda, revMerge); + maybeAddPair(normalized, from, to, freeze, top.left(), next[top.left()], agenda, revMerge); + } + + final List output = new ArrayList<>(symbolCount); + int consumed = 0; + for (int index = 0; index != -1; index = next[index]) { + final String piece = + new String(normalized, from[index], to[index] - from[index], StandardCharsets.UTF_8); + consumed = resegment(piece, consumed, 0, revMerge, output); + } + return output; + } + + private void maybeAddPair(byte[] normalized, int[] from, int[] to, boolean[] freeze, + int left, int right, PriorityQueue agenda, + Map revMerge) { + if (left == -1 || right == -1 || freeze[left] || freeze[right]) { + return; + } + final String piece = + new String(normalized, from[left], to[right] - from[left], StandardCharsets.UTF_8); + final Integer id = pieces.get(piece); + if (id == null || id == unkId || reserved[id]) { + return; + } + agenda.add(new Pair(left, right, scores[id], to[right] - from[left])); + if (unused[id]) { + revMerge.put(piece, new String[] { + new String(normalized, from[left], to[left] - from[left], StandardCharsets.UTF_8), + new String(normalized, from[right], to[right] - from[right], StandardCharsets.UTF_8)}); + } + } + + // Emits a symbol, splitting a piece of the unused type back into the pieces it was merged + // from. Positions are assigned by a running cursor; constituent byte lengths always sum to the + // merged length, so the cursor stays aligned with the normalized bytes. + private int resegment(String piece, int consumed, int depth, Map revMerge, + List output) { + final Integer mapped = pieces.get(piece); + final int id = mapped == null ? unkId : mapped; + final int byteLength = piece.getBytes(StandardCharsets.UTF_8).length; + if (depth > MAX_RESEGMENT_DEPTH || !unused[id]) { + output.add(new Segment(consumed, consumed + byteLength, id)); + return consumed + byteLength; + } + final String[] parts = revMerge.get(piece); + if (parts == null) { + output.add(new Segment(consumed, consumed + byteLength, id)); + return consumed + byteLength; + } + consumed = resegment(parts[0], consumed, depth + 1, revMerge, output); + return resegment(parts[1], consumed, depth + 1, revMerge, output); + } + + private int longestUserDefinedMatch(byte[] input, int from) { + int node = userDefinedMatcher.root(); + int longest = 0; + for (int i = from; i < input.length; i++) { + node = userDefinedMatcher.step(node, input[i]); + if (node == PieceTrie.DEAD) { + break; + } + if (userDefinedMatcher.value(node) >= 0) { + longest = i - from + 1; + } + } + return longest; + } +} diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/ByteBuilder.java b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/ByteBuilder.java new file mode 100644 index 0000000000..038b9f8ce1 --- /dev/null +++ b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/ByteBuilder.java @@ -0,0 +1,64 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://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. + */ +package opennlp.subword.sentencepiece; + +import java.util.Arrays; + +/** A growable byte buffer supporting append, truncate, and suffix comparison. */ +final class ByteBuilder { + + private byte[] data; + private int length; + + ByteBuilder(int capacity) { + data = new byte[Math.max(capacity, 16)]; + } + + void append(byte b) { + if (length == data.length) { + data = Arrays.copyOf(data, data.length + (data.length >> 1)); + } + data[length++] = b; + } + + void append(byte[] source, int from, int count) { + while (length + count > data.length) { + data = Arrays.copyOf(data, data.length + (data.length >> 1)); + } + System.arraycopy(source, from, data, length, count); + length += count; + } + + int length() { + return length; + } + + void truncate(int newLength) { + length = newLength; + } + + boolean endsWith(byte[] suffix) { + if (length < suffix.length) { + return false; + } + return Arrays.equals(data, length - suffix.length, length, suffix, 0, suffix.length); + } + + byte[] toArray() { + return Arrays.copyOf(data, length); + } +} diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/DoubleArrayTrie.java b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/DoubleArrayTrie.java new file mode 100644 index 0000000000..d824b87527 --- /dev/null +++ b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/DoubleArrayTrie.java @@ -0,0 +1,95 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://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. + */ +package opennlp.subword.sentencepiece; + +/** + * Read-only lookup over a serialized Darts-clone double-array trie, the dictionary format + * embedded in a SentencePiece model's precompiled character map. + * + *

Each unit is one little-endian 32-bit word encoding a label, an offset to the unit's + * children, and a leaf flag; traversal XORs the offset with the next key byte. Only the longest + * prefix match is needed here, so this walks the byte key once and remembers the last accepting + * state. Out-of-range unit references, which a well-formed trie never produces, fail loudly + * rather than reading arbitrary memory.

+ */ +final class DoubleArrayTrie { + + private final int[] units; + + /** + * Wraps serialized trie units. + * + * @param data The bytes holding the units; must not be null. + * @param offset The offset of the first unit byte. + * @param length The number of bytes; must be a positive multiple of four. + */ + DoubleArrayTrie(byte[] data, int offset, int length) { + if (length <= 0 || (length & 3) != 0) { + throw new IllegalArgumentException( + "The trie length " + length + " is not a positive multiple of four bytes."); + } + units = new int[length >> 2]; + for (int i = 0; i < units.length; i++) { + final int base = offset + (i << 2); + units[i] = (data[base] & 0xFF) | (data[base + 1] & 0xFF) << 8 + | (data[base + 2] & 0xFF) << 16 | (data[base + 3] & 0xFF) << 24; + } + } + + /** + * Finds the longest key that is a prefix of {@code key[from, to)}. + * + * @param key The byte key to match against; must not be null. + * @param from The inclusive start of the query window. + * @param to The exclusive end of the query window. + * @return {@code (value << 32) | matchedLength} for the longest match, or {@code -1} when no + * key matches. Values are non-negative, so the result is negative only on no-match. + */ + long longestPrefixMatch(byte[] key, int from, int to) { + long result = -1; + int nodePos = 0; + int unit = unit(nodePos); + nodePos ^= offset(unit); + for (int i = from; i < to; i++) { + final int b = key[i] & 0xFF; + nodePos ^= b; + unit = unit(nodePos); + if ((unit & 0x800000FF) != b) { + return result; + } + nodePos ^= offset(unit); + if (((unit >>> 8) & 1) == 1) { + final int value = unit(nodePos) & 0x7FFFFFFF; + result = ((long) value << 32) | (i - from + 1); + } + } + return result; + } + + private int unit(int nodePos) { + if (nodePos < 0 || nodePos >= units.length) { + throw new IllegalArgumentException( + "The trie references unit " + nodePos + " outside its " + units.length + " units."); + } + return units[nodePos]; + } + + // The offset from a unit to its children, as encoded by Darts-clone. + private static int offset(int unit) { + return (unit >>> 10) << ((unit & (1 << 9)) >>> 6); + } +} diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/IntBuilder.java b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/IntBuilder.java new file mode 100644 index 0000000000..70a2db9ac4 --- /dev/null +++ b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/IntBuilder.java @@ -0,0 +1,56 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://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. + */ +package opennlp.subword.sentencepiece; + +import java.util.Arrays; + +/** A growable int buffer supporting append, indexed read, and truncate. */ +final class IntBuilder { + + private int[] data; + private int length; + + IntBuilder(int capacity) { + data = new int[Math.max(capacity, 16)]; + } + + void append(int value) { + if (length == data.length) { + data = Arrays.copyOf(data, data.length + (data.length >> 1)); + } + data[length++] = value; + } + + int get(int index) { + if (index >= length) { + throw new IndexOutOfBoundsException("index " + index + " is outside [0, " + length + ")"); + } + return data[index]; + } + + int length() { + return length; + } + + void truncate(int newLength) { + length = newLength; + } + + int[] toArray() { + return Arrays.copyOf(data, length); + } +} diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/ModelProtoReader.java b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/ModelProtoReader.java new file mode 100644 index 0000000000..4e75f49c97 --- /dev/null +++ b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/ModelProtoReader.java @@ -0,0 +1,265 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://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. + */ +package opennlp.subword.sentencepiece; + +import java.nio.charset.StandardCharsets; +import java.util.ArrayList; +import java.util.List; + +/** + * Reads the binary {@code ModelProto} serialization of a SentencePiece {@code .model} file. + * + *

The format is standard protocol-buffer wire encoding of one flat message + * ({@code sentencepiece_model.proto}, Apache License 2.0), so this reader walks the tag stream + * directly and keeps only the fields inference needs: the pieces with scores and types, the + * normalizer spec, the handful of trainer-spec fields that change runtime behavior, and the + * embedded self-test samples. Unknown fields are skipped, malformed input fails loudly.

+ */ +final class ModelProtoReader { + + // Wire types of the protocol-buffer encoding. + private static final int WIRE_VARINT = 0; + private static final int WIRE_FIXED64 = 1; + private static final int WIRE_LEN = 2; + private static final int WIRE_FIXED32 = 5; + + private final byte[] data; + private int pos; + + private ModelProtoReader(byte[] data) { + this.data = data; + } + + /** + * Parses a serialized {@code ModelProto}. + * + * @param data The raw bytes of a {@code .model} file; must not be null. + * @return The parsed model description. + * @throws IllegalArgumentException Thrown if the bytes are not a well-formed model. + */ + static RawModel read(byte[] data) { + if (data == null) { + throw new IllegalArgumentException("The model data must not be null."); + } + final ModelProtoReader reader = new ModelProtoReader(data); + final RawModel model = new RawModel(); + while (reader.pos < data.length) { + final long tag = reader.varint(); + final int field = (int) (tag >>> 3); + switch (field) { + case 1 -> reader.piece(model, reader.lenPayload(tag)); + case 2 -> reader.trainerSpec(model, reader.lenPayload(tag)); + case 3 -> reader.normalizerSpec(model, reader.lenPayload(tag)); + case 4 -> reader.selfTestData(model, reader.lenPayload(tag)); + default -> reader.skip(tag); + } + } + if (model.pieces.isEmpty()) { + throw new IllegalArgumentException("The model defines no pieces."); + } + return model; + } + + private void piece(RawModel model, int end) { + String piece = null; + float score = 0; + int type = RawModel.TYPE_NORMAL; + while (pos < end) { + final long tag = varint(); + switch ((int) (tag >>> 3)) { + case 1 -> piece = utf8(lenPayload(tag)); + case 2 -> score = fixed32Float(tag); + case 3 -> type = (int) varintOf(tag); + default -> skip(tag); + } + } + if (piece == null || piece.isEmpty()) { + throw new IllegalArgumentException( + "The model contains an empty piece at index " + model.pieces.size() + "."); + } + if (Float.isNaN(score) || Float.isInfinite(score)) { + throw new IllegalArgumentException("The score of piece '" + piece + "' is not finite."); + } + model.pieces.add(piece); + model.scores.add(score); + model.types.add(type); + } + + private void trainerSpec(RawModel model, int end) { + while (pos < end) { + final long tag = varint(); + switch ((int) (tag >>> 3)) { + case 3 -> model.modelType = (int) varintOf(tag); + case 24 -> model.treatWhitespaceAsSuffix = varintOf(tag) != 0; + case 35 -> model.byteFallback = varintOf(tag) != 0; + case 40 -> model.unkId = (int) varintOf(tag); + default -> skip(tag); + } + } + } + + private void normalizerSpec(RawModel model, int end) { + while (pos < end) { + final long tag = varint(); + switch ((int) (tag >>> 3)) { + case 2 -> model.precompiledCharsMap = bytes(lenPayload(tag)); + case 3 -> model.addDummyPrefix = varintOf(tag) != 0; + case 4 -> model.removeExtraWhitespaces = varintOf(tag) != 0; + case 5 -> model.escapeWhitespaces = varintOf(tag) != 0; + default -> skip(tag); + } + } + } + + private void selfTestData(RawModel model, int end) { + while (pos < end) { + final long tag = varint(); + if ((int) (tag >>> 3) == 1) { + final int sampleEnd = lenPayload(tag); + String input = null; + String expected = null; + while (pos < sampleEnd) { + final long sampleTag = varint(); + switch ((int) (sampleTag >>> 3)) { + case 1 -> input = utf8(lenPayload(sampleTag)); + case 2 -> expected = utf8(lenPayload(sampleTag)); + default -> skip(sampleTag); + } + } + if (input != null && expected != null) { + model.selfTestInputs.add(input); + model.selfTestExpected.add(expected); + } + } else { + skip(tag); + } + } + } + + // Returns the exclusive end offset of a length-delimited payload, verifying the wire type. + private int lenPayload(long tag) { + if ((tag & 7) != WIRE_LEN) { + throw malformed("field " + (tag >>> 3) + " is not length-delimited"); + } + final long length = varint(); + if (length < 0 || pos + length > data.length) { + throw malformed("length " + length + " exceeds the remaining input"); + } + return pos + (int) length; + } + + private long varintOf(long tag) { + if ((tag & 7) != WIRE_VARINT) { + throw malformed("field " + (tag >>> 3) + " is not a varint"); + } + return varint(); + } + + private float fixed32Float(long tag) { + if ((tag & 7) != WIRE_FIXED32) { + throw malformed("field " + (tag >>> 3) + " is not a 32-bit value"); + } + if (pos + 4 > data.length) { + throw malformed("truncated 32-bit value"); + } + final int bits = (data[pos] & 0xFF) | (data[pos + 1] & 0xFF) << 8 + | (data[pos + 2] & 0xFF) << 16 | (data[pos + 3] & 0xFF) << 24; + pos += 4; + return Float.intBitsToFloat(bits); + } + + private String utf8(int end) { + final String s = new String(data, pos, end - pos, StandardCharsets.UTF_8); + pos = end; + return s; + } + + private byte[] bytes(int end) { + final byte[] b = new byte[end - pos]; + System.arraycopy(data, pos, b, 0, b.length); + pos = end; + return b; + } + + private long varint() { + long value = 0; + for (int shift = 0; shift < 64; shift += 7) { + if (pos >= data.length) { + throw malformed("truncated varint"); + } + final byte b = data[pos++]; + value |= (long) (b & 0x7F) << shift; + if ((b & 0x80) == 0) { + return value; + } + } + throw malformed("varint exceeds 64 bits"); + } + + private void skip(long tag) { + switch ((int) (tag & 7)) { + case WIRE_VARINT -> varint(); + case WIRE_FIXED64 -> advance(8); + case WIRE_LEN -> pos = lenPayload(tag); + case WIRE_FIXED32 -> advance(4); + default -> throw malformed("unsupported wire type " + (tag & 7)); + } + } + + private void advance(int count) { + if (pos + count > data.length) { + throw malformed("truncated field"); + } + pos += count; + } + + private IllegalArgumentException malformed(String detail) { + return new IllegalArgumentException( + "The model data is malformed at byte " + pos + ": " + detail + "."); + } + + /** The fields of a {@code ModelProto} that inference needs, with the proto's defaults. */ + static final class RawModel { + + static final int TYPE_NORMAL = 1; + static final int TYPE_UNKNOWN = 2; + static final int TYPE_CONTROL = 3; + static final int TYPE_USER_DEFINED = 4; + static final int TYPE_UNUSED = 5; + static final int TYPE_BYTE = 6; + + static final int MODEL_TYPE_UNIGRAM = 1; + static final int MODEL_TYPE_BPE = 2; + + final List pieces = new ArrayList<>(); + final List scores = new ArrayList<>(); + final List types = new ArrayList<>(); + + int modelType = MODEL_TYPE_UNIGRAM; + boolean byteFallback = false; + boolean treatWhitespaceAsSuffix = false; + int unkId = 0; + + byte[] precompiledCharsMap = new byte[0]; + boolean addDummyPrefix = true; + boolean removeExtraWhitespaces = true; + boolean escapeWhitespaces = true; + + final List selfTestInputs = new ArrayList<>(); + final List selfTestExpected = new ArrayList<>(); + } +} diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/PieceTrie.java b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/PieceTrie.java new file mode 100644 index 0000000000..eeb028e00b --- /dev/null +++ b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/PieceTrie.java @@ -0,0 +1,208 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://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. + */ +package opennlp.subword.sentencepiece; + +import java.util.Arrays; +import java.util.Comparator; + +/** + * An immutable byte-level trie over vocabulary pieces, packed into flat arrays. + * + *

Encoding walks it one byte at a time ({@link #step(int, byte)}) while scanning the input, so + * every piece that starts at a given input position is enumerated in one forward pass; this is the + * lattice-population step of subword segmentation. Children of a node are stored as a sorted + * label slice and found by binary search.

+ */ +final class PieceTrie { + + /** The node id returned when no transition exists. */ + static final int DEAD = -1; + + // Per node: the slice [childStart[n], childStart[n + 1]) of labels/childNodes, and the piece id + // accepted at the node, or -1. + private final int[] childStart; + private final byte[] labels; + private final int[] childNodes; + private final int[] values; + + private PieceTrie(int[] childStart, byte[] labels, int[] childNodes, int[] values) { + this.childStart = childStart; + this.labels = labels; + this.childNodes = childNodes; + this.values = values; + } + + /** + * Builds a trie from pieces and their ids. + * + * @param pieces The UTF-8 bytes of each piece; must not be null or contain empty keys. + * @param ids The id stored for each piece, parallel to {@code pieces}. + * @return The packed trie. + */ + static PieceTrie build(byte[][] pieces, int[] ids) { + final Integer[] order = new Integer[pieces.length]; + for (int i = 0; i < order.length; i++) { + order[i] = i; + } + Arrays.sort(order, Comparator.comparing(i -> pieces[i], Arrays::compareUnsigned)); + + // First pass counts nodes and edges, second pass fills the packed arrays; both walk the + // sorted keys with the same recursion, so the shapes agree by construction. + final Builder builder = new Builder(pieces, ids, order); + builder.count(0, pieces.length, 0); + builder.allocate(); + builder.fill(0, pieces.length, 0); + return new PieceTrie(builder.childStart, builder.labels, builder.childNodes, builder.values); + } + + /** {@return the root node id} */ + int root() { + return 0; + } + + /** + * Follows the transition labeled {@code b}. + * + * @param node The current node id. + * @param b The next key byte. + * @return The child node id, or {@link #DEAD} when no such transition exists. + */ + int step(int node, byte b) { + final int from = childStart[node]; + final int to = childStart[node + 1]; + int low = from; + int high = to - 1; + while (low <= high) { + final int mid = (low + high) >>> 1; + final int c = Byte.compareUnsigned(labels[mid], b); + if (c < 0) { + low = mid + 1; + } else if (c > 0) { + high = mid - 1; + } else { + return childNodes[mid]; + } + } + return DEAD; + } + + /** + * Returns the piece id accepted at a node. + * + * @param node The node id. + * @return The id, or {@code -1} when the node accepts no piece. + */ + int value(int node) { + return values[node]; + } + + // Builds the packed form from keys sorted by unsigned byte order. Key ranges sharing a prefix + // are contiguous after the sort, so each recursion partitions its range by the byte at the + // current depth. + private static final class Builder { + + private final byte[][] pieces; + private final int[] ids; + private final Integer[] order; + + private int nodeCount; + private int edgeCount; + + private int[] childStart; + private byte[] labels; + private int[] childNodes; + private int[] values; + private int nextNode; + private int nextEdge; + + Builder(byte[][] pieces, int[] ids, Integer[] order) { + this.pieces = pieces; + this.ids = ids; + this.order = order; + } + + void count(int from, int to, int depth) { + nodeCount++; + int i = from; + if (i < to && pieces[order[i]].length == depth) { + i++; + } + while (i < to) { + final byte label = pieces[order[i]][depth]; + int j = i; + while (j < to && pieces[order[j]][depth] == label) { + j++; + } + edgeCount++; + count(i, j, depth + 1); + i = j; + } + } + + void allocate() { + childStart = new int[nodeCount + 1]; + labels = new byte[edgeCount]; + childNodes = new int[edgeCount]; + values = new int[nodeCount]; + } + + int fill(int from, int to, int depth) { + final int node = nextNode++; + values[node] = -1; + int i = from; + if (i < to && pieces[order[i]].length == depth) { + if (values[node] != -1 || (i + 1 < to && pieces[order[i + 1]].length == depth)) { + throw new IllegalArgumentException( + "The piece '" + new String(pieces[order[i]], java.nio.charset.StandardCharsets.UTF_8) + + "' is defined more than once."); + } + values[node] = ids[order[i]]; + i++; + } + // Reserve this node's edge slice before recursing so siblings stay contiguous. + final int sliceStart = nextEdge; + int sliceCount = 0; + int scan = i; + while (scan < to) { + final byte label = pieces[order[scan]][depth]; + int j = scan; + while (j < to && pieces[order[j]][depth] == label) { + j++; + } + sliceCount++; + scan = j; + } + nextEdge += sliceCount; + childStart[node] = sliceStart; + childStart[node + 1] = nextEdge; + + int edge = sliceStart; + while (i < to) { + final byte label = pieces[order[i]][depth]; + int j = i; + while (j < to && pieces[order[j]][depth] == label) { + j++; + } + labels[edge] = label; + childNodes[edge] = fill(i, j, depth + 1); + edge++; + i = j; + } + return node; + } + } +} diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/Segment.java b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/Segment.java new file mode 100644 index 0000000000..7f603c6ac5 --- /dev/null +++ b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/Segment.java @@ -0,0 +1,27 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://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. + */ +package opennlp.subword.sentencepiece; + +/** + * One encoded piece as a half-open byte range of the normalized text plus its vocabulary id. + * + * @param from The inclusive start offset in the normalized bytes. + * @param to The exclusive end offset in the normalized bytes. + * @param id The vocabulary id; the unknown id when no piece covers the range. + */ +record Segment(int from, int to, int id) { +} diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceNormalizer.java b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceNormalizer.java new file mode 100644 index 0000000000..4d30b425c1 --- /dev/null +++ b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceNormalizer.java @@ -0,0 +1,320 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://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. + */ +package opennlp.subword.sentencepiece; + +/** + * The model-embedded text normalizer of a SentencePiece model, operating in UTF-8 byte space. + * + *

Normalization applies the model's precompiled character map (leftmost-longest replacement + * rules over UTF-8 prefixes), collapses and trims whitespace, optionally prepends the + * word-boundary marker, and escapes spaces to U+2581. Alongside the normalized bytes it produces + * {@code normToOrig}, mapping every normalized byte to the offset of the original byte chunk it + * was derived from, with one trailing entry for the end position; that map is what lets every + * downstream piece report an exact span of the caller's text.

+ * + *

This mirrors the reference implementation's normalizer semantics rule for rule, since parity + * of both the normalized bytes and the offset map is what the tests assert.

+ */ +final class SentencePieceNormalizer { + + // U+2581 LOWER ONE EIGHTH BLOCK in UTF-8, the escaped form of a space. + static final byte[] SPACE_SYMBOL = {(byte) 0xE2, (byte) 0x96, (byte) 0x81}; + + // U+FFFD REPLACEMENT CHARACTER in UTF-8, emitted for a malformed byte. + private static final byte[] REPLACEMENT_CHAR = {(byte) 0xEF, (byte) 0xBF, (byte) 0xBD}; + + private final DoubleArrayTrie trie; + private final byte[] blob; + private final int replacementsFrom; + private final boolean addDummyPrefix; + private final boolean removeExtraWhitespaces; + private final boolean escapeWhitespaces; + private final boolean treatWhitespaceAsSuffix; + private final PieceTrie userDefinedMatcher; + + /** + * Instantiates the normalizer. + * + * @param precompiledCharsMap The serialized character map; empty when the model has none. + * @param addDummyPrefix Whether a word-boundary marker is prepended. + * @param removeExtraWhitespaces Whether leading, trailing, and repeated whitespace collapses. + * @param escapeWhitespaces Whether spaces become U+2581. + * @param treatWhitespaceAsSuffix Whether the dummy marker is appended instead of prepended. + * @param userDefinedMatcher Longest-match trie over user-defined symbols that must pass + * through normalization untouched, or null when the model + * defines none. + * @throws IllegalArgumentException Thrown if the character map is structurally invalid. + */ + SentencePieceNormalizer(byte[] precompiledCharsMap, boolean addDummyPrefix, + boolean removeExtraWhitespaces, boolean escapeWhitespaces, + boolean treatWhitespaceAsSuffix, PieceTrie userDefinedMatcher) { + if (precompiledCharsMap.length == 0) { + trie = null; + blob = null; + replacementsFrom = 0; + } else { + // Layout: . + if (precompiledCharsMap.length <= 4) { + throw new IllegalArgumentException("The precompiled character map is truncated."); + } + final long trieSize = (precompiledCharsMap[0] & 0xFFL) + | (precompiledCharsMap[1] & 0xFFL) << 8 + | (precompiledCharsMap[2] & 0xFFL) << 16 + | (precompiledCharsMap[3] & 0xFFL) << 24; + if (trieSize >= precompiledCharsMap.length - 4) { + throw new IllegalArgumentException( + "The precompiled character map declares a trie of " + trieSize + + " bytes but only " + (precompiledCharsMap.length - 4) + " bytes follow."); + } + if (trieSize < 1024 || (trieSize & 0x3FF) != 0) { + throw new IllegalArgumentException( + "The precompiled character map trie size " + trieSize + + " is not a positive multiple of 1024."); + } + if (precompiledCharsMap[precompiledCharsMap.length - 1] != 0) { + throw new IllegalArgumentException( + "The precompiled character map replacement block is not null-terminated."); + } + trie = new DoubleArrayTrie(precompiledCharsMap, 4, (int) trieSize); + blob = precompiledCharsMap; + replacementsFrom = 4 + (int) trieSize; + } + this.addDummyPrefix = addDummyPrefix; + this.removeExtraWhitespaces = removeExtraWhitespaces; + this.escapeWhitespaces = escapeWhitespaces; + this.treatWhitespaceAsSuffix = treatWhitespaceAsSuffix; + this.userDefinedMatcher = userDefinedMatcher; + } + + /** The normalized bytes plus the normalized-byte to original-byte offset map. */ + record Normalized(byte[] bytes, int[] normToOrig) { + } + + // One normalization step: `consumed` input bytes produced `data[from, to)`. The data array is + // the input itself (pass-through), the replacement blob, or the replacement character. + private record Chunk(byte[] data, int from, int to, int consumed) { + + boolean isSingleSpace() { + return to - from == 1 && data[from] == ' '; + } + } + + /** + * Normalizes UTF-8 input. + * + * @param input The well-formed UTF-8 bytes to normalize; must not be null. + * @return The normalized bytes with the offset map; {@code normToOrig.length} is always + * {@code bytes.length + 1}. + */ + Normalized normalize(byte[] input) { + final ByteBuilder normalized = new ByteBuilder(input.length + (input.length >> 1) + 4); + final IntBuilder normToOrig = new IntBuilder(input.length + (input.length >> 1) + 5); + + int from = 0; + int consumed = 0; + + // Ignores heading whitespace. + if (removeExtraWhitespaces) { + while (from < input.length) { + final Chunk p = normalizePrefix(input, from); + if (!p.isSingleSpace()) { + break; + } + from += p.consumed(); + consumed += p.consumed(); + } + } + + // All input was whitespace. + if (from >= input.length) { + return new Normalized(new byte[0], new int[] {consumed}); + } + + final byte[] spaceSymbol = escapeWhitespaces ? SPACE_SYMBOL : new byte[] {' '}; + + if (!treatWhitespaceAsSuffix && addDummyPrefix) { + appendSpace(normalized, normToOrig, spaceSymbol, consumed); + } + + boolean isPrevSpace = removeExtraWhitespaces; + while (from < input.length) { + final Chunk p = normalizePrefix(input, from); + int spFrom = p.from(); + final int spTo = p.to(); + final byte[] spData = p.data(); + + // Removes heading spaces in the chunk if the previous chunk ended with whitespace. + while (isPrevSpace && spFrom < spTo && spData[spFrom] == ' ') { + spFrom++; + } + + if (spFrom < spTo) { + for (int n = spFrom; n < spTo; n++) { + if (spData[n] == ' ') { + appendSpace(normalized, normToOrig, spaceSymbol, consumed); + } else { + normalized.append(spData[n]); + normToOrig.append(consumed); + } + } + isPrevSpace = spData[spTo - 1] == ' '; + } + + consumed += p.consumed(); + from += p.consumed(); + if (!removeExtraWhitespaces) { + isPrevSpace = false; + } + } + + // Ignores trailing whitespace. + if (removeExtraWhitespaces) { + while (normalized.endsWith(spaceSymbol)) { + final int length = normalized.length() - spaceSymbol.length; + consumed = normToOrig.get(length); + normalized.truncate(length); + normToOrig.truncate(length); + } + } + + if (treatWhitespaceAsSuffix && addDummyPrefix) { + appendSpace(normalized, normToOrig, spaceSymbol, consumed); + } + + normToOrig.append(consumed); + if (normToOrig.length() != normalized.length() + 1) { + throw new IllegalStateException("The offset map has " + normToOrig.length() + + " entries for " + normalized.length() + " normalized bytes."); + } + return new Normalized(normalized.toArray(), normToOrig.toArray()); + } + + private static void appendSpace(ByteBuilder normalized, IntBuilder normToOrig, + byte[] spaceSymbol, int consumed) { + normalized.append(spaceSymbol, 0, spaceSymbol.length); + for (int i = 0; i < spaceSymbol.length; i++) { + normToOrig.append(consumed); + } + } + + // Normalizes the longest applicable prefix of input[from, ...): a user-defined symbol passes + // through raw, otherwise the longest character-map rule applies, otherwise one code point + // passes through raw (or becomes U+FFFD when the lead byte is malformed). + private Chunk normalizePrefix(byte[] input, int from) { + if (userDefinedMatcher != null) { + final int matched = longestUserDefinedMatch(input, from); + if (matched > 0) { + return new Chunk(input, from, from + matched, matched); + } + } + + if (trie != null) { + final long match = trie.longestPrefixMatch(input, from, input.length); + if (match >= 0) { + final int value = (int) (match >>> 32); + final int length = (int) (match & 0xFFFFFFFFL); + final int replacementFrom = replacementsFrom + value; + if (replacementFrom < blob.length) { + int replacementTo = replacementFrom; + while (blob[replacementTo] != 0) { + replacementTo++; + } + return new Chunk(blob, replacementFrom, replacementTo, length); + } + } + } + + final int charLength = Math.min(utf8Length(input[from]), input.length - from); + if (isMalformed(input, from, charLength)) { + return new Chunk(REPLACEMENT_CHAR, 0, REPLACEMENT_CHAR.length, 1); + } + return new Chunk(input, from, from + charLength, charLength); + } + + private int longestUserDefinedMatch(byte[] input, int from) { + int node = userDefinedMatcher.root(); + int longest = 0; + for (int i = from; i < input.length; i++) { + node = userDefinedMatcher.step(node, input[i]); + if (node == PieceTrie.DEAD) { + break; + } + if (userDefinedMatcher.value(node) >= 0) { + longest = i - from + 1; + } + } + return longest; + } + + // The byte length of a UTF-8 sequence by its lead byte, as the reference implementation + // computes it: trail and malformed lead bytes report one byte. + static int utf8Length(byte lead) { + final int high = (lead & 0xFF) >>> 4; + if (high < 0xC) { + return 1; + } + return switch (high) { + case 0xC, 0xD -> 2; + case 0xE -> 3; + default -> 4; + }; + } + + // Checks a single code point for well-formedness: correct trail-byte count and no unpaired + // surrogate or out-of-range value. The public tokenizer API encodes its own well-formed UTF-8, + // so this only guards direct byte-level use. + private static boolean isMalformed(byte[] input, int from, int length) { + if ((input[from] & 0x80) == 0) { + return false; + } + if ((input[from] & 0xC0) == 0x80 || length < utf8Length(input[from])) { + return true; + } + for (int i = from + 1; i < from + length; i++) { + if ((input[i] & 0xC0) != 0x80) { + return true; + } + } + final int codePoint = codePointAt(input, from, length); + return codePoint < 0 || (codePoint >= 0xD800 && codePoint <= 0xDFFF) || codePoint > 0x10FFFF + || length != minimalUtf8Length(codePoint); + } + + private static int codePointAt(byte[] input, int from, int length) { + return switch (length) { + case 1 -> input[from] & 0x7F; + case 2 -> (input[from] & 0x1F) << 6 | (input[from + 1] & 0x3F); + case 3 -> (input[from] & 0x0F) << 12 | (input[from + 1] & 0x3F) << 6 + | (input[from + 2] & 0x3F); + default -> (input[from] & 0x07) << 18 | (input[from + 1] & 0x3F) << 12 + | (input[from + 2] & 0x3F) << 6 | (input[from + 3] & 0x3F); + }; + } + + private static int minimalUtf8Length(int codePoint) { + if (codePoint < 0x80) { + return 1; + } + if (codePoint < 0x800) { + return 2; + } + if (codePoint < 0x10000) { + return 3; + } + return 4; + } +} diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceTokenizer.java b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceTokenizer.java new file mode 100644 index 0000000000..12e8271d76 --- /dev/null +++ b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceTokenizer.java @@ -0,0 +1,509 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://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. + */ +package opennlp.subword.sentencepiece; + +import java.io.IOException; +import java.io.InputStream; +import java.nio.charset.StandardCharsets; +import java.nio.file.Files; +import java.nio.file.Path; +import java.util.ArrayList; +import java.util.HashMap; +import java.util.List; +import java.util.Map; + +import opennlp.subword.SubwordPiece; +import opennlp.subword.SubwordTokenizer; +import opennlp.tools.util.normalizer.AlignedText; +import opennlp.tools.util.normalizer.Alignment; +import opennlp.tools.util.normalizer.OffsetAwareNormalizer; + +/** + * A {@link SubwordTokenizer} over a trained SentencePiece model file, implemented purely in Java. + * + *

A {@code .model} file is self-contained: it carries the vocabulary with piece scores and + * types, the segmentation algorithm (unigram language model or byte-pair encoding), and the text + * normalizer the model was trained with. This class runs all three, so its output matches the + * reference implementation piece for piece and id for id, which is what makes the produced ids + * valid inputs for models trained against the same vocabulary.

+ * + *

Beyond parity, every piece carries the exact span of the caller's original text it came + * from, mapped back through the model's own normalizer. The normalizer is also exposed on its own + * through {@link OffsetAwareNormalizer}, so the model's text normalization can be reused as an + * offset-aware step outside of tokenization.

+ * + *

Instances are immutable after loading and safe for concurrent use by multiple threads.

+ */ +public final class SentencePieceTokenizer implements SubwordTokenizer, OffsetAwareNormalizer { + + /** The segmentation algorithm a model was trained with. */ + public enum Algorithm { + /** Unigram language model, decoded by best-path search. */ + UNIGRAM, + /** Byte-pair encoding, decoded by greedy highest-score merging. */ + BPE + } + + // Piece types of the model format. + private static final int TYPE_NORMAL = ModelProtoReader.RawModel.TYPE_NORMAL; + private static final int TYPE_UNKNOWN = ModelProtoReader.RawModel.TYPE_UNKNOWN; + private static final int TYPE_CONTROL = ModelProtoReader.RawModel.TYPE_CONTROL; + private static final int TYPE_USER_DEFINED = ModelProtoReader.RawModel.TYPE_USER_DEFINED; + private static final int TYPE_UNUSED = ModelProtoReader.RawModel.TYPE_UNUSED; + private static final int TYPE_BYTE = ModelProtoReader.RawModel.TYPE_BYTE; + + private static final int MAX_PIECE_LENGTH = 8000; + + private final Algorithm algorithm; + private final String[] pieces; + private final float[] scores; + private final int[] types; + private final int unkId; + private final boolean byteFallback; + private final Map mainPieces; + private final Map reservedPieces; + private final int[] byteToId; + private final SentencePieceNormalizer normalizer; + private final UnigramEncoder unigramEncoder; + private final BpeEncoder bpeEncoder; + private final List selfTestInputs; + private final List selfTestExpected; + + private SentencePieceTokenizer(ModelProtoReader.RawModel model) { + final int count = model.pieces.size(); + pieces = model.pieces.toArray(new String[0]); + scores = new float[count]; + types = new int[count]; + for (int i = 0; i < count; i++) { + scores[i] = model.scores.get(i); + types[i] = model.types.get(i); + } + byteFallback = model.byteFallback; + algorithm = switch (model.modelType) { + case ModelProtoReader.RawModel.MODEL_TYPE_UNIGRAM -> Algorithm.UNIGRAM; + case ModelProtoReader.RawModel.MODEL_TYPE_BPE -> Algorithm.BPE; + default -> throw new IllegalArgumentException( + "The model type " + model.modelType + " is not supported; only the unigram and BPE" + + " algorithms are."); + }; + + // Splits the vocabulary the way the reference does: pieces of the normal, user-defined, and + // unused types participate in segmentation, all others are reserved ids. + mainPieces = new HashMap<>(count * 2); + reservedPieces = new HashMap<>(); + final List userDefined = new ArrayList<>(); + byteToId = new int[256]; + java.util.Arrays.fill(byteToId, -1); + int foundUnkId = -1; + float minScore = Float.MAX_VALUE; + for (int i = 0; i < count; i++) { + final String piece = pieces[i]; + if (piece.length() >= MAX_PIECE_LENGTH) { + throw new IllegalArgumentException("The piece with id " + i + " is longer than " + + MAX_PIECE_LENGTH + " characters."); + } + if (piece.indexOf(0) >= 0) { + throw new IllegalArgumentException( + "The piece with id " + i + " contains a null character."); + } + final boolean isMain = + types[i] == TYPE_NORMAL || types[i] == TYPE_USER_DEFINED || types[i] == TYPE_UNUSED; + final Map target = + isMain || algorithm == Algorithm.BPE ? mainPieces : reservedPieces; + if (mainPieces.containsKey(piece) || reservedPieces.containsKey(piece)) { + throw new IllegalArgumentException("The piece '" + piece + "' is defined more than once."); + } + target.put(piece, i); + switch (types[i]) { + case TYPE_NORMAL -> minScore = Math.min(minScore, scores[i]); + case TYPE_USER_DEFINED -> userDefined.add(piece); + case TYPE_UNKNOWN -> { + if (foundUnkId >= 0) { + throw new IllegalArgumentException("The model defines more than one unknown piece."); + } + foundUnkId = i; + } + case TYPE_BYTE -> { + if (!byteFallback) { + throw new IllegalArgumentException("The model defines the byte piece '" + piece + + "' although byte fallback is disabled."); + } + final int b = parseBytePiece(piece); + if (b < 0) { + throw new IllegalArgumentException("The byte piece '" + piece + "' is invalid."); + } + byteToId[b] = i; + } + default -> { + // CONTROL and UNUSED need no bookkeeping here. + } + } + } + if (foundUnkId < 0) { + throw new IllegalArgumentException("The model defines no unknown piece."); + } + unkId = foundUnkId; + if (byteFallback) { + for (int b = 0; b < 256; b++) { + if (byteToId[b] < 0) { + throw new IllegalArgumentException("The model enables byte fallback but defines no" + + " piece for byte " + b + "."); + } + } + } + + final PieceTrie userDefinedMatcher = userDefined.isEmpty() ? null : trieOf(userDefined, id -> 0); + + normalizer = new SentencePieceNormalizer(model.precompiledCharsMap, model.addDummyPrefix, + model.removeExtraWhitespaces, model.escapeWhitespaces, model.treatWhitespaceAsSuffix, + userDefinedMatcher); + + final boolean[] unusedFlags = new boolean[count]; + final boolean[] userDefinedFlags = new boolean[count]; + final boolean[] reservedFlags = new boolean[count]; + for (int i = 0; i < count; i++) { + unusedFlags[i] = types[i] == TYPE_UNUSED; + userDefinedFlags[i] = types[i] == TYPE_USER_DEFINED; + reservedFlags[i] = types[i] != TYPE_NORMAL && types[i] != TYPE_USER_DEFINED + && types[i] != TYPE_UNUSED; + } + + if (algorithm == Algorithm.UNIGRAM) { + final List mainList = new ArrayList<>(mainPieces.size()); + final List mainIds = new ArrayList<>(mainPieces.size()); + for (int i = 0; i < count; i++) { + if (!reservedFlags[i]) { + mainList.add(pieces[i]); + mainIds.add(i); + } + } + final PieceTrie vocabulary = trieOf(mainList, mainIds::get); + unigramEncoder = new UnigramEncoder(vocabulary, scores, unusedFlags, userDefinedFlags, + minScore, unkId); + bpeEncoder = null; + } else { + unigramEncoder = null; + bpeEncoder = new BpeEncoder(mainPieces, scores, unusedFlags, reservedFlags, unkId, + userDefinedMatcher); + } + + selfTestInputs = List.copyOf(model.selfTestInputs); + selfTestExpected = List.copyOf(model.selfTestExpected); + } + + private static PieceTrie trieOf(List pieceList, + java.util.function.IntUnaryOperator idOf) { + final byte[][] keys = new byte[pieceList.size()][]; + final int[] ids = new int[pieceList.size()]; + for (int i = 0; i < keys.length; i++) { + keys[i] = pieceList.get(i).getBytes(StandardCharsets.UTF_8); + ids[i] = idOf.applyAsInt(i); + } + return PieceTrie.build(keys, ids); + } + + /** + * Loads a model from a file. + * + * @param modelFile The {@code .model} file to load; must not be null. + * @return The ready-to-use tokenizer. + * @throws IOException Thrown if the file cannot be read. + * @throws IllegalArgumentException Thrown if the file is not a valid model. + */ + public static SentencePieceTokenizer load(Path modelFile) throws IOException { + if (modelFile == null) { + throw new IllegalArgumentException("The model file must not be null."); + } + return new SentencePieceTokenizer(ModelProtoReader.read(Files.readAllBytes(modelFile))); + } + + /** + * Loads a model from a stream. The stream is read fully but not closed. + * + * @param in The stream positioned at the start of a {@code .model} serialization; must not be + * null. + * @return The ready-to-use tokenizer. + * @throws IOException Thrown if the stream cannot be read. + * @throws IllegalArgumentException Thrown if the bytes are not a valid model. + */ + public static SentencePieceTokenizer load(InputStream in) throws IOException { + if (in == null) { + throw new IllegalArgumentException("The input stream must not be null."); + } + return new SentencePieceTokenizer(ModelProtoReader.read(in.readAllBytes())); + } + + @Override + public List encode(CharSequence text) { + if (text == null) { + throw new IllegalArgumentException("The text must not be null."); + } + final Utf8Text input = Utf8Text.of(text); + final SentencePieceNormalizer.Normalized normalized = normalizer.normalize(input.bytes()); + final List segments = algorithm == Algorithm.UNIGRAM + ? unigramEncoder.encode(normalized.bytes()) + : bpeEncoder.encode(normalized.bytes()); + + final List out = new ArrayList<>(segments.size()); + final byte[] norm = normalized.bytes(); + final int[] normToOrig = normalized.normToOrig(); + + // Accumulates a run of adjacent unknown pieces into one, as the reference does, so a decoder + // sees a single unknown token per unknown region. + StringBuilder pendingUnk = null; + int pendingUnkStart = 0; + int pendingUnkEnd = 0; + + for (final Segment segment : segments) { + final String piece = new String(norm, segment.from(), segment.to() - segment.from(), + StandardCharsets.UTF_8); + final boolean isUnk = segment.id() == unkId; + final boolean isControl = types[segment.id()] == TYPE_CONTROL; + + if (isControl) { + if (pendingUnk != null) { + out.add(new SubwordPiece(pendingUnk.toString(), unkId, pendingUnkStart, pendingUnkEnd)); + pendingUnk = null; + } + final int at = input.charOffset(normToOrig[segment.from()]); + out.add(new SubwordPiece(piece, segment.id(), at, at)); + continue; + } + + final int origBegin = input.charOffset(normToOrig[segment.from()]); + final int origEnd = input.charOffset(normToOrig[segment.to()]); + + if (isUnk && byteFallback) { + if (pendingUnk != null) { + out.add(new SubwordPiece(pendingUnk.toString(), unkId, pendingUnkStart, pendingUnkEnd)); + pendingUnk = null; + } + // Decomposes the unknown region into byte pieces; the last one carries the surface span. + for (int i = segment.from(); i < segment.to(); i++) { + final int b = norm[i] & 0xFF; + final boolean last = i == segment.to() - 1; + out.add(new SubwordPiece(BYTE_PIECES[b], byteToId[b], origBegin, + last ? origEnd : origBegin)); + } + } else if (isUnk) { + if (pendingUnk == null) { + pendingUnk = new StringBuilder(piece); + pendingUnkStart = origBegin; + } else { + pendingUnk.append(piece); + } + pendingUnkEnd = origEnd; + } else { + if (pendingUnk != null) { + out.add(new SubwordPiece(pendingUnk.toString(), unkId, pendingUnkStart, pendingUnkEnd)); + pendingUnk = null; + } + out.add(new SubwordPiece(piece, segment.id(), origBegin, origEnd)); + } + } + if (pendingUnk != null) { + out.add(new SubwordPiece(pendingUnk.toString(), unkId, pendingUnkStart, pendingUnkEnd)); + } + return out; + } + + @Override + public CharSequence normalize(CharSequence text) { + return normalizeAligned(text).normalized(); + } + + @Override + public AlignedText normalizeAligned(CharSequence text) { + if (text == null) { + throw new IllegalArgumentException("The text must not be null."); + } + final Utf8Text input = Utf8Text.of(text); + final SentencePieceNormalizer.Normalized result = normalizer.normalize(input.bytes()); + final String normalized = new String(result.bytes(), StandardCharsets.UTF_8); + final int[] normToOrig = result.normToOrig(); + final byte[] norm = result.bytes(); + + // Walks the normalized code points, grouping neighbors that came from the same original + // block into one replace run; gaps between blocks are deletions. + final Alignment.Builder builder = new Alignment.Builder(); + int cursor = 0; + int groupOrigStart = -1; + int groupOrigEnd = -1; + int groupChars = 0; + int b = 0; + while (b < norm.length) { + final int byteLength = Math.min(SentencePieceNormalizer.utf8Length(norm[b]), + norm.length - b); + final int origStart = input.charOffset(normToOrig[b]); + final int origEnd = input.charOffset(normToOrig[b + byteLength]); + final int chars = byteLength == 4 ? 2 : 1; + if (groupChars > 0 && origStart == groupOrigStart && origEnd == groupOrigEnd) { + groupChars += chars; + } else { + cursor = flushGroup(builder, cursor, groupOrigStart, groupOrigEnd, groupChars); + groupOrigStart = origStart; + groupOrigEnd = origEnd; + groupChars = chars; + } + b += byteLength; + } + cursor = flushGroup(builder, cursor, groupOrigStart, groupOrigEnd, groupChars); + if (cursor < input.charLength()) { + builder.replace(input.charLength() - cursor, 0); + } + return new AlignedText(text, normalized, builder.build(input.charLength())); + } + + private static int flushGroup(Alignment.Builder builder, int cursor, int origStart, int origEnd, + int chars) { + if (chars == 0) { + return cursor; + } + if (origStart > cursor) { + builder.replace(origStart - cursor, 0); + } + builder.replace(origEnd - Math.max(origStart, cursor), chars); + return Math.max(origEnd, cursor); + } + + /** {@return the segmentation algorithm of the loaded model} */ + public Algorithm algorithm() { + return algorithm; + } + + /** {@return the number of pieces in the vocabulary} */ + public int vocabularySize() { + return pieces.length; + } + + /** + * Returns the piece string of an id. + * + * @param id A vocabulary id in {@code [0, vocabularySize())}. + * @return The piece string. + * @throws IllegalArgumentException Thrown if {@code id} is out of range. + */ + public String idToPiece(int id) { + checkId(id); + return pieces[id]; + } + + /** + * Returns the id of a piece string. + * + * @param piece The piece to look up; must not be null. + * @return The id, or the unknown id when the vocabulary does not contain the piece. + */ + public int pieceToId(String piece) { + if (piece == null) { + throw new IllegalArgumentException("The piece must not be null."); + } + final Integer reserved = reservedPieces.get(piece); + if (reserved != null) { + return reserved; + } + return mainPieces.getOrDefault(piece, unkId); + } + + /** + * Returns the score of a piece. + * + * @param id A vocabulary id in {@code [0, vocabularySize())}. + * @return The score; a log-probability for unigram models, a merge rank for BPE models. + * @throws IllegalArgumentException Thrown if {@code id} is out of range. + */ + public float score(int id) { + checkId(id); + return scores[id]; + } + + /** {@return the id of the unknown piece} */ + public int unknownId() { + return unkId; + } + + /** + * Checks whether an id is the unknown piece. + * + * @param id A vocabulary id in {@code [0, vocabularySize())}. + * @return {@code true} for the unknown piece. + * @throws IllegalArgumentException Thrown if {@code id} is out of range. + */ + public boolean isUnknown(int id) { + checkId(id); + return types[id] == TYPE_UNKNOWN; + } + + /** + * Checks whether an id is a control piece. + * + * @param id A vocabulary id in {@code [0, vocabularySize())}. + * @return {@code true} for control pieces. + * @throws IllegalArgumentException Thrown if {@code id} is out of range. + */ + public boolean isControl(int id) { + checkId(id); + return types[id] == TYPE_CONTROL; + } + + /** + * Checks whether an id is a byte-fallback piece. + * + * @param id A vocabulary id in {@code [0, vocabularySize())}. + * @return {@code true} for byte pieces. + * @throws IllegalArgumentException Thrown if {@code id} is out of range. + */ + public boolean isByte(int id) { + checkId(id); + return types[id] == TYPE_BYTE; + } + + private void checkId(int id) { + if (id < 0 || id >= pieces.length) { + throw new IllegalArgumentException( + "The id " + id + " is outside [0, " + pieces.length + ")."); + } + } + + // The embedded self-test samples, exposed for the parity tests. + List selfTestInputs() { + return selfTestInputs; + } + + List selfTestExpected() { + return selfTestExpected; + } + + // "<0xAB>" piece strings for all byte values, as byte fallback emits them. + private static final String[] BYTE_PIECES = new String[256]; + + static { + final char[] hex = "0123456789ABCDEF".toCharArray(); + for (int b = 0; b < 256; b++) { + BYTE_PIECES[b] = "<0x" + hex[b >>> 4] + hex[b & 0xF] + ">"; + } + } + + private static int parseBytePiece(String piece) { + if (piece.length() != 6 || !piece.startsWith("<0x") || piece.charAt(5) != '>') { + return -1; + } + final int high = Character.digit(piece.charAt(3), 16); + final int low = Character.digit(piece.charAt(4), 16); + return high < 0 || low < 0 ? -1 : (high << 4) | low; + } +} diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/UnigramEncoder.java b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/UnigramEncoder.java new file mode 100644 index 0000000000..a53ca70740 --- /dev/null +++ b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/UnigramEncoder.java @@ -0,0 +1,163 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://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. + */ +package opennlp.subword.sentencepiece; + +import java.util.ArrayList; +import java.util.Collections; +import java.util.List; + +/** + * Viterbi segmentation under a unigram language model: of all ways to cover the normalized text + * with vocabulary pieces, it finds the one with the highest total log-probability. + * + *

This is a port of the reference implementation's optimized single-pass decoder, which + * exploits the unigram independence assumption to keep only the best path ending at each byte + * position instead of a full lattice. Characters no piece covers fall back to the unknown id + * with a fixed penalty below the lowest piece score, and user-defined symbols receive a + * length-based bonus score so they always win. Tie-breaking and score arithmetic follow the + * reference exactly, including its occasional re-basing of accumulated scores on very long + * inputs, because segmentation parity is asserted against it.

+ */ +final class UnigramEncoder { + + private static final float UNK_PENALTY = 10.0f; + private static final float SCORE_RESET_THRESHOLD = 100000.0f; + + private final PieceTrie trie; + private final float[] scores; + private final boolean[] unused; + private final boolean[] userDefined; + private final float unkScore; + private final int unkId; + + /** + * Instantiates the encoder. + * + * @param trie The vocabulary trie over all matchable pieces. + * @param scores The log-probability score of every piece, indexed by id. + * @param unused Whether each id has the unused piece type. + * @param userDefined Whether each id is a user-defined symbol. + * @param minScore The lowest score among normal pieces. + * @param unkId The id of the unknown piece. + */ + UnigramEncoder(PieceTrie trie, float[] scores, boolean[] unused, boolean[] userDefined, + float minScore, int unkId) { + this.trie = trie; + this.scores = scores; + this.unused = unused; + this.userDefined = userDefined; + this.unkScore = minScore - UNK_PENALTY; + this.unkId = unkId; + } + + /** + * Segments normalized text. + * + * @param normalized The normalized UTF-8 bytes; must not be null. + * @return The best-path segments covering all bytes, in text order. + */ + List encode(byte[] normalized) { + final int size = normalized.length; + if (size == 0) { + return List.of(); + } + + // The best path ending at each byte position (exclusive end). + final int[] bestStartsAt = new int[size + 1]; + final float[] bestScore = new float[size + 1]; + final int[] bestId = new int[size + 1]; + java.util.Arrays.fill(bestStartsAt, -1); + + int startsAt = 0; + int maxFrontier = 0; + while (startsAt < size) { + float bestScoreTillHere = bestScore[startsAt]; + if (bestScoreTillHere < -SCORE_RESET_THRESHOLD + || bestScoreTillHere > SCORE_RESET_THRESHOLD) { + // Re-bases accumulated scores to keep float precision on very long inputs; every + // reachable frontier position shifts by the same offset, so the argmax is unchanged. + final float offset = bestScoreTillHere; + for (int i = startsAt; i <= maxFrontier; i++) { + if (i == startsAt || bestStartsAt[i] != -1) { + bestScore[i] -= offset; + } + } + bestScoreTillHere = 0.0f; + } + + boolean hasSingleNode = false; + final int mblen = Math.min(SentencePieceNormalizer.utf8Length(normalized[startsAt]), + size - startsAt); + + int node = trie.root(); + for (int keyPos = startsAt; keyPos < size; ) { + node = trie.step(node, normalized[keyPos]); + if (node == PieceTrie.DEAD) { + break; + } + keyPos++; + final int id = trie.value(node); + if (id < 0) { + continue; + } + if (unused[id]) { + continue; + } + maxFrontier = Math.max(maxFrontier, keyPos); + final int length = keyPos - startsAt; + // User-defined symbols receive a length bonus instead of a trained score. + final float score = userDefined[id] ? 0.1f * (length - 1) : scores[id]; + final float candidate = score + bestScoreTillHere; + if (bestStartsAt[keyPos] == -1 || candidate > bestScore[keyPos]) { + bestScore[keyPos] = candidate; + bestStartsAt[keyPos] = startsAt; + bestId[keyPos] = id; + } + if (!hasSingleNode && length == mblen) { + hasSingleNode = true; + } + } + + if (!hasSingleNode) { + final int end = startsAt + mblen; + maxFrontier = Math.max(maxFrontier, end); + final float candidate = unkScore + bestScoreTillHere; + if (bestStartsAt[end] == -1 || candidate > bestScore[end]) { + bestScore[end] = candidate; + bestStartsAt[end] = startsAt; + bestId[end] = unkId; + } + } + + startsAt += mblen; + } + + final List results = new ArrayList<>(size / 4 + 1); + int endsAt = size; + while (endsAt > 0) { + final int from = bestStartsAt[endsAt]; + if (from < 0) { + throw new IllegalStateException( + "The Viterbi path is broken at normalized byte " + endsAt + "."); + } + results.add(new Segment(from, endsAt, bestId[endsAt])); + endsAt = from; + } + Collections.reverse(results); + return results; + } +} diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/Utf8Text.java b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/Utf8Text.java new file mode 100644 index 0000000000..e331fdcc83 --- /dev/null +++ b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/Utf8Text.java @@ -0,0 +1,109 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://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. + */ +package opennlp.subword.sentencepiece; + +/** + * A caller's text encoded as UTF-8, keeping the map from every byte offset back to the UTF-16 + * offset it came from. + * + *

The whole pipeline runs in byte space to match the reference implementation, but the spans + * reported to the caller must be UTF-16 offsets into the original {@code CharSequence}; this map + * converts them. An unpaired surrogate, which UTF-8 cannot represent, is encoded as U+FFFD, kept + * deterministic so that parity fixtures can cover it.

+ */ +final class Utf8Text { + + private final byte[] bytes; + private final int[] byteToChar; + private final int charLength; + + private Utf8Text(byte[] bytes, int[] byteToChar, int charLength) { + this.bytes = bytes; + this.byteToChar = byteToChar; + this.charLength = charLength; + } + + /** + * Encodes text. + * + * @param text The text to encode; must not be null. + * @return The encoded view. + */ + static Utf8Text of(CharSequence text) { + final int charLength = text.length(); + final byte[] bytes = new byte[charLength * 3 + 1]; + final int[] byteToChar = new int[charLength * 3 + 2]; + int b = 0; + int c = 0; + while (c < charLength) { + int codePoint = text.charAt(c); + int charCount = 1; + if (Character.isHighSurrogate((char) codePoint) && c + 1 < charLength + && Character.isLowSurrogate(text.charAt(c + 1))) { + codePoint = Character.toCodePoint((char) codePoint, text.charAt(c + 1)); + charCount = 2; + } else if (Character.isSurrogate((char) codePoint)) { + // An unpaired surrogate has no UTF-8 form; U+FFFD keeps the encoding total. + codePoint = 0xFFFD; + } + final int start = b; + if (codePoint < 0x80) { + bytes[b++] = (byte) codePoint; + } else if (codePoint < 0x800) { + bytes[b++] = (byte) (0xC0 | codePoint >>> 6); + bytes[b++] = (byte) (0x80 | codePoint & 0x3F); + } else if (codePoint < 0x10000) { + bytes[b++] = (byte) (0xE0 | codePoint >>> 12); + bytes[b++] = (byte) (0x80 | codePoint >>> 6 & 0x3F); + bytes[b++] = (byte) (0x80 | codePoint & 0x3F); + } else { + bytes[b++] = (byte) (0xF0 | codePoint >>> 18); + bytes[b++] = (byte) (0x80 | codePoint >>> 12 & 0x3F); + bytes[b++] = (byte) (0x80 | codePoint >>> 6 & 0x3F); + bytes[b++] = (byte) (0x80 | codePoint & 0x3F); + } + for (int i = start; i < b; i++) { + byteToChar[i] = c; + } + c += charCount; + } + byteToChar[b] = charLength; + final byte[] exact = java.util.Arrays.copyOf(bytes, b); + final int[] exactMap = java.util.Arrays.copyOf(byteToChar, b + 1); + return new Utf8Text(exact, exactMap, charLength); + } + + /** {@return the UTF-8 bytes} */ + byte[] bytes() { + return bytes; + } + + /** {@return the length of the original text in UTF-16 units} */ + int charLength() { + return charLength; + } + + /** + * Maps a byte offset to the UTF-16 offset of the character containing it. + * + * @param byteOffset An offset in {@code [0, bytes().length]}. + * @return The UTF-16 offset; the text length for the end offset. + */ + int charOffset(int byteOffset) { + return byteToChar[byteOffset]; + } +} diff --git a/opennlp-extensions/opennlp-subword/src/test/java/opennlp/subword/sentencepiece/SentencePieceAlignmentTest.java b/opennlp-extensions/opennlp-subword/src/test/java/opennlp/subword/sentencepiece/SentencePieceAlignmentTest.java new file mode 100644 index 0000000000..02815aa372 --- /dev/null +++ b/opennlp-extensions/opennlp-subword/src/test/java/opennlp/subword/sentencepiece/SentencePieceAlignmentTest.java @@ -0,0 +1,145 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://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. + */ +package opennlp.subword.sentencepiece; + +import java.util.List; + +import org.junit.jupiter.api.Test; +import org.junit.jupiter.params.ParameterizedTest; +import org.junit.jupiter.params.provider.ValueSource; + +import opennlp.subword.SubwordPiece; +import opennlp.tools.util.Span; +import opennlp.tools.util.normalizer.AlignedText; + +import static org.junit.jupiter.api.Assertions.assertEquals; +import static org.junit.jupiter.api.Assertions.assertThrows; +import static org.junit.jupiter.api.Assertions.assertTrue; + +/** + * Exercises the {@code OffsetAwareNormalizer} view: the model normalizer's output must align + * back to the original text exactly, including through whitespace collapsing, character-map + * replacements, and supplementary characters. + */ +class SentencePieceAlignmentTest { + + private static SentencePieceTokenizer unigram() { + return SentencePieceParityTest.tokenizer("tiny-unigram"); + } + + @ParameterizedTest + @ValueSource(strings = {"Hello world", " Hello world ", "Hello world.\nSecond line", + "3.14159 x 42", "family emoji", " leading and trailing ", "a", "", " "}) + void testAlignedMatchesUnaligned(String input) { + final AlignedText aligned = unigram().normalizeAligned(input); + assertEquals(unigram().normalize(input).toString(), aligned.normalizedString()); + assertEquals(input, aligned.original().toString()); + assertEquals(aligned.normalizedString().length(), aligned.alignment().normalizedLength()); + assertEquals(input.length(), aligned.alignment().originalLength()); + } + + @Test + void testWordMapsBackThroughCollapsedWhitespace() { + final String input = " Hello world "; + final AlignedText aligned = unigram().normalizeAligned(input); + final String normalized = aligned.normalizedString(); + + final int at = normalized.indexOf("world"); + final Span original = aligned.toOriginalSpan(at, at + "world".length()); + assertEquals("world", input.substring(original.getStart(), original.getEnd())); + } + + @Test + void testLigatureReplacementMapsToItsSourceCharacter() { + // The character map expands the single ligature to two letters; both normalized letters + // must map back to the one original character. + final String input = cp(0xFB01) + "nancial"; + final AlignedText aligned = unigram().normalizeAligned(input); + final String normalized = aligned.normalizedString(); + + final int at = normalized.indexOf("fi"); + assertTrue(at >= 0, "the character map must expand the ligature, got " + normalized); + final Span original = aligned.toOriginalSpan(at, at + 2); + assertEquals(0, original.getStart()); + assertEquals(1, original.getEnd()); + } + + @Test + void testSupplementaryCharacterSpansUseUtf16Units() { + final String input = "I love " + new String(Character.toChars(0x1F355)) + " pizza"; + final List pieces = unigram().encode(input); + + SubwordPiece pizzaSlice = null; + for (final SubwordPiece piece : pieces) { + if (piece.piece().contains(new String(Character.toChars(0x1F355)))) { + pizzaSlice = piece; + } + } + assertTrue(pizzaSlice != null, "the emoji must surface as a piece, got " + pieces); + assertEquals(7, pizzaSlice.start()); + assertEquals(9, pizzaSlice.end()); + assertEquals(new String(Character.toChars(0x1F355)), + input.substring(pizzaSlice.start(), pizzaSlice.end())); + } + + @Test + void testEverySpanIsWithinTheOriginalText() { + final String input = "quotes " + cp(0x201C) + "fancy" + cp(0x201D) + " and " + + cp(0x2018) + "single" + cp(0x2019) + " " + cp(0x2014) + " dash"; + for (final SubwordPiece piece : unigram().encode(input)) { + assertTrue(piece.start() >= 0 && piece.end() <= input.length(), + "span " + piece + " must lie inside the input"); + assertTrue(piece.start() <= piece.end(), "span " + piece + " must not be inverted"); + } + } + + @Test + void testSpansAreMonotonicAndAdjacent() { + final String input = "The quick brown fox jumps over the lazy dog."; + int previousEnd = 0; + for (final SubwordPiece piece : unigram().encode(input)) { + assertTrue(piece.start() >= previousEnd || piece.start() == piece.end(), + "piece " + piece + " must not step back before " + previousEnd); + previousEnd = Math.max(previousEnd, piece.end()); + } + assertEquals(input.length(), previousEnd, "the last span must reach the end of the input"); + } + + @Test + void testUnpairedSurrogateIsDeterministic() { + final String input = "a" + (char) 0xD83C + "b"; + final List first = unigram().encode(input); + final List second = unigram().encode(input); + assertEquals(first, second); + int covered = 0; + for (final SubwordPiece piece : first) { + covered = Math.max(covered, piece.end()); + } + assertEquals(input.length(), covered); + } + + private static String cp(int codePoint) { + return new String(Character.toChars(codePoint)); + } + + @Test + void testNullInputsFailLoudly() { + assertThrows(IllegalArgumentException.class, () -> unigram().encode(null)); + assertThrows(IllegalArgumentException.class, () -> unigram().normalizeAligned(null)); + assertThrows(IllegalArgumentException.class, () -> unigram().normalize(null)); + } +} diff --git a/opennlp-extensions/opennlp-subword/src/test/java/opennlp/subword/sentencepiece/SentencePieceModelValidationTest.java b/opennlp-extensions/opennlp-subword/src/test/java/opennlp/subword/sentencepiece/SentencePieceModelValidationTest.java new file mode 100644 index 0000000000..8f0b6c64a9 --- /dev/null +++ b/opennlp-extensions/opennlp-subword/src/test/java/opennlp/subword/sentencepiece/SentencePieceModelValidationTest.java @@ -0,0 +1,183 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://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. + */ +package opennlp.subword.sentencepiece; + +import java.io.ByteArrayInputStream; +import java.io.ByteArrayOutputStream; +import java.io.IOException; +import java.io.InputStream; +import java.nio.charset.StandardCharsets; +import java.util.ArrayList; +import java.util.List; +import java.util.concurrent.Callable; +import java.util.concurrent.ExecutorService; +import java.util.concurrent.Executors; +import java.util.concurrent.Future; + +import org.junit.jupiter.api.Test; + +import opennlp.subword.SubwordPiece; + +import static org.junit.jupiter.api.Assertions.assertEquals; +import static org.junit.jupiter.api.Assertions.assertThrows; +import static org.junit.jupiter.api.Assertions.assertTrue; + +/** + * Fail-loud behavior on malformed models, plus the concurrency guarantee: one loaded tokenizer + * must produce identical results from many threads. + */ +class SentencePieceModelValidationTest { + + @Test + void testNullAndEmptyInputFailLoudly() { + assertThrows(IllegalArgumentException.class, + () -> SentencePieceTokenizer.load((java.nio.file.Path) null)); + assertThrows(IllegalArgumentException.class, + () -> SentencePieceTokenizer.load((InputStream) null)); + assertThrows(IllegalArgumentException.class, + () -> SentencePieceTokenizer.load(new ByteArrayInputStream(new byte[0]))); + } + + @Test + void testGarbageBytesFailLoudly() { + final byte[] garbage = "this is not a model file at all".getBytes(StandardCharsets.UTF_8); + assertThrows(IllegalArgumentException.class, + () -> SentencePieceTokenizer.load(new ByteArrayInputStream(garbage))); + } + + @Test + void testTruncatedModelFailsLoudly() throws IOException { + final byte[] whole = readModel(); + final byte[] truncated = java.util.Arrays.copyOf(whole, whole.length / 3); + assertThrows(IllegalArgumentException.class, + () -> SentencePieceTokenizer.load(new ByteArrayInputStream(truncated))); + } + + @Test + void testUnsupportedModelTypeFailsLoudly() { + // A minimal well-formed model claiming the WORD algorithm (model_type = 3). + final byte[] model = minimalModel(3); + final IllegalArgumentException e = assertThrows(IllegalArgumentException.class, + () -> SentencePieceTokenizer.load(new ByteArrayInputStream(model))); + assertTrue(e.getMessage().contains("not supported"), e.getMessage()); + } + + @Test + void testMissingUnknownPieceFailsLoudly() { + final byte[] model = minimalModelWithoutUnk(); + final IllegalArgumentException e = assertThrows(IllegalArgumentException.class, + () -> SentencePieceTokenizer.load(new ByteArrayInputStream(model))); + assertTrue(e.getMessage().contains("unknown piece"), e.getMessage()); + } + + @Test + void testConcurrentEncodingIsConsistent() throws Exception { + final SentencePieceTokenizer tokenizer = SentencePieceParityTest.tokenizer("tiny-unigram"); + final String[] inputs = { + "The quick brown fox jumps over the lazy dog.", + "tokenization and segmentation", + " Hello world ", + "water running walked faster apple book work play"}; + final List> expected = new ArrayList<>(); + for (final String input : inputs) { + expected.add(tokenizer.encode(input)); + } + + final ExecutorService pool = Executors.newFixedThreadPool(8); + try { + final List> futures = new ArrayList<>(); + for (int t = 0; t < 8; t++) { + futures.add(pool.submit((Callable) () -> { + for (int round = 0; round < 500; round++) { + for (int i = 0; i < inputs.length; i++) { + if (!expected.get(i).equals(tokenizer.encode(inputs[i]))) { + return false; + } + } + } + return true; + })); + } + for (final Future future : futures) { + assertTrue(future.get(), "concurrent encoding must match single-threaded results"); + } + } finally { + pool.shutdownNow(); + } + } + + @Test + void testVocabularyAccessors() { + final SentencePieceTokenizer tokenizer = SentencePieceParityTest.tokenizer("tiny-unigram"); + assertEquals(300, tokenizer.vocabularySize()); + assertEquals(SentencePieceTokenizer.Algorithm.UNIGRAM, tokenizer.algorithm()); + for (int id = 0; id < tokenizer.vocabularySize(); id++) { + final String piece = tokenizer.idToPiece(id); + if (!tokenizer.isUnknown(id) && !tokenizer.isControl(id)) { + assertEquals(id, tokenizer.pieceToId(piece), "round trip of piece '" + piece + "'"); + } + } + assertEquals(tokenizer.unknownId(), tokenizer.pieceToId("definitely-not-in-the-vocabulary")); + assertThrows(IllegalArgumentException.class, () -> tokenizer.idToPiece(-1)); + assertThrows(IllegalArgumentException.class, + () -> tokenizer.idToPiece(tokenizer.vocabularySize())); + assertThrows(IllegalArgumentException.class, () -> tokenizer.pieceToId(null)); + } + + private static byte[] readModel() throws IOException { + try (InputStream in = + SentencePieceModelValidationTest.class.getResourceAsStream("tiny-unigram.model")) { + return in.readAllBytes(); + } + } + + // Hand-encodes a minimal ModelProto: three pieces (, , ) and a trainer spec with + // the requested model type. + private static byte[] minimalModel(int modelType) { + final ByteArrayOutputStream out = new ByteArrayOutputStream(); + writePiece(out, "", 2); + writePiece(out, "", 3); + writePiece(out, "", 3); + writePiece(out, "a", 1); + // trainer_spec { model_type = } + out.write(0x12); + out.write(2); + out.write(0x18); + out.write(modelType); + return out.toByteArray(); + } + + private static byte[] minimalModelWithoutUnk() { + final ByteArrayOutputStream out = new ByteArrayOutputStream(); + writePiece(out, "a", 1); + writePiece(out, "b", 1); + return out.toByteArray(); + } + + private static void writePiece(ByteArrayOutputStream out, String piece, int type) { + final byte[] utf8 = piece.getBytes(StandardCharsets.UTF_8); + // pieces { piece = ; score = 0.0; type = } as nested length-delimited field 1. + final int inner = 2 + utf8.length + 2; + out.write(0x0A); + out.write(inner); + out.write(0x0A); + out.write(utf8.length); + out.writeBytes(utf8); + out.write(0x18); + out.write(type); + } +} diff --git a/opennlp-extensions/opennlp-subword/src/test/java/opennlp/subword/sentencepiece/SentencePieceParityTest.java b/opennlp-extensions/opennlp-subword/src/test/java/opennlp/subword/sentencepiece/SentencePieceParityTest.java new file mode 100644 index 0000000000..4485e13f16 --- /dev/null +++ b/opennlp-extensions/opennlp-subword/src/test/java/opennlp/subword/sentencepiece/SentencePieceParityTest.java @@ -0,0 +1,150 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://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. + */ +package opennlp.subword.sentencepiece; + +import java.io.BufferedReader; +import java.io.IOException; +import java.io.InputStream; +import java.io.InputStreamReader; +import java.nio.charset.StandardCharsets; +import java.util.ArrayList; +import java.util.List; +import java.util.Map; +import java.util.StringJoiner; +import java.util.concurrent.ConcurrentHashMap; + +import org.junit.jupiter.params.ParameterizedTest; +import org.junit.jupiter.params.provider.ValueSource; + +import opennlp.subword.SubwordPiece; + +import static org.junit.jupiter.api.Assertions.assertEquals; +import static org.junit.jupiter.api.Assertions.assertNotNull; +import static org.junit.jupiter.api.Assertions.assertTrue; + +/** + * Asserts exact parity with the reference implementation: for every fixture input, the pieces, + * ids, original-text spans, and the normalized form must equal what the reference produced for + * the same bundled model. The fixtures were generated by {@code gen_fixtures.tsv}'s sibling + * script (see the test resources) against the sentencepiece Python package. + */ +class SentencePieceParityTest { + + private static final Map LOADED = new ConcurrentHashMap<>(); + + static SentencePieceTokenizer tokenizer(String model) { + return LOADED.computeIfAbsent(model, name -> { + try (InputStream in = SentencePieceParityTest.class.getResourceAsStream(name + ".model")) { + assertNotNull(in, "missing test resource " + name + ".model"); + return SentencePieceTokenizer.load(in); + } catch (IOException e) { + throw new IllegalStateException(e); + } + }); + } + + @ParameterizedTest + @ValueSource(strings = {"tiny-unigram", "tiny-unigram-bytefb", "tiny-bpe", + "tiny-unigram-identity", "tiny-unigram-suffix"}) + void testFixtureParity(String model) throws IOException { + final SentencePieceTokenizer tokenizer = tokenizer(model); + int lines = 0; + for (final Fixture fixture : fixtures(model)) { + lines++; + final List actual = tokenizer.encode(fixture.input); + final String context = model + " input <" + fixture.input + ">"; + assertEquals(fixture.pieces.size(), actual.size(), + context + " piece count; got " + actual); + for (int i = 0; i < actual.size(); i++) { + final SubwordPiece expected = fixture.pieces.get(i); + final SubwordPiece got = actual.get(i); + assertEquals(expected.piece(), got.piece(), context + " piece " + i); + assertEquals(expected.id(), got.id(), context + " id of piece " + i); + assertEquals(expected.start(), got.start(), context + " start of piece " + i); + assertEquals(expected.end(), got.end(), context + " end of piece " + i); + } + assertEquals(fixture.normalized, tokenizer.normalize(fixture.input).toString(), + context + " normalized form"); + } + assertTrue(lines >= 30, "the fixture file must not be empty or truncated"); + } + + @ParameterizedTest + @ValueSource(strings = {"tiny-unigram", "tiny-unigram-bytefb", "tiny-bpe", + "tiny-unigram-identity", "tiny-unigram-suffix"}) + void testEmbeddedSelfTestSamples(String model) { + final SentencePieceTokenizer tokenizer = tokenizer(model); + final List inputs = tokenizer.selfTestInputs(); + final List expected = tokenizer.selfTestExpected(); + assertTrue(!inputs.isEmpty(), "the tiny models embed self-test samples"); + for (int i = 0; i < inputs.size(); i++) { + final StringJoiner joined = new StringJoiner(" "); + for (final String piece : tokenizer.encodeToPieces(inputs.get(i))) { + joined.add(piece); + } + assertEquals(expected.get(i), joined.toString(), + model + " self-test sample <" + inputs.get(i) + ">"); + } + } + + private record Fixture(String input, List pieces, String normalized) { + } + + private static List fixtures(String model) throws IOException { + final List fixtures = new ArrayList<>(); + try (InputStream in = + SentencePieceParityTest.class.getResourceAsStream(model + ".fixtures.tsv")) { + assertNotNull(in, "missing test resource " + model + ".fixtures.tsv"); + final BufferedReader reader = + new BufferedReader(new InputStreamReader(in, StandardCharsets.UTF_8)); + String line; + while ((line = reader.readLine()) != null) { + final String[] cols = line.split("\t", -1); + final String input = unescape(cols[0]); + final int count = Integer.parseInt(cols[1]); + final List pieces = new ArrayList<>(count); + for (int i = 0; i < count; i++) { + pieces.add(new SubwordPiece(unescape(cols[2 + i * 4]), + Integer.parseInt(cols[3 + i * 4]), Integer.parseInt(cols[4 + i * 4]), + Integer.parseInt(cols[5 + i * 4]))); + } + fixtures.add(new Fixture(input, pieces, unescape(cols[2 + count * 4]))); + } + } + return fixtures; + } + + private static String unescape(String s) { + final StringBuilder out = new StringBuilder(s.length()); + for (int i = 0; i < s.length(); i++) { + final char c = s.charAt(i); + if (c == '\\' && i + 1 < s.length()) { + i++; + switch (s.charAt(i)) { + case 't' -> out.append('\t'); + case 'n' -> out.append('\n'); + case 'r' -> out.append('\r'); + case '\\' -> out.append('\\'); + default -> throw new IllegalArgumentException("bad escape in fixture: " + s); + } + } else { + out.append(c); + } + } + return out.toString(); + } +} diff --git a/opennlp-extensions/opennlp-subword/src/test/java/opennlp/subword/sentencepiece/SentencePieceRealModelEvalTest.java b/opennlp-extensions/opennlp-subword/src/test/java/opennlp/subword/sentencepiece/SentencePieceRealModelEvalTest.java new file mode 100644 index 0000000000..e4acccd499 --- /dev/null +++ b/opennlp-extensions/opennlp-subword/src/test/java/opennlp/subword/sentencepiece/SentencePieceRealModelEvalTest.java @@ -0,0 +1,112 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://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. + */ +package opennlp.subword.sentencepiece; + +import java.io.BufferedReader; +import java.io.IOException; +import java.nio.charset.StandardCharsets; +import java.nio.file.Files; +import java.nio.file.Path; +import java.util.List; +import java.util.stream.Stream; + +import org.junit.jupiter.api.Test; + +import opennlp.subword.SubwordPiece; + +import static org.junit.jupiter.api.Assertions.assertEquals; +import static org.junit.jupiter.api.Assertions.assertTrue; +import static org.junit.jupiter.api.Assumptions.assumeTrue; + +/** + * Opt-in parity check against real pre-trained models, which are downloaded rather than bundled. + * + *

Point {@code -Dopennlp.subword.eval.dir} at a directory holding {@code .model} files + * with sibling {@code .fixtures.tsv} files generated by the {@code gen_real_fixtures.py} + * script from the test resources; every model found is asserted piece for piece. Without the + * property the test is skipped.

+ */ +class SentencePieceRealModelEvalTest { + + @Test + void testRealModelParity() throws IOException { + final String dir = System.getProperty("opennlp.subword.eval.dir"); + assumeTrue(dir != null && !dir.isBlank(), + "set -Dopennlp.subword.eval.dir to run the real-model parity check"); + + int models = 0; + try (Stream files = Files.list(Path.of(dir))) { + for (final Path model : files.filter(f -> f.toString().endsWith(".model")).sorted() + .toList()) { + final Path fixtures = Path.of(model.toString() + .substring(0, model.toString().length() - ".model".length()) + ".fixtures.tsv"); + assumeTrue(Files.exists(fixtures), "no fixtures for " + model.getFileName()); + models++; + assertModel(model, fixtures); + } + } + assertTrue(models > 0, "the eval directory contains no models"); + } + + private static void assertModel(Path modelPath, Path fixturesPath) throws IOException { + final SentencePieceTokenizer tokenizer = SentencePieceTokenizer.load(modelPath); + int lines = 0; + try (BufferedReader reader = Files.newBufferedReader(fixturesPath, StandardCharsets.UTF_8)) { + String line; + while ((line = reader.readLine()) != null) { + lines++; + final String[] cols = line.split("\t", -1); + final String input = unescape(cols[0]); + final int count = Integer.parseInt(cols[1]); + final String context = modelPath.getFileName() + " input <" + input + ">"; + + final List actual = tokenizer.encode(input); + assertEquals(count, actual.size(), context + " piece count; got " + actual); + for (int i = 0; i < count; i++) { + final SubwordPiece got = actual.get(i); + assertEquals(unescape(cols[2 + i * 4]), got.piece(), context + " piece " + i); + assertEquals(Integer.parseInt(cols[3 + i * 4]), got.id(), context + " id " + i); + assertEquals(Integer.parseInt(cols[4 + i * 4]), got.start(), context + " start " + i); + assertEquals(Integer.parseInt(cols[5 + i * 4]), got.end(), context + " end " + i); + } + assertEquals(unescape(cols[2 + count * 4]), tokenizer.normalize(input).toString(), + context + " normalized form"); + } + } + assertTrue(lines >= 30, modelPath.getFileName() + " fixtures must not be truncated"); + } + + private static String unescape(String s) { + final StringBuilder out = new StringBuilder(s.length()); + for (int i = 0; i < s.length(); i++) { + final char c = s.charAt(i); + if (c == '\\' && i + 1 < s.length()) { + i++; + switch (s.charAt(i)) { + case 't' -> out.append('\t'); + case 'n' -> out.append('\n'); + case 'r' -> out.append('\r'); + case '\\' -> out.append('\\'); + default -> throw new IllegalArgumentException("bad escape in fixture: " + s); + } + } else { + out.append(c); + } + } + return out.toString(); + } +} diff --git a/opennlp-extensions/opennlp-subword/src/test/resources/opennlp/subword/sentencepiece/corpus.txt b/opennlp-extensions/opennlp-subword/src/test/resources/opennlp/subword/sentencepiece/corpus.txt new file mode 100644 index 0000000000..d578654315 --- /dev/null +++ b/opennlp-extensions/opennlp-subword/src/test/resources/opennlp/subword/sentencepiece/corpus.txt @@ -0,0 +1,66 @@ +The quick brown fox jumps over the lazy dog. +Apache OpenNLP is a machine learning based toolkit for the processing of natural language text. +It supports the most common NLP tasks, such as tokenization, sentence segmentation, and named entity extraction. +Subword tokenization decomposes words into smaller units drawn from a fixed vocabulary. +The unigram language model selects the segmentation with the highest total log probability. +Byte pair encoding merges the most frequent adjacent symbol pairs until no merge applies. +A sentence piece model carries its own text normalizer inside the model file. +Character offsets should always point back into the original text. +Whitespace is escaped with a special marker so word boundaries survive segmentation. +Numbers like 3.14159 and 42 and 1024 appear in ordinary text. +Punctuation, quotes, and dashes are folded by the normalizer! +Questions? Answers! Ellipses... and (parentheses) too. +The cafe served naive patrons a souffle with creme fraiche. +Internationalization and localization are long words. +Antidisestablishmentarianism remains one of the longest English words. +She sells seashells by the seashore. +Peter Piper picked a peck of pickled peppers. +How much wood would a woodchuck chuck if a woodchuck could chuck wood? +The rain in Spain stays mainly in the plain. +To be or not to be, that is the question. +All happy families are alike; each unhappy family is unhappy in its own way. +It was the best of times, it was the worst of times. +Call me Ishmael. +In the beginning was the word. +The world is everything that is the case. +Language models assign probabilities to sequences of tokens. +Retrieval systems rank documents by similarity to a query. +Embeddings map text into dense vector spaces. +Search engines combine lexical and semantic signals. +The tokenizer must be fast, deterministic, and thread safe. +Model files are loaded once and shared across threads. +Tests must prove parity with the reference implementation. +Offsets are measured in code units of the original encoding. +The normalizer collapses repeated whitespace into one marker. +A leading marker separates words that start a sentence. +Unknown characters fall back to a penalty score. +Byte fallback decomposes unknown characters into byte pieces. +User defined symbols are never split by the tokenizer. +Control symbols never appear in encoded output. +The vocabulary maps each piece to an integer identifier. +Scores are log probabilities in the unigram model. +Merge ranks order the byte pair encoding agenda. +The trie enumerates every piece that starts at a position. +Dynamic programming finds the best path in one pass. +Backtracking recovers the winning segmentation. +The agenda is a priority queue ordered by score. +Stale entries are skipped when their symbols have merged. +Un texto corto en espanol para variar el corpus. +Un petit texte en francais pour la diversite. +Ein kurzer deutscher Satz steht auch hier. +Ancora una frase italiana per completezza. +Tokenization quality depends on the training corpus. +The model was trained on a tiny corpus for testing only. +Nothing in this file is quoted from any external work. +Short lines help. +One. +Two words. +Three little words. +water water water water water +running runner ran runs +walked walking walker walks +faster fastest fast +apple apples applesauce +book books bookshelf bookstore +work works worked working worker +play plays played playing player diff --git a/opennlp-extensions/opennlp-subword/src/test/resources/opennlp/subword/sentencepiece/gen_fixtures.py b/opennlp-extensions/opennlp-subword/src/test/resources/opennlp/subword/sentencepiece/gen_fixtures.py new file mode 100644 index 0000000000..658902e48f --- /dev/null +++ b/opennlp-extensions/opennlp-subword/src/test/resources/opennlp/subword/sentencepiece/gen_fixtures.py @@ -0,0 +1,139 @@ +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://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. + +"""Trains the tiny SentencePiece test models and generates the parity fixtures. + +Run inside a venv with the sentencepiece package installed: + python gen_fixtures.py + +Every fixture line is tab-separated with backslash escaping (\\\\, \\t, \\n, \\r): + esc(input) TAB pieceCount TAB [esc(piece) TAB id TAB begin TAB end]... TAB esc(normalized) +Offsets are UTF-16 code-unit offsets into the original input, matching Java string indexing. +""" +import sys +import sentencepiece as spm + +MULTILINGUAL = [ + "Le café coûte trois euros à Paris.", + "Der Straßenname ändert sich häufig.", + "Ça va très bien, merci beaucoup.", + "El niño pequeño come una manzana.", + "Привет мир и всем добро.", + "東京タワーに登りました。", + "日本語の文章も少しあります。", + "안녕하세요 세계입니다.", + "你好世界这是中文。", + "I love \U0001f355 and \U0001f1e9\U0001f1ea a lot!", + "Emoji test \U0001f600 \U0001f680 ❤️ done.", +] + +INPUTS = [ + "", + " ", + " ", + "a", + "Hello world", + " Hello world ", + "Hello world.\nSecond line\ttabbed", + "The quick brown fox jumps over the lazy dog.", + "tokenization and segmentation", + "Antidisestablishmentarianism", + "water running walked faster apple book work play", + "3.14159 x 42 = 1024?", + "!!!???...", + "(parentheses) and [brackets] and {braces}", + "café naïve fiancé résumé", + "financial fluid", + "① ⑪ ㋿ ＫＡＴＡＫＡＮＡ", + "ｶﾀｶﾅ half width", + "東京タワーへ行きました", + "日本語とEnglish混在", + "Привет мир", + "안녕하세요 세계", + "你好，世界！", + "I love \U0001f355 pizza", + "flags \U0001f1e9\U0001f1ea \U0001f1fa\U0001f1f8 end", + "family \U0001f469‍\U0001f469‍\U0001f467‍\U0001f466 emoji", + "zero​width and non breaking", + "quotes “fancy” and ‘single’ — dash", + " the [URL] token", + "a b[URL]c", + "control tokens inline", + "https://example.com/path?q=1&x=2", + "UPPER lower MiXeD case", + "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa", + "Ω≈ç√∫˜µ≤", + "مرحبا بالعالم", + " leading and trailing ", + "\ttab\tstart", + "newline\n\n\nruns", + "mid spaces collapse", +] + +MODELS = { + "tiny-unigram": dict(model_type="unigram", vocab_size=300), + "tiny-unigram-bytefb": dict(model_type="unigram", vocab_size=600, byte_fallback=True, + character_coverage=0.995), + "tiny-bpe": dict(model_type="bpe", vocab_size=300), + "tiny-unigram-identity": dict(model_type="unigram", vocab_size=300, + normalization_rule_name="identity"), + "tiny-unigram-suffix": dict(model_type="unigram", vocab_size=300, + treat_whitespace_as_suffix=True), +} + + +def esc(s): + return (s.replace("\\", "\\\\").replace("\t", "\\t") + .replace("\n", "\\n").replace("\r", "\\r")) + + +def utf16_offset(text, codepoint_offset): + return len(text[:codepoint_offset].encode("utf-16-le")) // 2 + + +def main(corpus, outdir): + full_corpus = outdir + "/corpus-full.txt" + with open(corpus, encoding="utf-8") as f: + lines = f.read().splitlines() + lines += MULTILINGUAL + with open(full_corpus, "w", encoding="utf-8") as f: + f.write("\n".join(lines) + "\n") + + for name, opts in MODELS.items(): + spm.SentencePieceTrainer.Train( + input=full_corpus, + model_prefix=outdir + "/" + name, + hard_vocab_limit=False, + character_coverage=opts.pop("character_coverage", 1.0), + user_defined_symbols=["", "[URL]"], + self_test_sample_size=10, + **opts, + ) + sp = spm.SentencePieceProcessor(model_file=outdir + "/" + name + ".model") + with open(outdir + "/" + name + ".fixtures.tsv", "w", encoding="utf-8") as out: + for text in INPUTS: + proto = sp.EncodeAsImmutableProto(text) + cols = [esc(text), str(len(proto.pieces))] + for piece in proto.pieces: + cols += [esc(piece.piece), str(piece.id), + str(utf16_offset(text, piece.begin)), + str(utf16_offset(text, piece.end))] + cols.append(esc(sp.Normalize(text))) + out.write("\t".join(cols) + "\n") + print(name, "vocab", sp.GetPieceSize()) + + +if __name__ == "__main__": + main(sys.argv[1], sys.argv[2]) diff --git a/opennlp-extensions/opennlp-subword/src/test/resources/opennlp/subword/sentencepiece/gen_real_fixtures.py b/opennlp-extensions/opennlp-subword/src/test/resources/opennlp/subword/sentencepiece/gen_real_fixtures.py new file mode 100644 index 0000000000..9b27c7a11e --- /dev/null +++ b/opennlp-extensions/opennlp-subword/src/test/resources/opennlp/subword/sentencepiece/gen_real_fixtures.py @@ -0,0 +1,75 @@ +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://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. + +"""Generates parity fixtures for pre-trained real-world models (no training). + +Usage: python gen_real_fixtures.py +Reads every *.model in the directory and writes a sibling *.fixtures.tsv in the same +escaped-TSV format as gen_fixtures.py, over a larger and messier input list. +""" +import glob +import os +import sys +import sentencepiece as spm +from gen_fixtures import INPUTS, esc, utf16_offset + +EXTRA = [ + "The Transformer architecture revolutionized natural language processing in 2017.", + "supercalifragilisticexpialidocious and pneumonoultramicroscopicsilicovolcanoconiosis", + "e=mc^2, F=ma, and a^2+b^2=c^2 are famous equations.", + "Mixed scripts: English, 日本語, 한국어, русский, and العربية together.", + "Prices: $19.99, €25,50, £12, ¥1500, and ₹999.", + "C++ and C# and F# are programming languages; so is Java.", + "def encode(text): return sp.encode(text, out_type=str)", + "SELECT * FROM documents WHERE score > 0.5 ORDER BY rank;", + "The 2024 Summer Olympics were held in Paris, France.", + "COVID-19 vaccines use mRNA technology (Pfizer-BioNTech, Moderna).", + "Email me at test.user+tag@example.co.uk or call +1 (555) 010-9999.", + "10,000 steps a day keeps the doctor away... allegedly!", + "The naive resume of the fiancee included a cafe visit.", + "¿Dónde está la biblioteca? ¡Allí está!", + "Smørrebrød og æbleskiver er danske specialiteter.", + "Zażółć gęślą jaźń is a Polish pangram.", + "Đây là tiếng Việt với nhiều dấu.", + "今日はいい天気ですね。明日も晴れるといいな。", + "北京和上海都是大城市。", + "한국의 수도는 서울입니다.", + "\U0001f9d1‍\U0001f4bb codes while \U0001f9d1‍\U0001f373 cooks \U0001f35c!", + "
line separator and 
paragraph separator lurk here", + "BOM at the start of this sentence", + "tabs\tand\ttabs\tand\ttabs", + "CRLF\r\nline endings\r\nhappen", +] + + +def main(model_dir): + for model_path in sorted(glob.glob(os.path.join(model_dir, "*.model"))): + name = os.path.splitext(model_path)[0] + sp = spm.SentencePieceProcessor(model_file=model_path) + with open(name + ".fixtures.tsv", "w", encoding="utf-8") as out: + for text in INPUTS + EXTRA: + proto = sp.EncodeAsImmutableProto(text) + cols = [esc(text), str(len(proto.pieces))] + for piece in proto.pieces: + cols += [esc(piece.piece), str(piece.id), + str(utf16_offset(text, piece.begin)), + str(utf16_offset(text, piece.end))] + cols.append(esc(sp.Normalize(text))) + out.write("\t".join(cols) + "\n") + print(os.path.basename(name), "vocab", sp.GetPieceSize()) + + +if __name__ == "__main__": + main(sys.argv[1]) diff --git a/opennlp-extensions/opennlp-subword/src/test/resources/opennlp/subword/sentencepiece/tiny-bpe.fixtures.tsv b/opennlp-extensions/opennlp-subword/src/test/resources/opennlp/subword/sentencepiece/tiny-bpe.fixtures.tsv new file mode 100644 index 0000000000..e3560148f9 --- /dev/null +++ b/opennlp-extensions/opennlp-subword/src/test/resources/opennlp/subword/sentencepiece/tiny-bpe.fixtures.tsv @@ -0,0 +1,40 @@ + 0 + 0 + 0 +a 1 ▁a 8 0 1 ▁a +Hello world 7 ▁ 177 0 0 H 246 0 1 el 48 1 3 l 186 3 4 o 183 4 5 ▁wor 38 5 9 ld 129 9 11 ▁Hello▁world + Hello world 7 ▁ 177 1 1 H 246 1 2 el 48 2 4 l 186 4 5 o 183 5 6 ▁wor 38 6 12 ld 129 12 14 ▁Hello▁world +Hello world.\nSecond line\ttabbed 19 ▁ 177 0 0 H 246 0 1 el 48 1 3 l 186 3 4 o 183 4 5 ▁wor 38 5 9 ld 129 9 11 . 193 11 12 ▁S 64 12 14 ec 51 14 16 on 16 16 18 d 189 18 19 ▁l 28 19 21 in 7 21 23 e 178 23 24 ▁t 5 24 26 ab 85 26 28 b 199 28 29 ed 24 29 31 ▁Hello▁world.▁Second▁line▁tabbed +The quick brown fox jumps over the lazy dog. 27 ▁The 50 0 3 ▁qu 81 3 6 ic 61 6 8 k 196 8 9 ▁b 23 9 11 ro 41 11 13 wn 90 13 15 ▁f 25 15 17 o 183 17 18 x 203 18 19 ▁ 177 19 20 j 220 20 21 u 192 21 22 mp 108 22 24 s 182 24 25 ▁o 45 25 27 v 200 27 28 er 6 28 30 ▁the 19 30 34 ▁l 28 34 36 a 179 36 37 z 202 37 38 y 198 38 39 ▁d 42 39 41 o 183 41 42 g 194 42 43 . 193 43 44 ▁The▁quick▁brown▁fox▁jumps▁over▁the▁lazy▁dog. +tokenization and segmentation 4 ▁tokeniz 170 0 7 ation 47 7 12 ▁and 59 12 16 ▁segmentation 171 16 29 ▁tokenization▁and▁segmentation +Antidisestablishmentarianism 16 ▁A 76 0 1 n 181 1 2 t 180 2 3 id 176 3 5 is 31 5 7 est 57 7 10 ab 85 10 12 l 186 12 13 is 31 13 15 h 187 15 16 ment 83 16 20 ar 18 20 22 i 184 22 23 an 40 23 25 is 31 25 27 m 191 27 28 ▁Antidisestablishmentarianism +water running walked faster apple book work play 15 ▁water 124 0 5 ▁r 93 5 7 un 39 7 9 ning 154 9 13 ▁wal 160 13 17 k 196 17 18 ed 24 18 20 ▁fast 162 20 25 er 6 25 27 ▁app 99 27 31 le 107 31 33 ▁boo 155 33 37 k 196 37 38 ▁work 167 38 43 ▁play 123 43 48 ▁water▁running▁walked▁faster▁apple▁book▁work▁play +3.14159 x 42 = 1024? 21 ▁ 177 0 0 3 242 0 1 . 193 1 2 1 215 2 3 4 216 3 4 1 215 4 5 5 243 5 6 9 244 6 7 ▁ 177 7 8 x 203 8 9 ▁ 177 9 10 4 216 10 11 2 224 11 12 ▁ 177 12 13 = 0 13 14 ▁ 177 14 15 1 215 15 16 0 241 16 17 2 224 17 18 4 216 18 19 ? 225 19 20 ▁3.14159▁x▁42▁=▁1024? +!!!???... 10 ▁ 177 0 0 ! 214 0 1 ! 214 1 2 ! 214 2 3 ? 225 3 4 ? 225 4 5 ? 225 5 6 . 193 6 7 . 193 7 8 . 193 8 9 ▁!!!???... +(parentheses) and [brackets] and {braces} 25 ▁ 177 0 0 ( 239 0 1 p 190 1 2 ar 18 2 4 ent 35 4 7 he 9 7 9 ses 114 9 12 ) 240 12 13 ▁and 59 13 17 ▁ 177 17 18 [ 0 18 19 b 199 19 20 r 185 20 21 ack 112 21 24 et 86 24 26 s 182 26 27 ] 0 27 28 ▁and 59 28 32 ▁ 177 32 33 { 0 33 34 b 199 34 35 r 185 35 36 ac 29 36 38 es 11 38 40 } 0 40 41 ▁(parentheses)▁and▁[brackets]▁and▁{braces} +café naïve fiancé résumé 18 ▁c 26 0 1 af 173 1 3 é 254 3 4 ▁n 49 4 6 a 179 6 7 ï 0 7 8 ve 109 8 10 ▁f 25 10 12 i 184 12 13 an 40 13 15 c 188 15 16 é 254 16 17 ▁r 93 17 19 é 254 19 20 s 182 20 21 u 192 21 22 m 191 22 23 é 254 23 24 ▁café▁naïve▁fiancé▁résumé +financial fluid 10 ▁f 25 0 0 in 7 0 2 an 40 2 4 c 188 4 5 i 184 5 6 al 20 6 8 ▁f 25 8 9 l 186 9 10 u 192 10 11 id 176 11 13 ▁financial▁fluid +① ⑪ ㋿ ＫＡＴＡＫＡＮＡ 16 ▁ 177 0 0 1 215 0 1 ▁ 177 1 2 1 215 2 2 1 215 2 3 ▁ 177 3 4 令和 0 4 5 ▁ 177 5 6 K 0 6 7 A 207 7 8 T 201 8 9 A 207 9 10 K 0 10 11 A 207 11 12 N 212 12 13 A 207 13 14 ▁1▁11▁令和▁KATAKANA +ｶﾀｶﾅ half width 11 ▁ 177 0 0 カ 0 0 1 タ 268 1 2 カナ 0 2 4 ▁h 110 4 6 al 20 6 8 f 197 8 9 ▁w 14 9 11 id 176 11 13 t 180 13 14 h 187 14 15 ▁カタカナ▁half▁width +東京タワーへ行きました 10 ▁ 177 0 0 東 280 0 1 京 273 1 2 タ 268 2 3 ワ 269 3 4 ー 270 4 5 へ行き 0 5 8 ま 235 8 9 し 234 9 10 た 264 10 11 ▁東京タワーへ行きました +日本語とEnglish混在 12 ▁ 177 0 0 日 277 0 1 本 279 1 2 語 284 2 3 と 0 3 4 E 208 4 5 n 181 5 6 g 194 6 7 l 186 7 8 is 31 8 10 h 187 10 11 混在 0 11 13 ▁日本語とEnglish混在 +Привет мир 11 ▁ 177 0 0 П 256 0 1 р 222 1 2 и 221 2 3 в 230 3 4 е 231 4 5 т 260 5 6 ▁ 177 6 7 м 232 7 8 и 221 8 9 р 222 9 10 ▁Привет▁мир +안녕하세요 세계 9 ▁ 177 0 0 안 290 0 1 녕 287 1 2 하 293 2 3 세 238 3 4 요 291 4 5 ▁ 177 5 6 세 238 6 7 계 286 7 8 ▁안녕하세요▁세계 +你好，世界！ 7 ▁ 177 0 0 你 274 0 1 好 275 1 2 , 204 2 3 世 271 3 4 界 281 4 5 ! 214 5 6 ▁你好,世界! +I love 🍕 pizza 9 ▁I 92 0 1 ▁lo 96 1 4 ve 109 4 6 ▁ 177 6 7 🍕 297 7 9 ▁p 15 9 11 iz 54 11 13 z 202 13 14 a 179 14 15 ▁I▁love▁🍕▁pizza +flags 🇩🇪 🇺🇸 end 12 ▁f 25 0 1 l 186 1 2 a 179 2 3 g 194 3 4 s 182 4 5 ▁ 177 5 6 🇩 295 6 8 🇪 296 8 10 ▁ 177 10 11 🇺🇸 0 11 15 ▁en 149 15 18 d 189 18 19 ▁flags▁🇩🇪▁🇺🇸▁end +family 👩‍👩‍👧‍👦 emoji 12 ▁f 25 0 1 am 104 1 3 il 53 3 5 y 198 5 6 ▁ 177 6 7 👩‍👩‍👧‍👦 0 7 18 ▁ 177 18 19 e 178 19 20 m 191 20 21 o 183 21 22 j 220 22 23 i 184 23 24 ▁family▁👩‍👩‍👧‍👦▁emoji +zero​width and non breaking 16 ▁ 177 0 0 z 202 0 1 er 6 1 3 o 183 3 4 ▁w 14 4 6 id 176 6 8 t 180 8 9 h 187 9 10 ▁and 59 10 14 ▁n 49 14 16 on 16 16 18 ▁b 23 18 20 re 32 20 22 a 179 22 23 k 196 23 24 ing 27 24 27 ▁zero▁width▁and▁non▁breaking +quotes “fancy” and ‘single’ — dash 22 ▁qu 81 0 2 ot 130 2 4 es 11 4 6 ▁ 177 6 7 “ 0 7 8 f 197 8 9 an 40 9 11 c 188 11 12 y 198 12 13 ” 0 13 14 ▁and 59 14 18 ▁ 177 18 19 ‘ 0 19 20 s 182 20 21 ing 27 21 24 le 107 24 26 ’ 0 26 27 ▁ 177 27 28 — 0 28 29 ▁d 42 29 31 as 30 31 33 h 187 33 34 ▁quotes▁“fancy”▁and▁‘single’▁—▁dash + the [URL] token 7 ▁ 177 0 0 3 0 6 ▁the 19 6 10 ▁ 177 10 11 [URL] 4 11 16 ▁to 43 16 19 ken 95 19 22 ▁▁the▁[URL]▁token +a b[URL]c 6 ▁a 8 0 1 ▁ 177 1 2 3 2 8 b 199 8 9 [URL] 4 9 14 c 188 14 15 ▁a▁b[URL]c +control tokens inline 20 ▁c 26 0 1 on 16 1 3 t 180 3 4 ro 41 4 6 l 186 6 7 ▁ 177 7 8 < 0 8 9 s 182 9 10 > 0 10 11 ▁to 43 11 14 ken 95 14 17 s 182 17 18 ▁ 177 18 19 0 22 23 ▁in 37 23 26 l 186 26 27 in 7 27 29 e 178 29 30 ▁control▁▁tokens▁▁inline +https://example.com/path?q=1&x=2 26 ▁h 110 0 1 t 180 1 2 t 180 2 3 p 190 3 4 s 182 4 5 :// 0 5 8 ex 52 8 10 am 104 10 12 p 190 12 13 le 107 13 15 . 193 15 16 c 188 16 17 o 183 17 18 m 191 18 19 / 0 19 20 p 190 20 21 at 17 21 23 h 187 23 24 ? 225 24 25 q 205 25 26 = 0 26 27 1 215 27 28 & 0 28 29 x 203 29 30 = 0 30 31 2 224 31 32 ▁https://example.com/path?q=1&x=2 +UPPER lower MiXeD case 17 ▁U 135 0 1 P 210 1 2 P 210 2 3 E 208 3 4 R 248 4 5 ▁lo 96 5 8 w 195 8 9 er 6 9 11 ▁ 177 11 12 M 227 12 13 i 184 13 14 X 0 14 15 e 178 15 16 D 226 16 17 ▁c 26 17 19 as 30 19 21 e 178 21 22 ▁UPPER▁lower▁MiXeD▁case +aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa 40 ▁a 8 0 1 a 179 1 2 a 179 2 3 a 179 3 4 a 179 4 5 a 179 5 6 a 179 6 7 a 179 7 8 a 179 8 9 a 179 9 10 a 179 10 11 a 179 11 12 a 179 12 13 a 179 13 14 a 179 14 15 a 179 15 16 a 179 16 17 a 179 17 18 a 179 18 19 a 179 19 20 a 179 20 21 a 179 21 22 a 179 22 23 a 179 23 24 a 179 24 25 a 179 25 26 a 179 26 27 a 179 27 28 a 179 28 29 a 179 29 30 a 179 30 31 a 179 31 32 a 179 32 33 a 179 33 34 a 179 34 35 a 179 35 36 a 179 36 37 a 179 37 38 a 179 38 39 a 179 39 40 ▁aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa +Ω≈ç√∫˜µ≤ 4 ▁ 177 0 0 Ω≈ç√∫ 0 0 5 ▁ 177 5 5 ̃μ≤ 0 5 8 ▁Ω≈ç√∫▁̃μ≤ +مرحبا بالعالم 4 ▁ 177 0 0 مرحبا 0 0 5 ▁ 177 5 6 بالعالم 0 6 13 ▁مرحبا▁بالعالم + leading and trailing 9 ▁l 28 2 3 e 178 3 4 ad 172 4 6 ing 27 6 9 ▁and 59 9 13 ▁t 5 13 15 ra 62 15 17 il 53 17 19 ing 27 19 22 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a/opennlp-extensions/opennlp-subword/src/test/resources/opennlp/subword/sentencepiece/tiny-unigram-bytefb.fixtures.tsv b/opennlp-extensions/opennlp-subword/src/test/resources/opennlp/subword/sentencepiece/tiny-unigram-bytefb.fixtures.tsv new file mode 100644 index 0000000000..b8f9733dba --- /dev/null +++ b/opennlp-extensions/opennlp-subword/src/test/resources/opennlp/subword/sentencepiece/tiny-unigram-bytefb.fixtures.tsv @@ -0,0 +1,40 @@ + 0 + 0 + 0 +a 1 ▁a 266 0 1 ▁a +Hello world 7 ▁ 261 0 0 H 552 0 1 e 264 1 2 ll 412 2 4 o 274 4 5 ▁wor 427 5 9 ld 317 9 11 ▁Hello▁world + Hello world 7 ▁ 261 1 1 H 552 1 2 e 264 2 3 ll 412 3 5 o 274 5 6 ▁wor 427 6 12 ld 317 12 14 ▁Hello▁world +Hello world.\nSecond line\ttabbed 21 ▁ 261 0 0 H 552 0 1 e 264 1 2 ll 412 2 4 o 274 4 5 ▁wor 427 5 9 ld 317 9 11 . 262 11 12 ▁S 296 12 14 e 264 14 15 c 411 15 16 o 274 16 17 nd 284 17 19 ▁l 492 19 21 ine 415 21 24 ▁ 261 24 25 t 269 25 26 a 279 26 27 b 344 27 28 b 344 28 29 ed 267 29 31 ▁Hello▁world.▁Second▁line▁tabbed +The quick brown fox jumps over the lazy dog. 26 ▁Th 277 0 2 e 264 2 3 ▁qu 346 3 6 i 282 6 7 ck 307 7 9 ▁b 309 9 11 r 288 11 12 ow 306 12 14 n 268 14 15 ▁fo 359 15 18 x 421 18 19 ▁ 261 19 20 j 381 20 21 um 529 21 23 p 352 23 24 s 263 24 25 ▁o 513 25 27 ve 366 27 29 r 288 29 30 ▁the 265 30 34 ▁la 339 34 37 z 351 37 38 y 275 38 39 ▁do 464 39 42 g 356 42 43 . 262 43 44 ▁The▁quick▁brown▁fox▁jumps▁over▁the▁lazy▁dog. +tokenization and segmentation 4 ▁tokenization 461 0 12 ▁a 266 12 14 nd 284 14 16 ▁segmentation 335 16 29 ▁tokenization▁and▁segmentation +Antidisestablishmentarianism 15 ▁An 408 0 2 ti 523 2 4 d 273 4 5 is 278 5 7 est 323 7 10 a 279 10 11 b 344 11 12 lish 454 12 16 ment 508 16 20 ar 353 20 22 i 282 22 23 a 279 23 24 n 268 24 25 is 278 25 27 m 320 27 28 ▁Antidisestablishmentarianism +water running walked faster apple book work play 15 ▁water 312 0 5 ▁runn 467 5 10 ing 272 10 13 ▁walk 348 13 18 ed 267 18 20 ▁fast 358 20 25 er 270 25 27 ▁app 498 27 31 le 405 31 33 ▁b 309 33 35 o 274 35 36 o 274 36 37 k 354 37 38 ▁work 303 38 43 ▁play 313 43 48 ▁water▁running▁walked▁faster▁apple▁book▁work▁play +3.14159 x 42 = 1024? 21 ▁ 261 0 0 3 543 0 1 . 262 1 2 1 371 2 3 4 372 3 4 1 371 4 5 5 549 5 6 9 550 6 7 ▁ 261 7 8 x 421 8 9 ▁ 261 9 10 4 372 10 11 2 434 11 12 ▁ 261 12 13 <0x3D> 66 13 14 ▁ 261 14 15 1 371 15 16 0 548 16 17 2 434 17 18 4 372 18 19 ? 435 19 20 ▁3.14159▁x▁42▁=▁1024? +!!!???... 10 ▁ 261 0 0 ! 370 0 1 ! 370 1 2 ! 370 2 3 ? 435 3 4 ? 435 4 5 ? 435 5 6 . 262 6 7 . 262 7 8 . 262 8 9 ▁!!!???... +(parentheses) and [brackets] and {braces} 27 ▁ 261 0 0 ( 546 0 1 p 352 1 2 are 286 2 5 n 268 5 6 th 289 6 8 e 264 8 9 ses 414 9 12 ) 547 12 13 ▁a 266 13 15 nd 284 15 17 ▁ 261 17 18 <0x5B> 96 18 19 b 344 19 20 r 288 20 21 ack 327 21 24 e 264 24 25 ts 311 25 27 <0x5D> 98 27 28 ▁a 266 28 30 nd 284 30 32 ▁ 261 32 33 <0x7B> 128 33 34 b 344 34 35 ra 409 35 37 ces 478 37 40 <0x7D> 130 40 41 ▁(parentheses)▁and▁[brackets]▁and▁{braces} +café naïve fiancé résumé 19 ▁caf 484 0 3 é 557 3 4 ▁ 261 4 5 n 268 5 6 a 279 6 7 <0xC3> 200 7 7 <0xAF> 180 7 8 ve 366 8 10 ▁fi 518 10 13 a 279 13 14 n 268 14 15 c 411 15 16 é 557 16 17 ▁ 261 17 18 r 288 18 19 é 557 19 20 s 263 20 21 um 529 21 23 é 557 23 24 ▁café▁naïve▁fiancé▁résumé +financial fluid 13 ▁fi 518 0 1 n 268 1 2 a 279 2 3 n 268 3 4 c 411 4 5 i 282 5 6 al 281 6 8 ▁ 261 8 9 f 337 9 9 l 319 9 10 u 271 10 11 i 282 11 12 d 273 12 13 ▁financial▁fluid +① ⑪ ㋿ ＫＡＴＡＫＡＮＡ 21 ▁ 261 0 0 1 371 0 1 ▁ 261 1 2 1 371 2 2 1 371 2 3 ▁ 261 3 4 <0xE4> 233 4 4 <0xBB> 192 4 4 <0xA4> 169 4 4 <0xE5> 234 4 4 <0x92> 151 4 4 <0x8C> 145 4 5 ▁ 261 5 6 <0x4B> 80 6 7 A 596 7 8 T 599 8 9 A 596 9 10 <0x4B> 80 10 11 A 596 11 12 N 542 12 13 A 596 13 14 ▁1▁11▁令和▁KATAKANA +ｶﾀｶﾅ half width 17 ▁ 261 0 0 <0xE3> 232 0 0 <0x82> 135 0 0 <0xAB> 176 0 1 タ 565 1 2 <0xE3> 232 2 2 <0x82> 135 2 2 <0xAB> 176 2 3 <0xE3> 232 3 3 <0x83> 136 3 3 <0x8A> 143 3 4 ▁h 512 4 6 al 281 6 8 f 337 8 9 ▁wi 314 9 12 d 273 12 13 th 289 13 15 ▁カタカナ▁half▁width +東京タワーへ行きました 18 ▁ 261 0 0 東 571 0 1 京 568 1 2 タ 565 2 3 ワ 580 3 4 ー 581 4 5 <0xE3> 232 5 5 <0x81> 134 5 5 <0xB8> 189 5 6 <0xE8> 237 6 6 <0xA1> 166 6 6 <0x8C> 145 6 7 <0xE3> 232 7 7 <0x81> 134 7 7 <0x8D> 146 7 8 ま 587 8 9 し 450 9 10 た 564 10 11 ▁東京タワーへ行きました +日本語とEnglish混在 18 ▁ 261 0 0 日 583 0 1 本 585 1 2 <0xE8> 237 2 2 <0xAA> 175 2 2 <0x9E> 163 2 3 <0xE3> 232 3 3 <0x81> 134 3 3 <0xA8> 173 3 4 E 595 4 5 ng 328 5 7 lish 454 7 11 <0xE6> 235 11 11 <0xB7> 188 11 11 <0xB7> 188 11 12 <0xE5> 234 12 12 <0x9C> 161 12 12 <0xA8> 173 12 13 ▁日本語とEnglish混在 +Привет мир 11 ▁ 261 0 0 П 559 0 1 р 382 1 2 и 373 2 3 в 437 3 4 е 438 4 5 т 561 5 6 ▁ 261 6 7 м 439 7 8 и 373 8 9 р 382 9 10 ▁Привет▁мир +안녕하세요 세계 19 ▁ 261 0 0 <0xEC> 241 0 0 <0x95> 154 0 0 <0x88> 141 0 1 <0xEB> 240 1 1 <0x85> 138 1 1 <0x95> 154 1 2 <0xED> 242 2 2 <0x95> 154 2 2 <0x98> 157 2 3 세 432 3 4 <0xEC> 241 4 4 <0x9A> 159 4 4 <0x94> 153 4 5 ▁ 261 5 6 세 432 6 7 <0xEA> 239 7 7 <0xB3> 184 7 7 <0x84> 137 7 8 ▁안녕하세요▁세계 +你好，世界！ 7 ▁ 261 0 0 你 569 0 1 好 570 1 2 , 280 2 3 世 566 3 4 界 572 4 5 ! 370 5 6 ▁你好,世界! +I love 🍕 pizza 12 ▁I 329 0 1 ▁lo 305 1 4 ve 366 4 6 ▁ 261 6 7 <0xF0> 245 7 7 <0x9F> 164 7 7 <0x8D> 146 7 7 <0x95> 154 7 9 ▁p 486 9 11 i 282 11 12 z 351 12 13 za 540 13 15 ▁I▁love▁🍕▁pizza +flags 🇩🇪 🇺🇸 end 26 ▁ 261 0 0 f 337 0 1 l 319 1 2 a 279 2 3 g 356 3 4 s 263 4 5 ▁ 261 5 6 <0xF0> 245 6 6 <0x9F> 164 6 6 <0x87> 140 6 6 <0xA9> 174 6 8 <0xF0> 245 8 8 <0x9F> 164 8 8 <0x87> 140 8 8 <0xAA> 175 8 10 ▁ 261 10 11 <0xF0> 245 11 11 <0x9F> 164 11 11 <0x87> 140 11 11 <0xBA> 191 11 13 <0xF0> 245 13 13 <0x9F> 164 13 13 <0x87> 140 13 13 <0xB8> 189 13 15 ▁en 318 15 18 d 273 18 19 ▁flags▁🇩🇪▁🇺🇸▁end +family 👩‍👩‍👧‍👦 emoji 34 ▁famil 446 0 5 y 275 5 6 ▁ 261 6 7 <0xF0> 245 7 7 <0x9F> 164 7 7 <0x91> 150 7 7 <0xA9> 174 7 9 <0xE2> 231 9 9 <0x80> 133 9 9 <0x8D> 146 9 10 <0xF0> 245 10 10 <0x9F> 164 10 10 <0x91> 150 10 10 <0xA9> 174 10 12 <0xE2> 231 12 12 <0x80> 133 12 12 <0x8D> 146 12 13 <0xF0> 245 13 13 <0x9F> 164 13 13 <0x91> 150 13 13 <0xA7> 172 13 15 <0xE2> 231 15 15 <0x80> 133 15 15 <0x8D> 146 15 16 <0xF0> 245 16 16 <0x9F> 164 16 16 <0x91> 150 16 16 <0xA6> 171 16 18 ▁ 261 18 19 e 264 19 20 m 320 20 21 o 274 21 22 j 381 22 23 i 282 23 24 ▁family▁👩‍👩‍👧‍👦▁emoji +zero​width and non breaking 16 ▁ 261 0 0 z 351 0 1 er 270 1 3 o 274 3 4 ▁wi 314 4 7 d 273 7 8 th 289 8 10 ▁a 266 10 12 nd 284 12 14 ▁no 489 14 17 n 268 17 18 ▁b 309 18 20 re 349 20 22 a 279 22 23 k 354 23 24 ing 272 24 27 ▁zero▁width▁and▁non▁breaking +quotes “fancy” and ‘single’ — dash 33 ▁quote 456 0 5 s 263 5 6 ▁ 261 6 7 <0xE2> 231 7 7 <0x80> 133 7 7 <0x9C> 161 7 8 f 337 8 9 a 279 9 10 n 268 10 11 c 411 11 12 y 275 12 13 <0xE2> 231 13 13 <0x80> 133 13 13 <0x9D> 162 13 14 ▁a 266 14 16 nd 284 16 18 ▁ 261 18 19 <0xE2> 231 19 19 <0x80> 133 19 19 <0x98> 157 19 20 s 263 20 21 ing 272 21 24 le 405 24 26 <0xE2> 231 26 26 <0x80> 133 26 26 <0x99> 158 26 27 ▁ 261 27 28 <0xE2> 231 28 28 <0x80> 133 28 28 <0x94> 153 28 29 ▁d 368 29 31 as 420 31 33 h 308 33 34 ▁quotes▁“fancy”▁and▁‘single’▁—▁dash + the [URL] token 8 ▁ 261 0 0 3 0 6 ▁the 265 6 10 ▁ 261 10 11 [URL] 4 11 16 ▁to 304 16 19 k 354 19 20 en 350 20 22 ▁▁the▁[URL]▁token +a b[URL]c 6 ▁a 266 0 1 ▁ 261 1 2 3 2 8 b 344 8 9 [URL] 4 9 14 c 411 14 15 ▁a▁b[URL]c +control tokens inline 20 ▁co 326 0 2 n 268 2 3 tro 429 3 6 l 319 6 7 ▁ 261 7 8 <0x3C> 65 8 9 s 263 9 10 <0x3E> 67 10 11 ▁to 304 11 14 k 354 14 15 en 350 15 17 s 263 17 18 ▁ 261 18 19 <0x3C> 65 19 20 <0x2F> 52 20 21 s 263 21 22 <0x3E> 67 22 23 ▁in 276 23 26 l 319 26 27 ine 415 27 30 ▁control▁▁tokens▁▁inline +https://example.com/path?q=1&x=2 27 ▁h 512 0 1 t 269 1 2 t 269 2 3 p 352 3 4 s 263 4 5 <0x3A> 63 5 6 <0x2F> 52 6 7 <0x2F> 52 7 8 e 264 8 9 x 421 9 10 a 279 10 11 mple 485 11 15 . 262 15 16 c 411 16 17 om 475 17 19 <0x2F> 52 19 20 p 352 20 21 a 279 21 22 th 289 22 24 ? 435 24 25 q 598 25 26 <0x3D> 66 26 27 1 371 27 28 <0x26> 43 28 29 x 421 29 30 <0x3D> 66 30 31 2 434 31 32 ▁https://example.com/path?q=1&x=2 +UPPER lower MiXeD case 17 ▁ 261 0 0 U 593 0 1 P 425 1 2 P 425 2 3 E 595 3 4 R 544 4 5 ▁lo 305 5 8 w 419 8 9 er 270 9 11 ▁M 442 11 13 i 282 13 14 <0x58> 93 14 15 e 264 15 16 D 589 16 17 ▁c 338 17 19 as 420 19 21 e 264 21 22 ▁UPPER▁lower▁MiXeD▁case +aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa 40 ▁a 266 0 1 a 279 1 2 a 279 2 3 a 279 3 4 a 279 4 5 a 279 5 6 a 279 6 7 a 279 7 8 a 279 8 9 a 279 9 10 a 279 10 11 a 279 11 12 a 279 12 13 a 279 13 14 a 279 14 15 a 279 15 16 a 279 16 17 a 279 17 18 a 279 18 19 a 279 19 20 a 279 20 21 a 279 21 22 a 279 22 23 a 279 23 24 a 279 24 25 a 279 25 26 a 279 26 27 a 279 27 28 a 279 28 29 a 279 29 30 a 279 30 31 a 279 31 32 a 279 32 33 a 279 33 34 a 279 34 35 a 279 35 36 a 279 36 37 a 279 37 38 a 279 38 39 a 279 39 40 ▁aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa +Ω≈ç√∫˜µ≤ 22 ▁ 261 0 0 <0xCE> 211 0 0 <0xA9> 174 0 1 <0xE2> 231 1 1 <0x89> 142 1 1 <0x88> 141 1 2 <0xC3> 200 2 2 <0xA7> 172 2 3 <0xE2> 231 3 3 <0x88> 141 3 3 <0x9A> 159 3 4 <0xE2> 231 4 4 <0x88> 141 4 4 <0xAB> 176 4 5 ▁ 261 5 5 <0xCC> 209 5 5 <0x83> 136 5 6 <0xCE> 211 6 6 <0xBC> 193 6 7 <0xE2> 231 7 7 <0x89> 142 7 7 <0xA4> 169 7 8 ▁Ω≈ç√∫▁̃μ≤ +مرحبا بالعالم 26 ▁ 261 0 0 <0xD9> 222 0 0 <0x85> 138 0 1 <0xD8> 221 1 1 <0xB1> 182 1 2 <0xD8> 221 2 2 <0xAD> 178 2 3 <0xD8> 221 3 3 <0xA8> 173 3 4 <0xD8> 221 4 4 <0xA7> 172 4 5 ▁ 261 5 6 <0xD8> 221 6 6 <0xA8> 173 6 7 <0xD8> 221 7 7 <0xA7> 172 7 8 <0xD9> 222 8 8 <0x84> 137 8 9 <0xD8> 221 9 9 <0xB9> 190 9 10 <0xD8> 221 10 10 <0xA7> 172 10 11 <0xD9> 222 11 11 <0x84> 137 11 12 <0xD9> 222 12 12 <0x85> 138 12 13 ▁مرحبا▁بالعالم + leading and trailing 10 ▁lea 499 2 5 ding 501 5 9 ▁a 266 9 11 nd 284 11 13 ▁ 261 13 14 t 269 14 15 ra 409 15 17 i 282 17 18 l 319 18 19 ing 272 19 22 ▁leading▁and▁trailing +\ttab\tstart 5 ▁ 261 1 1 t 269 1 2 a 279 2 3 b 344 3 4 ▁start 453 4 10 ▁tab▁start +newline\n\n\nruns 11 ▁ 261 0 0 n 268 0 1 e 264 1 2 w 419 2 3 l 319 3 4 ine 415 4 7 ▁ 261 7 10 r 288 10 11 u 271 11 12 n 268 12 13 s 263 13 14 ▁newline▁runs +mid spaces collapse 12 ▁ 261 0 0 m 320 0 1 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b/opennlp-extensions/opennlp-subword/src/test/resources/opennlp/subword/sentencepiece/tiny-unigram.fixtures.tsv new file mode 100644 index 0000000000..5eeffe47e5 --- /dev/null +++ b/opennlp-extensions/opennlp-subword/src/test/resources/opennlp/subword/sentencepiece/tiny-unigram.fixtures.tsv @@ -0,0 +1,40 @@ + 0 + 0 + 0 +a 1 ▁a 10 0 1 ▁a +Hello world 7 ▁ 5 0 0 H 241 0 1 e 8 1 2 ll 105 2 4 o 17 4 5 ▁wor 160 5 9 ld 74 9 11 ▁Hello▁world + Hello world 7 ▁ 5 1 1 H 241 1 2 e 8 2 3 ll 105 3 5 o 17 5 6 ▁wor 160 6 12 ld 74 12 14 ▁Hello▁world +Hello world.\nSecond line\ttabbed 23 ▁ 5 0 0 H 241 0 1 e 8 1 2 ll 105 2 4 o 17 4 5 ▁wor 160 5 9 ld 74 9 11 . 7 11 12 ▁ 5 12 13 S 297 13 14 e 8 14 15 c 38 15 16 o 17 16 17 nd 24 17 19 ▁ 5 19 20 l 30 20 21 ine 147 21 24 ▁ 5 24 25 t 11 25 26 a 13 26 27 b 45 27 28 b 45 28 29 ed 18 29 31 ▁Hello▁world.▁Second▁line▁tabbed +The quick brown fox jumps over the lazy dog. 28 ▁Th 25 0 2 e 8 2 3 ▁qu 69 3 6 i 15 6 7 ck 43 7 9 ▁b 47 9 11 r 23 11 12 ow 60 12 14 n 9 14 15 ▁fo 97 15 18 x 108 18 19 ▁ 5 19 20 j 115 20 21 u 14 21 22 m 26 22 23 p 27 23 24 s 6 24 25 ▁ 5 25 26 o 17 26 27 ve 102 27 29 r 23 29 30 ▁the 12 30 34 ▁la 88 34 37 z 54 37 38 y 19 38 39 ▁do 159 39 42 g 48 42 43 . 7 43 44 ▁The▁quick▁brown▁fox▁jumps▁over▁the▁lazy▁dog. +tokenization and segmentation 4 ▁tokenization 200 0 12 ▁a 10 12 14 nd 24 14 16 ▁segmentation 80 16 29 ▁tokenization▁and▁segmentation +Antidisestablishmentarianism 14 ▁An 123 0 2 t 11 2 3 i 15 3 4 d 33 4 5 is 22 5 7 est 63 7 10 a 13 10 11 b 45 11 12 lish 193 12 16 ment 209 16 20 aria 221 20 24 n 9 24 25 is 22 25 27 m 26 27 28 ▁Antidisestablishmentarianism +water running walked faster apple book work play 15 ▁water 70 0 5 ▁runn 211 5 10 ing 20 10 13 ▁walk 87 13 18 ed 18 18 20 ▁fast 85 20 25 er 16 25 27 ▁appl 214 27 32 e 8 32 33 ▁b 47 33 35 o 17 35 36 o 17 36 37 k 66 37 38 ▁work 57 38 43 ▁play 71 43 48 ▁water▁running▁walked▁faster▁apple▁book▁work▁play +3.14159 x 42 = 1024? 21 ▁ 5 0 0 3 225 0 1 . 7 1 2 1 109 2 3 4 110 3 4 1 109 4 5 5 238 5 6 9 239 6 7 ▁ 5 7 8 x 108 8 9 ▁ 5 9 10 4 110 10 11 2 169 11 12 ▁ 5 12 13 = 0 13 14 ▁ 5 14 15 1 109 15 16 0 282 16 17 2 169 17 18 4 110 18 19 ? 170 19 20 ▁3.14159▁x▁42▁=▁1024? +!!!???... 10 ▁ 5 0 0 ! 113 0 1 ! 113 1 2 ! 113 2 3 ? 170 3 4 ? 170 4 5 ? 170 5 6 . 7 6 7 . 7 7 8 . 7 8 9 ▁!!!???... +(parentheses) and [brackets] and {braces} 27 ▁ 5 0 0 ( 236 0 1 p 27 1 2 are 34 2 5 n 9 5 6 th 36 6 8 e 8 8 9 ses 150 9 12 ) 237 12 13 ▁a 10 13 15 nd 24 15 17 ▁ 5 17 18 [ 0 18 19 b 45 19 20 r 23 20 21 ack 89 21 24 e 8 24 25 ts 101 25 27 ] 0 27 28 ▁a 10 28 30 nd 24 30 32 ▁ 5 32 33 { 0 33 34 b 45 34 35 ra 152 35 37 ces 216 37 40 } 0 40 41 ▁(parentheses)▁and▁[brackets]▁and▁{braces} +café naïve fiancé résumé 21 ▁ 5 0 0 ca 104 0 2 f 41 2 3 é 247 3 4 ▁ 5 4 5 n 9 5 6 a 13 6 7 ï 0 7 8 ve 102 8 10 ▁fi 210 10 13 a 13 13 14 n 9 14 15 c 38 15 16 é 247 16 17 ▁ 5 17 18 r 23 18 19 é 247 19 20 s 6 20 21 u 14 21 22 m 26 22 23 é 247 23 24 ▁café▁naïve▁fiancé▁résumé +financial fluid 13 ▁fi 210 0 1 n 9 1 2 a 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259 3 4 界 267 4 5 ! 113 5 6 ▁你好,世界! +I love 🍕 pizza 12 ▁ 5 0 0 I 294 0 1 ▁lo 53 1 4 ve 102 4 6 ▁ 5 6 7 🍕 279 7 9 ▁ 5 9 10 p 27 10 11 i 15 11 12 z 54 12 13 z 54 13 14 a 13 14 15 ▁I▁love▁🍕▁pizza +flags 🇩🇪 🇺🇸 end 14 ▁ 5 0 0 f 41 0 1 l 30 1 2 a 13 2 3 g 48 3 4 s 6 4 5 ▁ 5 5 6 🇩 277 6 8 🇪 278 8 10 ▁ 5 10 11 🇺🇸 0 11 15 ▁ 5 15 16 e 8 16 17 nd 24 17 19 ▁flags▁🇩🇪▁🇺🇸▁end +family 👩‍👩‍👧‍👦 emoji 10 ▁famil 185 0 5 y 19 5 6 ▁ 5 6 7 👩‍👩‍👧‍👦 0 7 18 ▁ 5 18 19 e 8 19 20 m 26 20 21 o 17 21 22 j 115 22 23 i 15 23 24 ▁family▁👩‍👩‍👧‍👦▁emoji +zero​width and non breaking 19 ▁ 5 0 0 z 54 0 1 er 16 1 3 o 17 3 4 ▁wi 73 4 7 d 33 7 8 th 36 8 10 ▁a 10 10 12 nd 24 12 14 ▁ 5 14 15 n 9 15 16 o 17 16 17 n 9 17 18 ▁b 47 18 20 r 23 20 21 e 8 21 22 a 13 22 23 k 66 23 24 ing 20 24 27 ▁zero▁width▁and▁non▁breaking +quotes “fancy” and ‘single’ — dash 24 ▁quote 198 0 5 s 6 5 6 ▁ 5 6 7 “ 0 7 8 f 41 8 9 a 13 9 10 n 9 10 11 c 38 11 12 y 19 12 13 ” 0 13 14 ▁a 10 14 16 nd 24 16 18 ▁ 5 18 19 ‘ 0 19 20 s 6 20 21 ing 20 21 24 le 107 24 26 ’ 0 26 27 ▁ 5 27 28 — 0 28 29 ▁d 100 29 31 a 13 31 32 s 6 32 33 h 32 33 34 ▁quotes▁“fancy”▁and▁‘single’▁—▁dash + the [URL] token 9 ▁ 5 0 0 3 0 6 ▁the 12 6 10 ▁ 5 10 11 [URL] 4 11 16 ▁to 51 16 19 k 66 19 20 e 8 20 21 n 9 21 22 ▁▁the▁[URL]▁token +a b[URL]c 6 ▁a 10 0 1 ▁ 5 1 2 3 2 8 b 45 8 9 [URL] 4 9 14 c 38 14 15 ▁a▁b[URL]c +control tokens inline 20 ▁co 99 0 2 n 9 2 3 tro 128 3 6 l 30 6 7 ▁ 5 7 8 < 0 8 9 s 6 9 10 > 0 10 11 ▁to 51 11 14 k 66 14 15 e 8 15 16 n 9 16 17 s 6 17 18 ▁ 5 18 19 0 22 23 ▁in 35 23 26 l 30 26 27 ine 147 27 30 ▁control▁▁tokens▁▁inline +https://example.com/path?q=1&x=2 29 ▁ 5 0 0 h 32 0 1 t 11 1 2 t 11 2 3 p 27 3 4 s 6 4 5 :// 0 5 8 e 8 8 9 x 108 9 10 a 13 10 11 m 26 11 12 p 27 12 13 le 107 13 15 . 7 15 16 c 38 16 17 o 17 17 18 m 26 18 19 / 0 19 20 p 27 20 21 a 13 21 22 th 36 22 24 ? 170 24 25 q 298 25 26 = 0 26 27 1 109 27 28 & 0 28 29 x 108 29 30 = 0 30 31 2 169 31 32 ▁https://example.com/path?q=1&x=2 +UPPER lower MiXeD case 19 ▁ 5 0 0 U 293 0 1 P 156 1 2 P 156 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zzkfOP`n|&&hgWa?t6Tr@Hx6&P(Zl%%r$0H|e*1MNzV(S;SBKkvF;PCvXhvz^3`Mb}5^89Cq=YRG5)8{{OnNMGR<)2?Z;`1LI zzPQNY`A-h6_2RuQ`P0j9yg2jImv4T2__@pf#D{%({;v+tKRrDE(TnSU>gNCNmu~&b z%UAr-+jrl%fBS2`v~R!h==K|T4qvH`%MV{@Ja+|IY2VzIx~N!{yCgcjd*|`!C)+c;(kz zt{<(=;4|M}9bFaOq?cOTw)@b%lT z-8tO#$$9YF?XO?%_T>w3`|#lFcV4?|>DO-G|EfFi;N{0VeC6Jww_m?~|L&a!hX;@D zf8(w{M8EhM{QT>3_${9@AKXW|eDupba0{0=-gS8Gwm+<&@BiYn{o;?0-}4)X*ZgsR z`8odl#xGa&x5SGNa9MRZ@s-29N0&c{x9=Y=cjo0q{K@&o;c}mx{l&Xw@ZDR#`q$rk z^4EWM{lBjM{Ihre;;+B^t^fJ#{mQ9`?GgH{p{U8{p=6k td*z>8KAy`TT;bpv2Uj_`&cT%qu61xV@B5x#?>YZ%Z$I_xPY opennlp-morfologik + opennlp-subword opennlp-spellcheck opennlp-uima diff --git a/rat-excludes b/rat-excludes index 5a5d86b90c..a505ab2d60 100644 --- a/rat-excludes +++ b/rat-excludes @@ -70,3 +70,8 @@ src/main/resources/opennlp/tools/tokenize/uax29/WordBreakProperty.txt src/main/resources/opennlp/tools/tokenize/uax29/ExtendedPictographic.txt src/main/resources/opennlp/tools/util/normalizer/confusables.txt src/test/resources/opennlp/tools/tokenize/uax29/WordBreakTest.txt + + +src/test/resources/opennlp/subword/sentencepiece/*.model +src/test/resources/opennlp/subword/sentencepiece/*.fixtures.tsv +src/test/resources/opennlp/subword/sentencepiece/corpus.txt From 3fb8b6bf5fea21430443af7724c4a049c1125a54 Mon Sep 17 00:00:00 2001 From: Kristian Rickert Date: Fri, 10 Jul 2026 19:08:45 -0400 Subject: [PATCH 2/9] Speed up the encode path 2.3x, parity-checked at every step The vocabulary trie dispatches wide nodes (the root and first level of a real vocabulary) through a 256-entry direct table, one load per byte, and scans narrow nodes' short label slices linearly instead of binary searching; a randomized differential test holds both layouts against a map-backed reference, and moving the duplicate-piece detection into the counting pass fixes the index error it previously produced. Non-unknown segments reuse the vocabulary's piece string instead of decoding their bytes, since the trie match means the bytes are identical. The normalizer precomputes, per possible first byte, whether any character-map rule or user-defined symbol starts with it; a clear bit proves the prefix machinery would pass the byte through raw, so plain ASCII text skips it entirely. The per-chunk record became a per-call scratch, the input view keeps its oversized buffers with an explicit length instead of trimming (pure-ASCII text gets an identity offset map and no map array at all), the Viterbi scratch is one interleaved array with scores as raw float bits, and the character-map trie walk relies on the JVM's own bounds checks with the fail-loud translation on the cold path. All 37 bundled parity tests and the T5-small and ALBERT real-model fixtures pass byte-identically. Single-thread throughput on the T5-small vocabulary goes from 2.83M to 6.47M pieces per second, from 0.62x to 1.42x of the reference implementation measured through its Python binding. --- .../subword/sentencepiece/BpeEncoder.java | 21 +-- .../subword/sentencepiece/ByteBuilder.java | 5 + .../sentencepiece/DoubleArrayTrie.java | 54 ++++--- .../subword/sentencepiece/IntBuilder.java | 5 + .../subword/sentencepiece/PieceTrie.java | 59 ++++++-- .../SentencePieceNormalizer.java | 132 ++++++++++++------ .../sentencepiece/SentencePieceTokenizer.java | 26 ++-- .../subword/sentencepiece/UnigramEncoder.java | 48 ++++--- .../subword/sentencepiece/Utf8Text.java | 33 ++++- .../subword/sentencepiece/PieceTrieTest.java | 116 +++++++++++++++ 10 files changed, 380 insertions(+), 119 deletions(-) create mode 100644 opennlp-extensions/opennlp-subword/src/test/java/opennlp/subword/sentencepiece/PieceTrieTest.java diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/BpeEncoder.java b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/BpeEncoder.java index 3cea8edb64..e7430032ae 100644 --- a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/BpeEncoder.java +++ b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/BpeEncoder.java @@ -75,29 +75,30 @@ private record Pair(int left, int right, float score, int size) { /** * Segments normalized text. * - * @param normalized The normalized UTF-8 bytes; must not be null. + * @param normalized The buffer holding the normalized UTF-8 bytes; must not be null. + * @param size The number of valid bytes in {@code normalized}. * @return The segments covering all bytes, in text order. */ - List encode(byte[] normalized) { - if (normalized.length == 0) { + List encode(byte[] normalized, int size) { + if (size == 0) { return List.of(); } // The symbol list as index-linked ranges of the normalized bytes; merged-away symbols // become empty ranges. - final IntBuilder fromB = new IntBuilder(normalized.length); - final IntBuilder toB = new IntBuilder(normalized.length); + final IntBuilder fromB = new IntBuilder(size); + final IntBuilder toB = new IntBuilder(size); final List freezeList = new ArrayList<>(); int position = 0; - while (position < normalized.length) { + while (position < size) { int matched = 0; if (userDefinedMatcher != null) { - matched = longestUserDefinedMatch(normalized, position); + matched = longestUserDefinedMatch(normalized, size, position); } final boolean frozen = matched > 0; final int length = frozen ? matched : Math.min(SentencePieceNormalizer.utf8Length(normalized[position]), - normalized.length - position); + size - position); fromB.append(position); toB.append(position + length); freezeList.add(frozen); @@ -199,10 +200,10 @@ private int resegment(String piece, int consumed, int depth, Map>> 8) & 1) == 1) { - final int value = unit(nodePos) & 0x7FFFFFFF; - result = ((long) value << 32) | (i - from + 1); + for (int i = from; i < to; i++) { + final int b = key[i] & 0xFF; + nodePos ^= b; + unit = u[nodePos]; + if ((unit & 0x800000FF) != b) { + return result; + } + nodePos ^= offset(unit); + if (((unit >>> 8) & 1) == 1) { + final int value = u[nodePos] & 0x7FFFFFFF; + result = ((long) value << 32) | (i - from + 1); + } } + return result; + } catch (ArrayIndexOutOfBoundsException e) { + throw new IllegalArgumentException( + "The trie references a unit outside its " + u.length + " units.", e); } - return result; } - private int unit(int nodePos) { + /** + * Tests whether any key starts with the given byte, which is exactly whether the root has a + * transition on it; used to precompute the first-byte gate of the normalizer scan. + * + * @param b The first key byte as an unsigned value. + * @return {@code true} if some key starts with {@code b}. + */ + boolean hasTransitionFromRoot(int b) { + final int root = units[0]; + final int nodePos = offset(root) ^ b; if (nodePos < 0 || nodePos >= units.length) { - throw new IllegalArgumentException( - "The trie references unit " + nodePos + " outside its " + units.length + " units."); + return false; } - return units[nodePos]; + return (units[nodePos] & 0x800000FF) == b; } // The offset from a unit to its children, as encoded by Darts-clone. diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/IntBuilder.java b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/IntBuilder.java index 70a2db9ac4..ec585685c1 100644 --- a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/IntBuilder.java +++ b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/IntBuilder.java @@ -53,4 +53,9 @@ void truncate(int newLength) { int[] toArray() { return Arrays.copyOf(data, length); } + + /** {@return the backing array, valid up to {@link #length()}} */ + int[] array() { + return data; + } } diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/PieceTrie.java b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/PieceTrie.java index eeb028e00b..37c2ef7912 100644 --- a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/PieceTrie.java +++ b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/PieceTrie.java @@ -24,26 +24,54 @@ * *

Encoding walks it one byte at a time ({@link #step(int, byte)}) while scanning the input, so * every piece that starts at a given input position is enumerated in one forward pass; this is the - * lattice-population step of subword segmentation. Children of a node are stored as a sorted - * label slice and found by binary search.

+ * lattice-population step of subword segmentation. This step sits in the innermost loop of the + * encoder, so wide nodes (the root and the first level of a real vocabulary) dispatch through a + * 256-entry direct table, one load per byte, and narrow nodes scan their short sorted label slice + * linearly; both layouts enumerate identical transitions.

*/ final class PieceTrie { /** The node id returned when no transition exists. */ static final int DEAD = -1; + // A node dispatches through a 256-entry slice of directPool when it has more children than + // this; below it, a linear scan of the sorted label slice wins on memory and is branch-cheap. + private static final int DIRECT_THRESHOLD = 8; + // Per node: the slice [childStart[n], childStart[n + 1]) of labels/childNodes, and the piece id - // accepted at the node, or -1. + // accepted at the node, or -1. Wide nodes additionally index directPool at directStart[n]. private final int[] childStart; private final byte[] labels; private final int[] childNodes; private final int[] values; + private final int[] directStart; + private final int[] directPool; private PieceTrie(int[] childStart, byte[] labels, int[] childNodes, int[] values) { this.childStart = childStart; this.labels = labels; this.childNodes = childNodes; this.values = values; + this.directStart = new int[values.length]; + int wide = 0; + for (int node = 0; node < values.length; node++) { + if (childStart[node + 1] - childStart[node] > DIRECT_THRESHOLD) { + directStart[node] = wide * 256; + wide++; + } else { + directStart[node] = -1; + } + } + this.directPool = new int[wide * 256]; + java.util.Arrays.fill(directPool, DEAD); + for (int node = 0; node < values.length; node++) { + final int direct = directStart[node]; + if (direct >= 0) { + for (int edge = childStart[node]; edge < childStart[node + 1]; edge++) { + directPool[direct + (labels[edge] & 0xFF)] = childNodes[edge]; + } + } + } } /** @@ -82,19 +110,14 @@ int root() { * @return The child node id, or {@link #DEAD} when no such transition exists. */ int step(int node, byte b) { - final int from = childStart[node]; + final int direct = directStart[node]; + if (direct >= 0) { + return directPool[direct + (b & 0xFF)]; + } final int to = childStart[node + 1]; - int low = from; - int high = to - 1; - while (low <= high) { - final int mid = (low + high) >>> 1; - final int c = Byte.compareUnsigned(labels[mid], b); - if (c < 0) { - low = mid + 1; - } else if (c > 0) { - high = mid - 1; - } else { - return childNodes[mid]; + for (int edge = childStart[node]; edge < to; edge++) { + if (labels[edge] == b) { + return childNodes[edge]; } } return DEAD; @@ -140,6 +163,12 @@ void count(int from, int to, int depth) { int i = from; if (i < to && pieces[order[i]].length == depth) { i++; + // A second key ending at the same depth is a duplicate; the sort made them adjacent. + if (i < to && pieces[order[i]].length == depth) { + throw new IllegalArgumentException("The piece '" + + new String(pieces[order[i]], java.nio.charset.StandardCharsets.UTF_8) + + "' is defined more than once."); + } } while (i < to) { final byte label = pieces[order[i]][depth]; diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceNormalizer.java b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceNormalizer.java index 4d30b425c1..d99d4b33ce 100644 --- a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceNormalizer.java +++ b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceNormalizer.java @@ -45,6 +45,10 @@ final class SentencePieceNormalizer { private final boolean escapeWhitespaces; private final boolean treatWhitespaceAsSuffix; private final PieceTrie userDefinedMatcher; + // For each possible first byte, whether any character-map rule or user-defined symbol starts + // with it. A clear bit proves normalizePrefix would pass the byte through raw, which lets the + // scan skip the whole prefix machinery for plain ASCII text. + private final boolean[] ruleLead = new boolean[256]; /** * Instantiates the normalizer. @@ -98,15 +102,32 @@ final class SentencePieceNormalizer { this.escapeWhitespaces = escapeWhitespaces; this.treatWhitespaceAsSuffix = treatWhitespaceAsSuffix; this.userDefinedMatcher = userDefinedMatcher; + for (int b = 0; b < 256; b++) { + final boolean charsMapLead = trie != null && trie.hasTransitionFromRoot(b); + final boolean userDefinedLead = userDefinedMatcher != null + && userDefinedMatcher.step(userDefinedMatcher.root(), (byte) b) != PieceTrie.DEAD; + ruleLead[b] = charsMapLead || userDefinedLead; + } } - /** The normalized bytes plus the normalized-byte to original-byte offset map. */ - record Normalized(byte[] bytes, int[] normToOrig) { + /** + * The normalized bytes plus the normalized-byte to original-byte offset map. The arrays are + * builder-backed and may be oversized; {@code length} bytes are valid, and the offset map + * holds {@code length + 1} entries. + */ + record Normalized(byte[] bytes, int length, int[] normToOrig) { } // One normalization step: `consumed` input bytes produced `data[from, to)`. The data array is - // the input itself (pass-through), the replacement blob, or the replacement character. - private record Chunk(byte[] data, int from, int to, int consumed) { + // the input itself (pass-through), the replacement blob, or the replacement character. One + // mutable scratch per normalize call, refilled per chunk, so the scan allocates nothing per + // code point. + private static final class Chunk { + + private byte[] data; + private int from; + private int to; + private int consumed; boolean isSingleSpace() { return to - from == 1 && data[from] == ' '; @@ -116,46 +137,61 @@ boolean isSingleSpace() { /** * Normalizes UTF-8 input. * - * @param input The well-formed UTF-8 bytes to normalize; must not be null. - * @return The normalized bytes with the offset map; {@code normToOrig.length} is always - * {@code bytes.length + 1}. + * @param input The buffer holding well-formed UTF-8 bytes; must not be null. + * @param inputLength The number of valid bytes in {@code input}. + * @return The normalized bytes with the offset map; the arrays are builder-backed, valid for + * {@code length} bytes and {@code length + 1} map entries. */ - Normalized normalize(byte[] input) { - final ByteBuilder normalized = new ByteBuilder(input.length + (input.length >> 1) + 4); - final IntBuilder normToOrig = new IntBuilder(input.length + (input.length >> 1) + 5); + Normalized normalize(byte[] input, int inputLength) { + final ByteBuilder normalized = new ByteBuilder(inputLength + (inputLength >> 1) + 4); + final IntBuilder normToOrig = new IntBuilder(inputLength + (inputLength >> 1) + 5); + final Chunk chunk = new Chunk(); int from = 0; int consumed = 0; // Ignores heading whitespace. if (removeExtraWhitespaces) { - while (from < input.length) { - final Chunk p = normalizePrefix(input, from); - if (!p.isSingleSpace()) { + while (from < inputLength) { + normalizePrefix(input, inputLength, from, chunk); + if (!chunk.isSingleSpace()) { break; } - from += p.consumed(); - consumed += p.consumed(); + from += chunk.consumed; + consumed += chunk.consumed; } } // All input was whitespace. - if (from >= input.length) { - return new Normalized(new byte[0], new int[] {consumed}); + if (from >= inputLength) { + normToOrig.append(consumed); + return new Normalized(normalized.array(), 0, normToOrig.array()); } - final byte[] spaceSymbol = escapeWhitespaces ? SPACE_SYMBOL : new byte[] {' '}; + final byte[] spaceSymbol = escapeWhitespaces ? SPACE_SYMBOL : SINGLE_SPACE; if (!treatWhitespaceAsSuffix && addDummyPrefix) { appendSpace(normalized, normToOrig, spaceSymbol, consumed); } boolean isPrevSpace = removeExtraWhitespaces; - while (from < input.length) { - final Chunk p = normalizePrefix(input, from); - int spFrom = p.from(); - final int spTo = p.to(); - final byte[] spData = p.data(); + while (from < inputLength) { + final int lead = input[from] & 0xFF; + // Fast path: an ASCII byte no rule starts with passes through raw; the chunk would be + // the byte itself, no leading-space stripping applies, and it does not end in a space. + if (lead < 0x80 && lead != ' ' && !ruleLead[lead]) { + normalized.append(input[from]); + normToOrig.append(consumed); + consumed++; + from++; + isPrevSpace = false; + continue; + } + + normalizePrefix(input, inputLength, from, chunk); + int spFrom = chunk.from; + final int spTo = chunk.to; + final byte[] spData = chunk.data; // Removes heading spaces in the chunk if the previous chunk ended with whitespace. while (isPrevSpace && spFrom < spTo && spData[spFrom] == ' ') { @@ -174,8 +210,8 @@ Normalized normalize(byte[] input) { isPrevSpace = spData[spTo - 1] == ' '; } - consumed += p.consumed(); - from += p.consumed(); + consumed += chunk.consumed; + from += chunk.consumed; if (!removeExtraWhitespaces) { isPrevSpace = false; } @@ -200,9 +236,11 @@ Normalized normalize(byte[] input) { throw new IllegalStateException("The offset map has " + normToOrig.length() + " entries for " + normalized.length() + " normalized bytes."); } - return new Normalized(normalized.toArray(), normToOrig.toArray()); + return new Normalized(normalized.array(), normalized.length(), normToOrig.array()); } + private static final byte[] SINGLE_SPACE = {' '}; + private static void appendSpace(ByteBuilder normalized, IntBuilder normToOrig, byte[] spaceSymbol, int consumed) { normalized.append(spaceSymbol, 0, spaceSymbol.length); @@ -211,19 +249,24 @@ private static void appendSpace(ByteBuilder normalized, IntBuilder normToOrig, } } - // Normalizes the longest applicable prefix of input[from, ...): a user-defined symbol passes - // through raw, otherwise the longest character-map rule applies, otherwise one code point - // passes through raw (or becomes U+FFFD when the lead byte is malformed). - private Chunk normalizePrefix(byte[] input, int from) { + // Fills the scratch with the normalized form of the longest applicable prefix of + // input[from, inputLength): a user-defined symbol passes through raw, otherwise the longest + // character-map rule applies, otherwise one code point passes through raw (or becomes U+FFFD + // when the lead byte is malformed). + private void normalizePrefix(byte[] input, int inputLength, int from, Chunk chunk) { if (userDefinedMatcher != null) { - final int matched = longestUserDefinedMatch(input, from); + final int matched = longestUserDefinedMatch(input, inputLength, from); if (matched > 0) { - return new Chunk(input, from, from + matched, matched); + chunk.data = input; + chunk.from = from; + chunk.to = from + matched; + chunk.consumed = matched; + return; } } if (trie != null) { - final long match = trie.longestPrefixMatch(input, from, input.length); + final long match = trie.longestPrefixMatch(input, from, inputLength); if (match >= 0) { final int value = (int) (match >>> 32); final int length = (int) (match & 0xFFFFFFFFL); @@ -233,22 +276,33 @@ private Chunk normalizePrefix(byte[] input, int from) { while (blob[replacementTo] != 0) { replacementTo++; } - return new Chunk(blob, replacementFrom, replacementTo, length); + chunk.data = blob; + chunk.from = replacementFrom; + chunk.to = replacementTo; + chunk.consumed = length; + return; } } } - final int charLength = Math.min(utf8Length(input[from]), input.length - from); + final int charLength = Math.min(utf8Length(input[from]), inputLength - from); if (isMalformed(input, from, charLength)) { - return new Chunk(REPLACEMENT_CHAR, 0, REPLACEMENT_CHAR.length, 1); + chunk.data = REPLACEMENT_CHAR; + chunk.from = 0; + chunk.to = REPLACEMENT_CHAR.length; + chunk.consumed = 1; + return; } - return new Chunk(input, from, from + charLength, charLength); + chunk.data = input; + chunk.from = from; + chunk.to = from + charLength; + chunk.consumed = charLength; } - private int longestUserDefinedMatch(byte[] input, int from) { + private int longestUserDefinedMatch(byte[] input, int inputLength, int from) { int node = userDefinedMatcher.root(); int longest = 0; - for (int i = from; i < input.length; i++) { + for (int i = from; i < inputLength; i++) { node = userDefinedMatcher.step(node, input[i]); if (node == PieceTrie.DEAD) { break; diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceTokenizer.java b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceTokenizer.java index 12e8271d76..5fa7855fde 100644 --- a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceTokenizer.java +++ b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceTokenizer.java @@ -253,10 +253,11 @@ public List encode(CharSequence text) { throw new IllegalArgumentException("The text must not be null."); } final Utf8Text input = Utf8Text.of(text); - final SentencePieceNormalizer.Normalized normalized = normalizer.normalize(input.bytes()); + final SentencePieceNormalizer.Normalized normalized = + normalizer.normalize(input.bytes(), input.byteLength()); final List segments = algorithm == Algorithm.UNIGRAM - ? unigramEncoder.encode(normalized.bytes()) - : bpeEncoder.encode(normalized.bytes()); + ? unigramEncoder.encode(normalized.bytes(), normalized.length()) + : bpeEncoder.encode(normalized.bytes(), normalized.length()); final List out = new ArrayList<>(segments.size()); final byte[] norm = normalized.bytes(); @@ -269,10 +270,14 @@ public List encode(CharSequence text) { int pendingUnkEnd = 0; for (final Segment segment : segments) { - final String piece = new String(norm, segment.from(), segment.to() - segment.from(), - StandardCharsets.UTF_8); final boolean isUnk = segment.id() == unkId; final boolean isControl = types[segment.id()] == TYPE_CONTROL; + // A non-unknown segment's bytes are exactly the vocabulary piece's bytes (that is what + // the match meant), so the vocabulary string is reused; only unknown segments carry + // surface content that needs decoding. + final String piece = isUnk + ? new String(norm, segment.from(), segment.to() - segment.from(), StandardCharsets.UTF_8) + : pieces[segment.id()]; if (isControl) { if (pendingUnk != null) { @@ -332,8 +337,10 @@ public AlignedText normalizeAligned(CharSequence text) { throw new IllegalArgumentException("The text must not be null."); } final Utf8Text input = Utf8Text.of(text); - final SentencePieceNormalizer.Normalized result = normalizer.normalize(input.bytes()); - final String normalized = new String(result.bytes(), StandardCharsets.UTF_8); + final SentencePieceNormalizer.Normalized result = + normalizer.normalize(input.bytes(), input.byteLength()); + final String normalized = new String(result.bytes(), 0, result.length(), + StandardCharsets.UTF_8); final int[] normToOrig = result.normToOrig(); final byte[] norm = result.bytes(); @@ -345,9 +352,10 @@ public AlignedText normalizeAligned(CharSequence text) { int groupOrigEnd = -1; int groupChars = 0; int b = 0; - while (b < norm.length) { + final int normLength = result.length(); + while (b < normLength) { final int byteLength = Math.min(SentencePieceNormalizer.utf8Length(norm[b]), - norm.length - b); + normLength - b); final int origStart = input.charOffset(normToOrig[b]); final int origEnd = input.charOffset(normToOrig[b + byteLength]); final int chars = byteLength == 4 ? 2 : 1; diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/UnigramEncoder.java b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/UnigramEncoder.java index a53ca70740..49ffcc2909 100644 --- a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/UnigramEncoder.java +++ b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/UnigramEncoder.java @@ -67,33 +67,37 @@ final class UnigramEncoder { /** * Segments normalized text. * - * @param normalized The normalized UTF-8 bytes; must not be null. + * @param normalized The buffer holding the normalized UTF-8 bytes; must not be null. + * @param size The number of valid bytes in {@code normalized}. * @return The best-path segments covering all bytes, in text order. */ - List encode(byte[] normalized) { - final int size = normalized.length; + List encode(byte[] normalized, int size) { if (size == 0) { return List.of(); } - // The best path ending at each byte position (exclusive end). - final int[] bestStartsAt = new int[size + 1]; - final float[] bestScore = new float[size + 1]; - final int[] bestId = new int[size + 1]; - java.util.Arrays.fill(bestStartsAt, -1); + // The best path ending at each byte position (exclusive end), interleaved as + // [startsAt, scoreBits, id] triples so a frontier update touches one cache line. Scores + // travel as raw float bits, a lossless round trip; all arithmetic happens on the floats. + final int[] best = new int[3 * (size + 1)]; + for (int i = 0; i <= size; i++) { + best[3 * i] = -1; + } + best[1] = Float.floatToRawIntBits(0.0f); int startsAt = 0; int maxFrontier = 0; while (startsAt < size) { - float bestScoreTillHere = bestScore[startsAt]; + float bestScoreTillHere = Float.intBitsToFloat(best[3 * startsAt + 1]); if (bestScoreTillHere < -SCORE_RESET_THRESHOLD || bestScoreTillHere > SCORE_RESET_THRESHOLD) { // Re-bases accumulated scores to keep float precision on very long inputs; every // reachable frontier position shifts by the same offset, so the argmax is unchanged. final float offset = bestScoreTillHere; for (int i = startsAt; i <= maxFrontier; i++) { - if (i == startsAt || bestStartsAt[i] != -1) { - bestScore[i] -= offset; + if (i == startsAt || best[3 * i] != -1) { + best[3 * i + 1] = Float.floatToRawIntBits( + Float.intBitsToFloat(best[3 * i + 1]) - offset); } } bestScoreTillHere = 0.0f; @@ -122,10 +126,11 @@ List encode(byte[] normalized) { // User-defined symbols receive a length bonus instead of a trained score. final float score = userDefined[id] ? 0.1f * (length - 1) : scores[id]; final float candidate = score + bestScoreTillHere; - if (bestStartsAt[keyPos] == -1 || candidate > bestScore[keyPos]) { - bestScore[keyPos] = candidate; - bestStartsAt[keyPos] = startsAt; - bestId[keyPos] = id; + final int slot = 3 * keyPos; + if (best[slot] == -1 || candidate > Float.intBitsToFloat(best[slot + 1])) { + best[slot + 1] = Float.floatToRawIntBits(candidate); + best[slot] = startsAt; + best[slot + 2] = id; } if (!hasSingleNode && length == mblen) { hasSingleNode = true; @@ -136,10 +141,11 @@ List encode(byte[] normalized) { final int end = startsAt + mblen; maxFrontier = Math.max(maxFrontier, end); final float candidate = unkScore + bestScoreTillHere; - if (bestStartsAt[end] == -1 || candidate > bestScore[end]) { - bestScore[end] = candidate; - bestStartsAt[end] = startsAt; - bestId[end] = unkId; + final int slot = 3 * end; + if (best[slot] == -1 || candidate > Float.intBitsToFloat(best[slot + 1])) { + best[slot + 1] = Float.floatToRawIntBits(candidate); + best[slot] = startsAt; + best[slot + 2] = unkId; } } @@ -149,12 +155,12 @@ List encode(byte[] normalized) { final List results = new ArrayList<>(size / 4 + 1); int endsAt = size; while (endsAt > 0) { - final int from = bestStartsAt[endsAt]; + final int from = best[3 * endsAt]; if (from < 0) { throw new IllegalStateException( "The Viterbi path is broken at normalized byte " + endsAt + "."); } - results.add(new Segment(from, endsAt, bestId[endsAt])); + results.add(new Segment(from, endsAt, best[3 * endsAt + 2])); endsAt = from; } Collections.reverse(results); diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/Utf8Text.java b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/Utf8Text.java index e331fdcc83..6ebb080f5e 100644 --- a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/Utf8Text.java +++ b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/Utf8Text.java @@ -28,11 +28,14 @@ final class Utf8Text { private final byte[] bytes; + private final int byteLength; + // Null for pure-ASCII text, where byte offsets equal UTF-16 offsets. private final int[] byteToChar; private final int charLength; - private Utf8Text(byte[] bytes, int[] byteToChar, int charLength) { + private Utf8Text(byte[] bytes, int byteLength, int[] byteToChar, int charLength) { this.bytes = bytes; + this.byteLength = byteLength; this.byteToChar = byteToChar; this.charLength = charLength; } @@ -45,6 +48,19 @@ private Utf8Text(byte[] bytes, int[] byteToChar, int charLength) { */ static Utf8Text of(CharSequence text) { final int charLength = text.length(); + // The common case: pure ASCII, where the bytes are the chars and the map is the identity. + int ascii = 0; + while (ascii < charLength && text.charAt(ascii) < 0x80) { + ascii++; + } + if (ascii == charLength) { + final byte[] exact = new byte[charLength]; + for (int i = 0; i < charLength; i++) { + exact[i] = (byte) text.charAt(i); + } + return new Utf8Text(exact, charLength, null, charLength); + } + final byte[] bytes = new byte[charLength * 3 + 1]; final int[] byteToChar = new int[charLength * 3 + 2]; int b = 0; @@ -82,16 +98,21 @@ static Utf8Text of(CharSequence text) { c += charCount; } byteToChar[b] = charLength; - final byte[] exact = java.util.Arrays.copyOf(bytes, b); - final int[] exactMap = java.util.Arrays.copyOf(byteToChar, b + 1); - return new Utf8Text(exact, exactMap, charLength); + // The oversized buffers are kept and carried with an explicit length instead of being + // trimmed; the per-call copies were pure allocation traffic. + return new Utf8Text(bytes, b, byteToChar, charLength); } - /** {@return the UTF-8 bytes} */ + /** {@return the UTF-8 buffer; valid up to {@link #byteLength()}} */ byte[] bytes() { return bytes; } + /** {@return the number of valid bytes in {@link #bytes()}} */ + int byteLength() { + return byteLength; + } + /** {@return the length of the original text in UTF-16 units} */ int charLength() { return charLength; @@ -104,6 +125,6 @@ int charLength() { * @return The UTF-16 offset; the text length for the end offset. */ int charOffset(int byteOffset) { - return byteToChar[byteOffset]; + return byteToChar == null ? byteOffset : byteToChar[byteOffset]; } } diff --git a/opennlp-extensions/opennlp-subword/src/test/java/opennlp/subword/sentencepiece/PieceTrieTest.java b/opennlp-extensions/opennlp-subword/src/test/java/opennlp/subword/sentencepiece/PieceTrieTest.java new file mode 100644 index 0000000000..bbde60825c --- /dev/null +++ b/opennlp-extensions/opennlp-subword/src/test/java/opennlp/subword/sentencepiece/PieceTrieTest.java @@ -0,0 +1,116 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://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. + */ +package opennlp.subword.sentencepiece; + +import java.nio.charset.StandardCharsets; +import java.util.HashMap; +import java.util.HashSet; +import java.util.Map; +import java.util.Random; +import java.util.Set; + +import org.junit.jupiter.api.Test; + +import static org.junit.jupiter.api.Assertions.assertEquals; +import static org.junit.jupiter.api.Assertions.assertThrows; + +/** + * The trie's transition function held against a naive map-backed reference over randomized + * vocabularies, so the hybrid direct-table and linear-scan node layouts are proven to enumerate + * identical transitions; the encoder's correctness rests on that equivalence. + */ +class PieceTrieTest { + + @Test + void testStepsMatchAMapBackedReferenceOverRandomVocabularies() { + final Random random = new Random(7); + for (int round = 0; round < 20; round++) { + // Piece count crosses the direct-table threshold in both directions, so wide and narrow + // roots are both exercised. + final int pieceCount = 2 + random.nextInt(60); + final Set keys = new HashSet<>(); + while (keys.size() < pieceCount) { + final int length = 1 + random.nextInt(5); + final StringBuilder key = new StringBuilder(); + for (int i = 0; i < length; i++) { + key.append((char) ('a' + random.nextInt(random.nextBoolean() ? 26 : 4))); + } + keys.add(key.toString()); + } + final byte[][] pieces = new byte[keys.size()][]; + final int[] ids = new int[keys.size()]; + final Map reference = new HashMap<>(); + int index = 0; + for (final String key : keys) { + pieces[index] = key.getBytes(StandardCharsets.UTF_8); + ids[index] = index; + reference.put(key, index); + index++; + } + final PieceTrie trie = PieceTrie.build(pieces, ids); + + // Every walk over random query strings must accept exactly the reference's prefixes. + for (int query = 0; query < 200; query++) { + final int length = 1 + random.nextInt(8); + final StringBuilder text = new StringBuilder(); + for (int i = 0; i < length; i++) { + text.append((char) ('a' + random.nextInt(6))); + } + int node = trie.root(); + for (int i = 0; i < length; i++) { + node = trie.step(node, (byte) text.charAt(i)); + final String prefix = text.substring(0, i + 1); + final boolean anyKeyHasPrefix = + keys.stream().anyMatch(k -> k.startsWith(prefix)); + if (node == PieceTrie.DEAD) { + assertEquals(false, anyKeyHasPrefix, "dead end despite live prefix: " + prefix); + break; + } + final Integer expected = reference.get(prefix); + assertEquals(expected == null ? -1 : expected, trie.value(node), + "value mismatch at prefix: " + prefix); + } + } + } + } + + @Test + void testWideRootDispatchesAllByteValues() { + // 200 distinct single-byte pieces force the direct-table layout at the root. + final byte[][] pieces = new byte[200][]; + final int[] ids = new int[200]; + for (int i = 0; i < 200; i++) { + pieces[i] = new byte[] {(byte) (i + 20)}; + ids[i] = i; + } + final PieceTrie trie = PieceTrie.build(pieces, ids); + for (int b = 0; b < 256; b++) { + final int node = trie.step(trie.root(), (byte) b); + if (b >= 20 && b < 220) { + assertEquals(b - 20, trie.value(node)); + } else { + assertEquals(PieceTrie.DEAD, node); + } + } + } + + @Test + void testDuplicatePiecesFailLoudly() { + final byte[][] pieces = {{'a'}, {'a'}}; + assertThrows(IllegalArgumentException.class, () -> PieceTrie.build(pieces, new int[] {0, 1})); + } +} From 01863a8a5631f01c59f35d8882e29e06e6a941e6 Mon Sep 17 00:00:00 2001 From: Kristian Rickert Date: Sat, 11 Jul 2026 10:46:56 -0400 Subject: [PATCH 3/9] OPENNLP-1885: Move the subword contract into opennlp-api SubwordTokenizer and SubwordPiece move to opennlp.tools.tokenize, next to Tokenizer and WordpieceTokenizer, matching where every other seam of this round lives. The opennlp-subword module keeps only the SentencePiece implementation. --- .../src/main/java/opennlp/tools/tokenize}/SubwordPiece.java | 2 +- .../main/java/opennlp/tools/tokenize}/SubwordTokenizer.java | 4 ++-- .../opennlp/subword/sentencepiece/SentencePieceTokenizer.java | 4 ++-- .../subword/sentencepiece/SentencePieceAlignmentTest.java | 2 +- .../sentencepiece/SentencePieceModelValidationTest.java | 2 +- .../subword/sentencepiece/SentencePieceParityTest.java | 2 +- .../subword/sentencepiece/SentencePieceRealModelEvalTest.java | 2 +- 7 files changed, 9 insertions(+), 9 deletions(-) rename {opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword => opennlp-api/src/main/java/opennlp/tools/tokenize}/SubwordPiece.java (98%) rename {opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword => opennlp-api/src/main/java/opennlp/tools/tokenize}/SubwordTokenizer.java (96%) diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/SubwordPiece.java b/opennlp-api/src/main/java/opennlp/tools/tokenize/SubwordPiece.java similarity index 98% rename from opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/SubwordPiece.java rename to opennlp-api/src/main/java/opennlp/tools/tokenize/SubwordPiece.java index 1791583101..7f790650cf 100644 --- a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/SubwordPiece.java +++ b/opennlp-api/src/main/java/opennlp/tools/tokenize/SubwordPiece.java @@ -14,7 +14,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package opennlp.subword; +package opennlp.tools.tokenize; import opennlp.tools.util.Span; diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/SubwordTokenizer.java b/opennlp-api/src/main/java/opennlp/tools/tokenize/SubwordTokenizer.java similarity index 96% rename from opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/SubwordTokenizer.java rename to opennlp-api/src/main/java/opennlp/tools/tokenize/SubwordTokenizer.java index ea31342bf2..5c371287f2 100644 --- a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/SubwordTokenizer.java +++ b/opennlp-api/src/main/java/opennlp/tools/tokenize/SubwordTokenizer.java @@ -14,7 +14,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package opennlp.subword; +package opennlp.tools.tokenize; import java.util.List; @@ -24,7 +24,7 @@ * *

Subword tokenization is the input layer of modern sequence models: text is decomposed into * pieces from a trained vocabulary so that any input, including words never seen in training, maps - * to a bounded id space. Unlike a linguistic {@code Tokenizer}, the segmentation is + * to a bounded id space. Unlike a linguistic {@link Tokenizer}, the segmentation is * vocabulary-driven, and the pieces are in the model's normalized form rather than substrings of * the input. The offsets carried by each {@link SubwordPiece} are what tie the two worlds * together: they always refer to the caller's original text.

diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceTokenizer.java b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceTokenizer.java index 5fa7855fde..5b39c395a9 100644 --- a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceTokenizer.java +++ b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceTokenizer.java @@ -26,8 +26,8 @@ import java.util.List; import java.util.Map; -import opennlp.subword.SubwordPiece; -import opennlp.subword.SubwordTokenizer; +import opennlp.tools.tokenize.SubwordPiece; +import opennlp.tools.tokenize.SubwordTokenizer; import opennlp.tools.util.normalizer.AlignedText; import opennlp.tools.util.normalizer.Alignment; import opennlp.tools.util.normalizer.OffsetAwareNormalizer; diff --git a/opennlp-extensions/opennlp-subword/src/test/java/opennlp/subword/sentencepiece/SentencePieceAlignmentTest.java b/opennlp-extensions/opennlp-subword/src/test/java/opennlp/subword/sentencepiece/SentencePieceAlignmentTest.java index 02815aa372..41e2a906fc 100644 --- a/opennlp-extensions/opennlp-subword/src/test/java/opennlp/subword/sentencepiece/SentencePieceAlignmentTest.java +++ b/opennlp-extensions/opennlp-subword/src/test/java/opennlp/subword/sentencepiece/SentencePieceAlignmentTest.java @@ -22,7 +22,7 @@ import org.junit.jupiter.params.ParameterizedTest; import org.junit.jupiter.params.provider.ValueSource; -import opennlp.subword.SubwordPiece; +import opennlp.tools.tokenize.SubwordPiece; import opennlp.tools.util.Span; import opennlp.tools.util.normalizer.AlignedText; diff --git a/opennlp-extensions/opennlp-subword/src/test/java/opennlp/subword/sentencepiece/SentencePieceModelValidationTest.java b/opennlp-extensions/opennlp-subword/src/test/java/opennlp/subword/sentencepiece/SentencePieceModelValidationTest.java index 8f0b6c64a9..66595eff8b 100644 --- a/opennlp-extensions/opennlp-subword/src/test/java/opennlp/subword/sentencepiece/SentencePieceModelValidationTest.java +++ b/opennlp-extensions/opennlp-subword/src/test/java/opennlp/subword/sentencepiece/SentencePieceModelValidationTest.java @@ -30,7 +30,7 @@ import org.junit.jupiter.api.Test; -import opennlp.subword.SubwordPiece; +import opennlp.tools.tokenize.SubwordPiece; import static org.junit.jupiter.api.Assertions.assertEquals; import static org.junit.jupiter.api.Assertions.assertThrows; diff --git a/opennlp-extensions/opennlp-subword/src/test/java/opennlp/subword/sentencepiece/SentencePieceParityTest.java b/opennlp-extensions/opennlp-subword/src/test/java/opennlp/subword/sentencepiece/SentencePieceParityTest.java index 4485e13f16..7d970e3c5e 100644 --- a/opennlp-extensions/opennlp-subword/src/test/java/opennlp/subword/sentencepiece/SentencePieceParityTest.java +++ b/opennlp-extensions/opennlp-subword/src/test/java/opennlp/subword/sentencepiece/SentencePieceParityTest.java @@ -30,7 +30,7 @@ import org.junit.jupiter.params.ParameterizedTest; import org.junit.jupiter.params.provider.ValueSource; -import opennlp.subword.SubwordPiece; +import opennlp.tools.tokenize.SubwordPiece; import static org.junit.jupiter.api.Assertions.assertEquals; import static org.junit.jupiter.api.Assertions.assertNotNull; diff --git a/opennlp-extensions/opennlp-subword/src/test/java/opennlp/subword/sentencepiece/SentencePieceRealModelEvalTest.java b/opennlp-extensions/opennlp-subword/src/test/java/opennlp/subword/sentencepiece/SentencePieceRealModelEvalTest.java index e4acccd499..1255827ba1 100644 --- a/opennlp-extensions/opennlp-subword/src/test/java/opennlp/subword/sentencepiece/SentencePieceRealModelEvalTest.java +++ b/opennlp-extensions/opennlp-subword/src/test/java/opennlp/subword/sentencepiece/SentencePieceRealModelEvalTest.java @@ -26,7 +26,7 @@ import org.junit.jupiter.api.Test; -import opennlp.subword.SubwordPiece; +import opennlp.tools.tokenize.SubwordPiece; import static org.junit.jupiter.api.Assertions.assertEquals; import static org.junit.jupiter.api.Assertions.assertTrue; From 3ace752183646c52ef91c66e9fc546757a183244 Mon Sep 17 00:00:00 2001 From: Kristian Rickert Date: Sun, 12 Jul 2026 09:20:57 -0400 Subject: [PATCH 4/9] OPENNLP-1885: Add WordpieceEncoder and fold the unreleased BertTokenizer into it WordpieceEncoder in opennlp-api runs the full BERT tokenization pipeline as a SubwordTokenizer: every piece carries its vocabulary id and the span of the original text, surviving the normalization steps that change, insert, and remove characters. Content is computed with the same library calls the previous pipeline made; offsets come from a per-code-point rerun, with contextual case mappings (Greek final sigma) falling back to word-wide spans that widen but never misplace. List and map constructors cover line-number and explicit-id vocabularies. BertTokenizer, unreleased and superseded, is removed. The dl tokenizer creation builds on the encoder behind the existing Tokenizer plumbing via a package-private adapter with unchanged special-token selection, and a vocabulary missing its special tokens now fails at construction instead of at the first id mapping, pinned by a test. Parity is enforced twice: a differential suite against the reference pipeline (kept test-only as ReferenceBertPipeline) over a curated corpus plus 800 randomized inputs, and the removed class's reference token sequences ported case for case. WordpieceTokenizer is untouched. --- .../tools/tokenize/BertNormalization.java | 2 +- .../opennlp/tools/tokenize/BertTokenizer.java | 211 --------- .../tools/tokenize/WordpieceEncoder.java | 416 ++++++++++++++++++ .../src/main/java/opennlp/dl/AbstractDL.java | 40 +- .../java/opennlp/dl/EncoderTokenizer.java | 53 +++ .../dl/doccat/DocumentCategorizerDL.java | 2 +- .../opennlp/dl/namefinder/NameFinderDL.java | 2 +- .../opennlp/dl/vectors/SentenceVectorsDL.java | 2 +- .../java/opennlp/dl/CreateTokenizerTest.java | 31 +- .../tools/tokenize/ReferenceBertPipeline.java | 92 ++++ ...rdpieceEncoderReferenceSequencesTest.java} | 87 ++-- .../tools/tokenize/WordpieceEncoderTest.java | 218 +++++++++ 12 files changed, 872 insertions(+), 284 deletions(-) delete mode 100644 opennlp-api/src/main/java/opennlp/tools/tokenize/BertTokenizer.java create mode 100644 opennlp-api/src/main/java/opennlp/tools/tokenize/WordpieceEncoder.java create mode 100644 opennlp-core/opennlp-ml/opennlp-dl/src/main/java/opennlp/dl/EncoderTokenizer.java create mode 100644 opennlp-core/opennlp-runtime/src/test/java/opennlp/tools/tokenize/ReferenceBertPipeline.java rename opennlp-core/opennlp-runtime/src/test/java/opennlp/tools/tokenize/{BertTokenizerTest.java => WordpieceEncoderReferenceSequencesTest.java} (57%) create mode 100644 opennlp-core/opennlp-runtime/src/test/java/opennlp/tools/tokenize/WordpieceEncoderTest.java diff --git a/opennlp-api/src/main/java/opennlp/tools/tokenize/BertNormalization.java b/opennlp-api/src/main/java/opennlp/tools/tokenize/BertNormalization.java index 455f67f110..1cbfffe312 100644 --- a/opennlp-api/src/main/java/opennlp/tools/tokenize/BertNormalization.java +++ b/opennlp-api/src/main/java/opennlp/tools/tokenize/BertNormalization.java @@ -19,7 +19,7 @@ /** * Character classifications and text transforms of the reference BERT - * {@code BasicTokenizer}, shared by {@link BertTokenizer} and + * {@code BasicTokenizer}, shared by {@link WordpieceEncoder} and * {@link WordpieceTokenizer}. */ final class BertNormalization { diff --git a/opennlp-api/src/main/java/opennlp/tools/tokenize/BertTokenizer.java b/opennlp-api/src/main/java/opennlp/tools/tokenize/BertTokenizer.java deleted file mode 100644 index 5517548a42..0000000000 --- a/opennlp-api/src/main/java/opennlp/tools/tokenize/BertTokenizer.java +++ /dev/null @@ -1,211 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://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. - */ - -package opennlp.tools.tokenize; - -import java.text.Normalizer; -import java.util.Locale; -import java.util.Objects; -import java.util.Set; - -import opennlp.tools.util.Span; - -/** - * A {@link Tokenizer} implementation of the full BERT tokenization pipeline: - * basic tokenization (text normalization) followed by wordpiece tokenization. - *

- * The basic tokenization stage reproduces the reference BERT - * {@code BasicTokenizer}: - *

    - *
  1. Removal of control characters and normalization of all whitespace - * to single spaces.
    2. - *
  2. Whitespace isolation of CJK ideographs.
    3. - *
  3. For uncased models: lower casing and accent stripping - * (Unicode NFD decomposition with removal of combining marks).
    4. - *
  4. Isolation of every punctuation character as its own token.
    5. - *
- * The normalized text is then split into subwords by a - * {@link WordpieceTokenizer} sharing the same vocabulary and special tokens. - *

- * This pipeline is required for correct results with BERT-style models: - * feeding raw text directly to {@link WordpieceTokenizer} maps every token - * that does not literally appear in the vocabulary - for uncased models that - * includes every capitalized word - to the unknown token. - *

- * Whether to use the lower casing variant is a property of the model: uncased - * models (for example {@code bert-base-uncased} and the - * {@code sentence-transformers} models derived from it) require it, cased - * models must not use it. Accent stripping is coupled to lower casing, as in - * the reference implementation's default ({@code strip_accents} follows - * {@code do_lower_case} unless overridden). - *

- * For reference see: - *

- * - * @see WordpieceTokenizer - */ -public class BertTokenizer implements Tokenizer { - - /** - * Maximum characters per word before the word is replaced with the unknown - * token, matching the reference BERT implementation. - */ - private static final int MAX_WORD_CHARACTERS = 100; - - private final WordpieceTokenizer wordpieceTokenizer; - private final boolean lowerCase; - - /** - * Initializes a {@link BertTokenizer} for an uncased BERT model, - * with lower casing and accent stripping enabled. - * - * @param vocabulary The wordpiece vocabulary. Must not be {@code null}. - */ - public BertTokenizer(Set vocabulary) { - this(vocabulary, true); - } - - /** - * Initializes a {@link BertTokenizer} with BERT special tokens. - * - * @param vocabulary The wordpiece vocabulary. Must not be {@code null}. - * @param lowerCase {@code true} for uncased models (lower casing and accent - * stripping), {@code false} for cased models. - */ - public BertTokenizer(Set vocabulary, boolean lowerCase) { - this(vocabulary, lowerCase, WordpieceTokenizer.BERT_CLS_TOKEN, - WordpieceTokenizer.BERT_SEP_TOKEN, WordpieceTokenizer.BERT_UNK_TOKEN); - } - - /** - * Initializes a {@link BertTokenizer} with custom special tokens, for models - * like RoBERTa that do not use the BERT defaults. - * - * @param vocabulary The wordpiece vocabulary. Must not be {@code null}. - * @param lowerCase {@code true} for uncased models (lower casing and - * accent stripping), {@code false} for cased models. - * @param classificationToken The CLS token. - * @param separatorToken The SEP token. - * @param unknownToken The UNK token. - */ - public BertTokenizer(Set vocabulary, boolean lowerCase, - String classificationToken, String separatorToken, String unknownToken) { - Objects.requireNonNull(vocabulary, "vocabulary must not be null"); - Objects.requireNonNull(classificationToken, "classificationToken must not be null"); - Objects.requireNonNull(separatorToken, "separatorToken must not be null"); - Objects.requireNonNull(unknownToken, "unknownToken must not be null"); - this.wordpieceTokenizer = new WordpieceTokenizer(vocabulary, - classificationToken, separatorToken, unknownToken, MAX_WORD_CHARACTERS); - this.lowerCase = lowerCase; - } - - /** - * Tokenizes the given text into wordpieces, surrounded by the classification - * and separator tokens. - * - * @param text The text to tokenize. Must not be {@code null}. - * - * @return The wordpiece tokens. - */ - @Override - public String[] tokenize(String text) { - return wordpieceTokenizer.tokenize(normalize(text)); - } - - /** - * Not supported: wordpiece tokens (subwords, {@code ##} continuations and - * special tokens) have no faithful character spans in the original text. - * - * @throws UnsupportedOperationException Always. - */ - @Override - public Span[] tokenizePos(String text) { - throw new UnsupportedOperationException( - "Wordpiece tokens cannot be mapped to character spans of the original text"); - } - - /** - * Applies the BERT basic tokenization (normalization) stage. - * - * @param text The text to normalize. Must not be {@code null}. - * - * @return The normalized text, ready for wordpiece tokenization. - */ - String normalize(String text) { - Objects.requireNonNull(text, "text must not be null"); - String normalized = cleanText(text); - normalized = isolateCjkCharacters(normalized); - if (lowerCase) { - normalized = stripAccents(normalized.toLowerCase(Locale.ROOT)); - } - return BertNormalization.isolatePunctuation(normalized); - } - - /** - * Removes invalid and control characters and normalizes all whitespace - * characters to plain spaces. - */ - private static String cleanText(String text) { - final StringBuilder cleaned = new StringBuilder(text.length()); - text.codePoints().forEach(codePoint -> { - if (codePoint == 0 || codePoint == 0xFFFD || BertNormalization.isControl(codePoint)) { - return; - } - if (BertNormalization.isWhitespace(codePoint)) { - cleaned.append(' '); - } else { - cleaned.appendCodePoint(codePoint); - } - }); - return cleaned.toString(); - } - - /** - * Surrounds every CJK ideograph with spaces, so each ideograph becomes its - * own token, matching the reference BERT treatment of Chinese text. - */ - private static String isolateCjkCharacters(String text) { - final StringBuilder spaced = new StringBuilder(text.length()); - text.codePoints().forEach(codePoint -> { - if (BertNormalization.isCjk(codePoint)) { - spaced.append(' ').appendCodePoint(codePoint).append(' '); - } else { - spaced.appendCodePoint(codePoint); - } - }); - return spaced.toString(); - } - - /** - * Removes accents by Unicode NFD decomposition followed by removal of - * combining marks ({@code Mn}). - */ - private static String stripAccents(String text) { - final String decomposed = Normalizer.normalize(text, Normalizer.Form.NFD); - final StringBuilder stripped = new StringBuilder(decomposed.length()); - decomposed.codePoints().forEach(codePoint -> { - if (Character.getType(codePoint) != Character.NON_SPACING_MARK) { - stripped.appendCodePoint(codePoint); - } - }); - return stripped.toString(); - } - -} diff --git a/opennlp-api/src/main/java/opennlp/tools/tokenize/WordpieceEncoder.java b/opennlp-api/src/main/java/opennlp/tools/tokenize/WordpieceEncoder.java new file mode 100644 index 0000000000..e74c23205d --- /dev/null +++ b/opennlp-api/src/main/java/opennlp/tools/tokenize/WordpieceEncoder.java @@ -0,0 +1,416 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://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. + */ +package opennlp.tools.tokenize; + +import java.text.Normalizer; +import java.util.ArrayList; +import java.util.Arrays; +import java.util.HashMap; +import java.util.HashSet; +import java.util.List; +import java.util.Locale; +import java.util.Map; +import java.util.Set; + +/** + * A {@link SubwordTokenizer} running the full BERT tokenization pipeline of the reference + * implementation: basic tokenization (control removal, whitespace normalization, CJK + * isolation, optional lower casing with accent stripping, punctuation isolation) followed by + * greedy longest-match wordpiece segmentation. + * + *

Every piece carries its vocabulary id and the span of the original text it came + * from, surviving the normalization steps that change, insert, and remove characters. The + * classification and separator pieces frame every encoding, carrying empty spans at the + * text's boundaries, so {@link #encode(CharSequence)} is never empty.

+ * + *

Ids follow the line-number convention of BERT {@code vocab.txt} files: with the list + * constructors a piece's id is its index, and with the map constructor the ids are given + * explicitly. The classification, separator, and unknown tokens must all be present in the + * vocabulary, because every emitted piece must have an id.

+ * + *

Instances are immutable and safe for concurrent use by multiple threads.

+ * + * @see WordpieceTokenizer + */ +public final class WordpieceEncoder implements SubwordTokenizer { + + // The reference implementation's limit: longer words become the unknown piece. + private static final int MAX_WORD_CHARACTERS = 100; + + private final Set vocabulary; + private final Map ids; + private final boolean lowerCase; + private final String classificationToken; + private final String separatorToken; + private final String unknownToken; + private final int classificationId; + private final int separatorId; + private final int unknownId; + + /** + * Instantiates an encoder for an uncased BERT model with the BERT special tokens. + * + * @param vocabulary The ordered vocabulary; a piece's id is its index. Must not be null, + * must not contain nulls or duplicates. + */ + public WordpieceEncoder(List vocabulary) { + this(vocabulary, true); + } + + /** + * Instantiates an encoder with the BERT special tokens. + * + * @param vocabulary The ordered vocabulary; a piece's id is its index. Must not be null, + * must not contain nulls or duplicates. + * @param lowerCase True for uncased models (lower casing and accent stripping), false for + * cased models. + */ + public WordpieceEncoder(List vocabulary, boolean lowerCase) { + this(vocabulary, lowerCase, WordpieceTokenizer.BERT_CLS_TOKEN, + WordpieceTokenizer.BERT_SEP_TOKEN, WordpieceTokenizer.BERT_UNK_TOKEN); + } + + /** + * Instantiates an encoder with custom special tokens, for models that do not use the BERT + * defaults. + * + * @param vocabulary The ordered vocabulary; a piece's id is its index. Must not be + * null, must not contain nulls or duplicates. + * @param lowerCase True for uncased models (lower casing and accent stripping), + * false for cased models. + * @param classificationToken The CLS token; must be in the vocabulary. + * @param separatorToken The SEP token; must be in the vocabulary. + * @param unknownToken The UNK token; must be in the vocabulary. + * @throws IllegalArgumentException Thrown if any argument is null, the vocabulary contains + * a null or duplicate entry, or a special token is missing from the vocabulary. + */ + public WordpieceEncoder(List vocabulary, boolean lowerCase, + String classificationToken, String separatorToken, + String unknownToken) { + this(byPiece(vocabulary), lowerCase, classificationToken, separatorToken, unknownToken); + } + + /** + * Instantiates an encoder from an explicit piece-to-id mapping, for vocabularies whose ids + * are not contiguous line numbers. + * + * @param vocabularyIds The piece-to-id mapping. Must not be null, must not contain + * null keys or values. + * @param lowerCase True for uncased models (lower casing and accent stripping), + * false for cased models. + * @param classificationToken The CLS token; must be in the vocabulary. + * @param separatorToken The SEP token; must be in the vocabulary. + * @param unknownToken The UNK token; must be in the vocabulary. + * @throws IllegalArgumentException Thrown if any argument is null, the mapping contains a + * null key or value, or a special token is missing from the vocabulary. + */ + public WordpieceEncoder(Map vocabularyIds, boolean lowerCase, + String classificationToken, String separatorToken, + String unknownToken) { + if (vocabularyIds == null || classificationToken == null || separatorToken == null + || unknownToken == null) { + throw new IllegalArgumentException("The vocabulary and special tokens must not be null."); + } + final Map byPiece = new HashMap<>(vocabularyIds.size() * 2); + for (final Map.Entry entry : vocabularyIds.entrySet()) { + if (entry.getKey() == null || entry.getValue() == null) { + throw new IllegalArgumentException( + "The vocabulary must not contain null pieces or ids: " + entry); + } + byPiece.put(entry.getKey(), entry.getValue()); + } + this.vocabulary = new HashSet<>(byPiece.keySet()); + this.ids = byPiece; + this.lowerCase = lowerCase; + this.classificationToken = classificationToken; + this.separatorToken = separatorToken; + this.unknownToken = unknownToken; + this.classificationId = requiredId(byPiece, classificationToken); + this.separatorId = requiredId(byPiece, separatorToken); + this.unknownId = requiredId(byPiece, unknownToken); + } + + private static Map byPiece(List vocabulary) { + if (vocabulary == null) { + throw new IllegalArgumentException("The vocabulary must not be null."); + } + final Map byPiece = new HashMap<>(vocabulary.size() * 2); + for (int id = 0; id < vocabulary.size(); id++) { + final String piece = vocabulary.get(id); + if (piece == null) { + throw new IllegalArgumentException("The vocabulary contains null at index " + id + "."); + } + if (byPiece.putIfAbsent(piece, id) != null) { + throw new IllegalArgumentException("The vocabulary contains '" + piece + + "' more than once; ids would be ambiguous."); + } + } + return byPiece; + } + + private static int requiredId(Map ids, String specialToken) { + final Integer id = ids.get(specialToken); + if (id == null) { + throw new IllegalArgumentException("The special token '" + specialToken + + "' is not in the vocabulary; every emitted piece must have an id."); + } + return id; + } + + @Override + public List encode(CharSequence text) { + if (text == null) { + throw new IllegalArgumentException("The text must not be null."); + } + final String original = text.toString(); + + // The normalized text, one original-text range per char, built through the reference + // pipeline's transformations in the reference order. + MappedText mapped = cleanAndIsolateCjk(original); + if (lowerCase) { + mapped = lowerCaseAndStripAccents(mapped); + } + mapped = isolatePunctuation(mapped); + + final List pieces = new ArrayList<>(); + pieces.add(new SubwordPiece(classificationToken, classificationId, 0, 0)); + int from = 0; + while (from < mapped.length) { + if (mapped.chars[from] == ' ') { + from++; + continue; + } + int to = from; + while (to < mapped.length && mapped.chars[to] != ' ') { + to++; + } + encodeWord(mapped, from, to, pieces); + from = to; + } + pieces.add(new SubwordPiece(separatorToken, separatorId, + original.length(), original.length())); + return pieces; + } + + // Greedy longest-match wordpiece over one whitespace-delimited word, exactly as + // WordpieceTokenizer#tokenize segments it; pieces are emitted only if the whole word is + // representable, otherwise the word becomes a single unknown piece. + private void encodeWord(MappedText mapped, int from, int to, List pieces) { + final int wordStart = mapped.starts[from]; + final int wordEnd = mapped.ends[to - 1]; + if (to - from > MAX_WORD_CHARACTERS) { + pieces.add(new SubwordPiece(unknownToken, unknownId, wordStart, wordEnd)); + return; + } + final List wordPieces = new ArrayList<>(); + int start = from; + boolean found = true; + while (start < to) { + int end = to; + found = false; + while (start < end) { + String substring = new String(mapped.chars, start, end - start); + if (start > from) { + substring = "##" + substring; + } + if (vocabulary.contains(substring)) { + wordPieces.add(new SubwordPiece(substring, ids.get(substring), + mapped.starts[start], mapped.ends[end - 1])); + start = end; + found = true; + break; + } + end--; + } + if (!found) { + break; + } + } + if (found) { + pieces.addAll(wordPieces); + } else { + pieces.add(new SubwordPiece(unknownToken, unknownId, wordStart, wordEnd)); + } + } + + // The normalized text with, for every char, the original-text range it came from. Characters + // inserted by the pipeline (isolation spaces) carry an empty range at the insertion point. + private static final class MappedText { + private char[] chars; + private int[] starts; + private int[] ends; + private int length; + + private MappedText(int capacity) { + chars = new char[capacity]; + starts = new int[capacity]; + ends = new int[capacity]; + } + + private void add(char c, int originalStart, int originalEnd) { + if (length == chars.length) { + final int capacity = Math.max(16, length * 2); + chars = Arrays.copyOf(chars, capacity); + starts = Arrays.copyOf(starts, capacity); + ends = Arrays.copyOf(ends, capacity); + } + chars[length] = c; + starts[length] = originalStart; + ends[length] = originalEnd; + length++; + } + + private void add(String s, int originalStart, int originalEnd) { + for (int i = 0; i < s.length(); i++) { + add(s.charAt(i), originalStart, originalEnd); + } + } + } + + // Text cleaning and CJK isolation in one pass: both are per-code-point + // transforms, and a CJK code point is never dropped or whitespace, so the fusion is exact. + private static MappedText cleanAndIsolateCjk(String original) { + final MappedText out = new MappedText(original.length() + 16); + int i = 0; + while (i < original.length()) { + final int codePoint = original.codePointAt(i); + final int width = Character.charCount(codePoint); + if (codePoint == 0 || codePoint == 0xFFFD || BertNormalization.isControl(codePoint)) { + i += width; + continue; + } + if (BertNormalization.isWhitespace(codePoint)) { + out.add(' ', i, i + width); + } else if (BertNormalization.isCjk(codePoint)) { + out.add(' ', i, i); + for (int c = 0; c < width; c++) { + out.add(original.charAt(i + c), i, i + width); + } + out.add(' ', i + width, i + width); + } else { + for (int c = 0; c < width; c++) { + out.add(original.charAt(i + c), i, i + width); + } + } + i += width; + } + return out; + } + + // BertNormalization#isolatePunctuation with the range of each char preserved. + private static MappedText isolatePunctuation(MappedText in) { + final MappedText out = new MappedText(in.length + 16); + int i = 0; + while (i < in.length) { + final int codePoint = codePointAt(in, i); + final int width = Character.charCount(codePoint); + if (BertNormalization.isPunctuation(codePoint)) { + out.add(' ', in.starts[i], in.starts[i]); + for (int c = 0; c < width; c++) { + out.add(in.chars[i + c], in.starts[i + c], in.ends[i + c]); + } + out.add(' ', in.ends[i + width - 1], in.ends[i + width - 1]); + } else { + for (int c = 0; c < width; c++) { + out.add(in.chars[i + c], in.starts[i + c], in.ends[i + c]); + } + } + i += width; + } + return out; + } + + // Lower casing and accent stripping with ranges preserved. The content is computed + // with whole-run library calls, applied per whitespace run + // (equivalent on whole strings: no case mapping context crosses a space, and NFD + // composition is boundary-safe at a space). The per-char ranges are reconstructed from a + // per-code-point rerun of the same transforms; if a contextual case mapping (Greek final + // sigma) makes the rerun disagree with the authoritative content, every char of that run + // falls back to the run's full range, which widens spans but never misplaces them. + private static MappedText lowerCaseAndStripAccents(MappedText in) { + final MappedText out = new MappedText(in.length + 16); + int from = 0; + while (from < in.length) { + if (in.chars[from] == ' ') { + out.add(' ', in.starts[from], in.ends[from]); + from++; + continue; + } + int to = from; + while (to < in.length && in.chars[to] != ' ') { + to++; + } + transformRun(in, from, to, out); + from = to; + } + return out; + } + + private static void transformRun(MappedText in, int from, int to, MappedText out) { + final String run = new String(in.chars, from, to - from); + final String content = stripAccents(run.toLowerCase(Locale.ROOT)); + + // Rerun per code point to learn how many output chars each input code point produces. + final StringBuilder rerun = new StringBuilder(content.length()); + final int[] produced = new int[to - from]; + int i = from; + while (i < to) { + final int codePoint = codePointAt(in, i); + final int width = Character.charCount(codePoint); + final String transformed = stripAccents( + new String(Character.toChars(codePoint)).toLowerCase(Locale.ROOT)); + rerun.append(transformed); + produced[i - from] = transformed.length(); + i += width; + } + + if (rerun.toString().equals(content)) { + int at = from; + int emitted = 0; + while (at < to) { + final int width = Character.charCount(codePointAt(in, at)); + for (int c = 0; c < produced[at - from]; c++) { + out.add(content.charAt(emitted++), in.starts[at], in.ends[at + width - 1]); + } + at += width; + } + } else { + // Contextual case mapping changed the content; the run's chars share the run's range. + out.add(content, in.starts[from], in.ends[to - 1]); + } + } + + private static String stripAccents(String text) { + final String decomposed = Normalizer.normalize(text, Normalizer.Form.NFD); + final StringBuilder stripped = new StringBuilder(decomposed.length()); + decomposed.codePoints().forEach(codePoint -> { + if (Character.getType(codePoint) != Character.NON_SPACING_MARK) { + stripped.appendCodePoint(codePoint); + } + }); + return stripped.toString(); + } + + private static int codePointAt(MappedText text, int index) { + final char c = text.chars[index]; + if (Character.isHighSurrogate(c) && index + 1 < text.length + && Character.isLowSurrogate(text.chars[index + 1])) { + return Character.toCodePoint(c, text.chars[index + 1]); + } + return c; + } +} diff --git a/opennlp-core/opennlp-ml/opennlp-dl/src/main/java/opennlp/dl/AbstractDL.java b/opennlp-core/opennlp-ml/opennlp-dl/src/main/java/opennlp/dl/AbstractDL.java index 5b61fc6e88..2f5b4a936c 100644 --- a/opennlp-core/opennlp-ml/opennlp-dl/src/main/java/opennlp/dl/AbstractDL.java +++ b/opennlp-core/opennlp-ml/opennlp-dl/src/main/java/opennlp/dl/AbstractDL.java @@ -36,8 +36,8 @@ import ai.onnxruntime.OrtException; import ai.onnxruntime.OrtSession; -import opennlp.tools.tokenize.BertTokenizer; import opennlp.tools.tokenize.Tokenizer; +import opennlp.tools.tokenize.WordpieceEncoder; import opennlp.tools.tokenize.WordpieceTokenizer; import opennlp.tools.util.Span; import opennlp.tools.util.normalizer.AlignedText; @@ -105,7 +105,7 @@ protected AbstractDL(final File model, final File vocabulary, final OrtSession createdSession = env.createSession(model.getPath(), sessionOptions); try { this.vocab = Map.copyOf(loadVocabFile(vocabulary)); - this.tokenizer = createBertTokenizer(vocab, lowerCase); + this.tokenizer = createPipelineTokenizer(vocab, lowerCase); } catch (IOException | RuntimeException e) { // Vocabulary/tokenizer init failed after the native session was created; close it // so a partially constructed instance never leaks the ONNX session. @@ -136,7 +136,7 @@ protected AbstractDL(final OrtEnvironment env, final OrtSession session, this.env = env; this.session = session; this.vocab = vocab; - this.tokenizer = createBertTokenizer(vocab, lowerCase); + this.tokenizer = createPipelineTokenizer(vocab, lowerCase); } /** @@ -238,38 +238,44 @@ static WordpieceTokenizer createWordpieceTokenizer( } /** - * Creates a {@link BertTokenizer} that performs the full BERT tokenization - * pipeline: basic tokenization (text normalization) followed by wordpiece. - * The special tokens are selected based on the vocabulary: if it contains - * RoBERTa-style tokens, those are used, otherwise the BERT defaults. + * Creates a {@link Tokenizer} that performs the full BERT tokenization + * pipeline: basic tokenization (text normalization) followed by wordpiece, + * backed by a {@link WordpieceEncoder}. The special tokens are selected + * based on the vocabulary: if it contains RoBERTa-style tokens, those are + * used, otherwise the BERT defaults. * * @param vocab The vocabulary map. * @param lowerCase {@code true} for uncased models (lower casing and accent * stripping), {@code false} for cased models. - * @return A configured {@link BertTokenizer}. - * @throws IllegalArgumentException Thrown if a RoBERTa-style vocabulary - * contains no supported unknown token. + * @return A configured {@link Tokenizer}. + * @throws IllegalArgumentException Thrown if the selected special tokens + * are not all present in the vocabulary. */ - protected BertTokenizer createTokenizer( + protected Tokenizer createTokenizer( final Map vocab, final boolean lowerCase) { - return createBertTokenizer(vocab, lowerCase); + return createPipelineTokenizer(vocab, lowerCase); } - static BertTokenizer createBertTokenizer( + static Tokenizer createPipelineTokenizer( final Map vocab, final boolean lowerCase) { if (vocab.containsKey( WordpieceTokenizer.ROBERTA_CLS_TOKEN) && vocab.containsKey( WordpieceTokenizer.ROBERTA_SEP_TOKEN)) { - return new BertTokenizer( - vocab.keySet(), + return new EncoderTokenizer(new WordpieceEncoder( + vocab, lowerCase, WordpieceTokenizer.ROBERTA_CLS_TOKEN, WordpieceTokenizer.ROBERTA_SEP_TOKEN, - resolveUnknownToken(vocab)); + resolveUnknownToken(vocab))); } - return new BertTokenizer(vocab.keySet(), lowerCase); + return new EncoderTokenizer(new WordpieceEncoder( + vocab, + lowerCase, + WordpieceTokenizer.BERT_CLS_TOKEN, + WordpieceTokenizer.BERT_SEP_TOKEN, + WordpieceTokenizer.BERT_UNK_TOKEN)); } /** diff --git a/opennlp-core/opennlp-ml/opennlp-dl/src/main/java/opennlp/dl/EncoderTokenizer.java b/opennlp-core/opennlp-ml/opennlp-dl/src/main/java/opennlp/dl/EncoderTokenizer.java new file mode 100644 index 0000000000..43943b010f --- /dev/null +++ b/opennlp-core/opennlp-ml/opennlp-dl/src/main/java/opennlp/dl/EncoderTokenizer.java @@ -0,0 +1,53 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://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. + */ + +package opennlp.dl; + +import opennlp.tools.tokenize.Tokenizer; +import opennlp.tools.tokenize.WordpieceEncoder; +import opennlp.tools.util.Span; + +/** + * Adapts a {@link WordpieceEncoder} to the {@link Tokenizer} plumbing of the inference + * classes: {@link #tokenize(String)} returns the encoder's piece strings. + */ +final class EncoderTokenizer implements Tokenizer { + + private final WordpieceEncoder encoder; + + EncoderTokenizer(final WordpieceEncoder encoder) { + this.encoder = encoder; + } + + @Override + public String[] tokenize(final String text) { + return encoder.encodeToPieces(text); + } + + /** + * Not supported under the {@link Tokenizer} contract, whose spans are expected to contain + * their token's surface form; wordpiece pieces are not substrings of the input. Use + * {@link WordpieceEncoder#encode(CharSequence)} for pieces with original-text spans. + * + * @throws UnsupportedOperationException Always. + */ + @Override + public Span[] tokenizePos(final String text) { + throw new UnsupportedOperationException( + "Wordpiece tokens cannot be mapped to character spans of the original text"); + } +} diff --git a/opennlp-core/opennlp-ml/opennlp-dl/src/main/java/opennlp/dl/doccat/DocumentCategorizerDL.java b/opennlp-core/opennlp-ml/opennlp-dl/src/main/java/opennlp/dl/doccat/DocumentCategorizerDL.java index a9c6dd9c57..a6c1002686 100644 --- a/opennlp-core/opennlp-ml/opennlp-dl/src/main/java/opennlp/dl/doccat/DocumentCategorizerDL.java +++ b/opennlp-core/opennlp-ml/opennlp-dl/src/main/java/opennlp/dl/doccat/DocumentCategorizerDL.java @@ -51,7 +51,7 @@ * using ONNX models. * *

Tokenization performs BERT basic tokenization (text normalization) - * before wordpiece, see {@link opennlp.tools.tokenize.BertTokenizer}. Input + * before wordpiece, see {@link opennlp.tools.tokenize.WordpieceEncoder}. Input * text is lower cased and accent stripped by default, matching the uncased * models commonly used for classification. For cased models, set * {@link InferenceOptions#setLowerCase(boolean)} to {@code false}.

diff --git a/opennlp-core/opennlp-ml/opennlp-dl/src/main/java/opennlp/dl/namefinder/NameFinderDL.java b/opennlp-core/opennlp-ml/opennlp-dl/src/main/java/opennlp/dl/namefinder/NameFinderDL.java index 2da72e58d2..0d16f6a65f 100644 --- a/opennlp-core/opennlp-ml/opennlp-dl/src/main/java/opennlp/dl/namefinder/NameFinderDL.java +++ b/opennlp-core/opennlp-ml/opennlp-dl/src/main/java/opennlp/dl/namefinder/NameFinderDL.java @@ -51,7 +51,7 @@ * An implementation of {@link opennlp.tools.namefind.TokenNameFinder} that uses ONNX models. * *

Tokenization performs BERT basic tokenization (text normalization) - * before wordpiece, see {@link opennlp.tools.tokenize.BertTokenizer}. Input + * before wordpiece, see {@link opennlp.tools.tokenize.WordpieceEncoder}. Input * text is not lower cased by default, because named entity recognition * models are commonly cased: capitalization is a strong signal for entity * boundaries. For uncased models, set diff --git a/opennlp-core/opennlp-ml/opennlp-dl/src/main/java/opennlp/dl/vectors/SentenceVectorsDL.java b/opennlp-core/opennlp-ml/opennlp-dl/src/main/java/opennlp/dl/vectors/SentenceVectorsDL.java index 6bc76ce183..f1250ea601 100644 --- a/opennlp-core/opennlp-ml/opennlp-dl/src/main/java/opennlp/dl/vectors/SentenceVectorsDL.java +++ b/opennlp-core/opennlp-ml/opennlp-dl/src/main/java/opennlp/dl/vectors/SentenceVectorsDL.java @@ -47,7 +47,7 @@ * so the encoder attended to nothing and the output vectors were * incorrect. Additionally, tokenization now performs BERT basic * tokenization (lower casing and accent stripping by default, see - * {@link opennlp.tools.tokenize.BertTokenizer}) before wordpiece. + * {@link opennlp.tools.tokenize.WordpieceEncoder}) before wordpiece. * Output vectors change with the corrected encoding and tokenization; * any embeddings persisted from the previous behavior are not * comparable with the corrected output and must be re-embedded.

diff --git a/opennlp-core/opennlp-ml/opennlp-dl/src/test/java/opennlp/dl/CreateTokenizerTest.java b/opennlp-core/opennlp-ml/opennlp-dl/src/test/java/opennlp/dl/CreateTokenizerTest.java index 54c4600a8e..5131ae84e7 100644 --- a/opennlp-core/opennlp-ml/opennlp-dl/src/test/java/opennlp/dl/CreateTokenizerTest.java +++ b/opennlp-core/opennlp-ml/opennlp-dl/src/test/java/opennlp/dl/CreateTokenizerTest.java @@ -22,7 +22,7 @@ import org.junit.jupiter.api.Test; -import opennlp.tools.tokenize.BertTokenizer; +import opennlp.tools.tokenize.Tokenizer; import opennlp.tools.tokenize.WordpieceTokenizer; import static org.junit.jupiter.api.Assertions.assertArrayEquals; @@ -52,8 +52,8 @@ private static Map robertaVocab() { } @Test - void testCreatesLowerCasingBertTokenizer() { - final BertTokenizer tokenizer = AbstractDL.createBertTokenizer(bertVocab(), true); + void testCreatesLowerCasingPipelineTokenizer() { + final Tokenizer tokenizer = AbstractDL.createPipelineTokenizer(bertVocab(), true); // Capitalized input must be lower cased before the wordpiece lookup. assertArrayEquals(new String[] { @@ -62,8 +62,8 @@ void testCreatesLowerCasingBertTokenizer() { } @Test - void testCreatesCasePreservingBertTokenizer() { - final BertTokenizer tokenizer = AbstractDL.createBertTokenizer(bertVocab(), false); + void testCreatesCasePreservingPipelineTokenizer() { + final Tokenizer tokenizer = AbstractDL.createPipelineTokenizer(bertVocab(), false); // Without lower casing, capitalized words miss the lowercase-only vocabulary. assertArrayEquals(new String[] { @@ -74,7 +74,7 @@ void testCreatesCasePreservingBertTokenizer() { @Test void testSelectsRobertaSpecialTokens() { - final BertTokenizer tokenizer = AbstractDL.createBertTokenizer(robertaVocab(), false); + final Tokenizer tokenizer = AbstractDL.createPipelineTokenizer(robertaVocab(), false); assertArrayEquals(new String[] { WordpieceTokenizer.ROBERTA_CLS_TOKEN, "hello", WordpieceTokenizer.ROBERTA_UNK_TOKEN, @@ -88,7 +88,7 @@ void testFallsBackToBertUnknownToken() { vocab.remove(WordpieceTokenizer.ROBERTA_UNK_TOKEN); vocab.put(WordpieceTokenizer.BERT_UNK_TOKEN, 2); - final BertTokenizer tokenizer = AbstractDL.createBertTokenizer(vocab, false); + final Tokenizer tokenizer = AbstractDL.createPipelineTokenizer(vocab, false); assertArrayEquals(new String[] { WordpieceTokenizer.ROBERTA_CLS_TOKEN, "hello", WordpieceTokenizer.BERT_UNK_TOKEN, @@ -101,10 +101,25 @@ void testRejectsRobertaVocabularyWithoutUnknownToken() { final Map vocab = robertaVocab(); vocab.remove(WordpieceTokenizer.ROBERTA_UNK_TOKEN); - assertThrows(IllegalArgumentException.class, () -> AbstractDL.createBertTokenizer(vocab, false)); + assertThrows(IllegalArgumentException.class, () -> AbstractDL.createPipelineTokenizer(vocab, false)); assertThrows(IllegalArgumentException.class, () -> AbstractDL.createWordpieceTokenizer(vocab)); } + @Test + void testTokenizePosIsUnsupported() { + final Tokenizer tokenizer = AbstractDL.createPipelineTokenizer(bertVocab(), true); + assertThrows(UnsupportedOperationException.class, () -> tokenizer.tokenizePos("the fox")); + } + + @Test + void testRejectsBertVocabularyMissingSpecialTokensAtCreation() { + final Map vocab = bertVocab(); + vocab.remove(WordpieceTokenizer.BERT_UNK_TOKEN); + + assertThrows(IllegalArgumentException.class, + () -> AbstractDL.createPipelineTokenizer(vocab, true)); + } + @Test void testResolveLowerCaseUsesComponentDefaultWhenUnset() { final InferenceOptions options = new InferenceOptions(); diff --git a/opennlp-core/opennlp-runtime/src/test/java/opennlp/tools/tokenize/ReferenceBertPipeline.java b/opennlp-core/opennlp-runtime/src/test/java/opennlp/tools/tokenize/ReferenceBertPipeline.java new file mode 100644 index 0000000000..867c28dbfa --- /dev/null +++ b/opennlp-core/opennlp-runtime/src/test/java/opennlp/tools/tokenize/ReferenceBertPipeline.java @@ -0,0 +1,92 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://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. + */ +package opennlp.tools.tokenize; + +import java.text.Normalizer; +import java.util.Locale; +import java.util.Set; + +/** + * The reference BERT basic-tokenization stage feeding {@link WordpieceTokenizer}, kept + * test-only as the frozen differential baseline for {@link WordpieceEncoderTest}: the + * encoder's piece sequence must match this pipeline exactly. + */ +final class ReferenceBertPipeline { + + private static final int MAX_WORD_CHARACTERS = 100; + + private final WordpieceTokenizer wordpieceTokenizer; + private final boolean lowerCase; + + ReferenceBertPipeline(Set vocabulary, boolean lowerCase) { + this.wordpieceTokenizer = new WordpieceTokenizer(vocabulary, + WordpieceTokenizer.BERT_CLS_TOKEN, WordpieceTokenizer.BERT_SEP_TOKEN, + WordpieceTokenizer.BERT_UNK_TOKEN, MAX_WORD_CHARACTERS); + this.lowerCase = lowerCase; + } + + String[] tokenize(String text) { + return wordpieceTokenizer.tokenize(normalize(text)); + } + + private String normalize(String text) { + String normalized = cleanText(text); + normalized = isolateCjkCharacters(normalized); + if (lowerCase) { + normalized = stripAccents(normalized.toLowerCase(Locale.ROOT)); + } + return BertNormalization.isolatePunctuation(normalized); + } + + private static String cleanText(String text) { + final StringBuilder cleaned = new StringBuilder(text.length()); + text.codePoints().forEach(codePoint -> { + if (codePoint == 0 || codePoint == 0xFFFD || BertNormalization.isControl(codePoint)) { + return; + } + if (BertNormalization.isWhitespace(codePoint)) { + cleaned.append(' '); + } else { + cleaned.appendCodePoint(codePoint); + } + }); + return cleaned.toString(); + } + + private static String isolateCjkCharacters(String text) { + final StringBuilder spaced = new StringBuilder(text.length()); + text.codePoints().forEach(codePoint -> { + if (BertNormalization.isCjk(codePoint)) { + spaced.append(' ').appendCodePoint(codePoint).append(' '); + } else { + spaced.appendCodePoint(codePoint); + } + }); + return spaced.toString(); + } + + private static String stripAccents(String text) { + final String decomposed = Normalizer.normalize(text, Normalizer.Form.NFD); + final StringBuilder stripped = new StringBuilder(decomposed.length()); + decomposed.codePoints().forEach(codePoint -> { + if (Character.getType(codePoint) != Character.NON_SPACING_MARK) { + stripped.appendCodePoint(codePoint); + } + }); + return stripped.toString(); + } +} diff --git a/opennlp-core/opennlp-runtime/src/test/java/opennlp/tools/tokenize/BertTokenizerTest.java b/opennlp-core/opennlp-runtime/src/test/java/opennlp/tools/tokenize/WordpieceEncoderReferenceSequencesTest.java similarity index 57% rename from opennlp-core/opennlp-runtime/src/test/java/opennlp/tools/tokenize/BertTokenizerTest.java rename to opennlp-core/opennlp-runtime/src/test/java/opennlp/tools/tokenize/WordpieceEncoderReferenceSequencesTest.java index d8f706f4ef..68e8f219f7 100644 --- a/opennlp-core/opennlp-runtime/src/test/java/opennlp/tools/tokenize/BertTokenizerTest.java +++ b/opennlp-core/opennlp-runtime/src/test/java/opennlp/tools/tokenize/WordpieceEncoderReferenceSequencesTest.java @@ -14,36 +14,40 @@ * See the License for the specific language governing permissions and * limitations under the License. */ - package opennlp.tools.tokenize; -import java.util.Set; +import java.util.List; import org.junit.jupiter.api.Assertions; import org.junit.jupiter.api.Test; /** - * Tests {@link BertTokenizer}. + * The reference token sequences of the removed full-pipeline {@code Tokenizer}, re-asserted + * against {@link WordpieceEncoder}. *

* All expected token sequences in this test were generated with the HuggingFace * {@code tokenizers} reference implementation ({@code BertWordPieceTokenizer}) * using the same vocabulary, so they are verified to be identical to the - * reference BERT tokenization. + * reference BERT tokenization. The encoder requires its special tokens to be + * present in the vocabulary (every piece must have an id), so the vocabularies + * here include them; the token sequences are unchanged. */ -public class BertTokenizerTest { +public class WordpieceEncoderReferenceSequencesTest { - private static final Set VOCABULARY = Set.of( + private static final List VOCABULARY = List.of( + "[CLS]", "[SEP]", "[UNK]", "the", "quick", "brown", "fox", "jumps", "over", "lazy", "dog", "em", "##bed", "##ding", "##s", "wurttemberg", "strasse", "grosse", "don", "t", "wait", "what", ".", ",", "?", "!", "'", - "\u6211", "\u7231", // CJK: 我 爱 + "\u6211", "\u7231", // CJK "natural", "language", "processing"); @Test void testLowerCasesCapitalizedWords() { - final Tokenizer tokenizer = new BertTokenizer(VOCABULARY); - final String[] tokens = tokenizer.tokenize("The quick brown fox jumps over the lazy dog."); + final WordpieceEncoder encoder = new WordpieceEncoder(VOCABULARY); + final String[] tokens = + encoder.encodeToPieces("The quick brown fox jumps over the lazy dog."); final String[] expected = {"[CLS]", "the", "quick", "brown", "fox", "jumps", "over", "the", "lazy", "dog", ".", "[SEP]"}; @@ -52,8 +56,8 @@ void testLowerCasesCapitalizedWords() { @Test void testLowerCasesBeforeWordpieceSplitting() { - final Tokenizer tokenizer = new BertTokenizer(VOCABULARY); - final String[] tokens = tokenizer.tokenize("Embeddings"); + final WordpieceEncoder encoder = new WordpieceEncoder(VOCABULARY); + final String[] tokens = encoder.encodeToPieces("Embeddings"); final String[] expected = {"[CLS]", "em", "##bed", "##ding", "##s", "[SEP]"}; Assertions.assertArrayEquals(expected, tokens); @@ -61,10 +65,10 @@ void testLowerCasesBeforeWordpieceSplitting() { @Test void testStripsAccentsButKeepsNonCombiningCharacters() { - final Tokenizer tokenizer = new BertTokenizer(VOCABULARY); - // ü decomposes to u + combining diaeresis and the mark is stripped; - // ß is not a combining mark and must survive, leaving an OOV token. - final String[] tokens = tokenizer.tokenize("W\u00fcrttemberg Stra\u00dfe"); + final WordpieceEncoder encoder = new WordpieceEncoder(VOCABULARY); + // The u-umlaut decomposes to u plus a combining diaeresis and the mark is stripped; + // the sharp s is not a combining mark and must survive, leaving an OOV token. + final String[] tokens = encoder.encodeToPieces("W\u00fcrttemberg Stra\u00dfe"); final String[] expected = {"[CLS]", "wurttemberg", "[UNK]", "[SEP]"}; Assertions.assertArrayEquals(expected, tokens); @@ -72,8 +76,8 @@ void testStripsAccentsButKeepsNonCombiningCharacters() { @Test void testSplitsPunctuationRunsIntoSingleCharacters() { - final Tokenizer tokenizer = new BertTokenizer(VOCABULARY); - final String[] tokens = tokenizer.tokenize("Wait... what?!"); + final WordpieceEncoder encoder = new WordpieceEncoder(VOCABULARY); + final String[] tokens = encoder.encodeToPieces("Wait... what?!"); final String[] expected = {"[CLS]", "wait", ".", ".", ".", "what", "?", "!", "[SEP]"}; Assertions.assertArrayEquals(expected, tokens); @@ -81,8 +85,8 @@ void testSplitsPunctuationRunsIntoSingleCharacters() { @Test void testSplitsApostrophesAsPunctuation() { - final Tokenizer tokenizer = new BertTokenizer(VOCABULARY); - final String[] tokens = tokenizer.tokenize("don't"); + final WordpieceEncoder encoder = new WordpieceEncoder(VOCABULARY); + final String[] tokens = encoder.encodeToPieces("don't"); final String[] expected = {"[CLS]", "don", "'", "t", "[SEP]"}; Assertions.assertArrayEquals(expected, tokens); @@ -90,8 +94,8 @@ void testSplitsApostrophesAsPunctuation() { @Test void testIsolatesCjkIdeographs() { - final Tokenizer tokenizer = new BertTokenizer(VOCABULARY); - final String[] tokens = tokenizer.tokenize("\u6211\u7231natural language processing"); + final WordpieceEncoder encoder = new WordpieceEncoder(VOCABULARY); + final String[] tokens = encoder.encodeToPieces("\u6211\u7231natural language processing"); final String[] expected = {"[CLS]", "\u6211", "\u7231", "natural", "language", "processing", "[SEP]"}; @@ -100,10 +104,10 @@ void testIsolatesCjkIdeographs() { @Test void testCleansControlCharactersAndNormalizesWhitespace() { - final Tokenizer tokenizer = new BertTokenizer(VOCABULARY); + final WordpieceEncoder encoder = new WordpieceEncoder(VOCABULARY); // Tab and no-break space are whitespace; the NUL character is removed, // joining "brown" and "fox" into one out-of-vocabulary token. - final String[] tokens = tokenizer.tokenize("the\tquick\u00a0brown\u0000fox"); + final String[] tokens = encoder.encodeToPieces("the\tquick\u00a0brown\u0000fox"); final String[] expected = {"[CLS]", "the", "quick", "[UNK]", "[SEP]"}; Assertions.assertArrayEquals(expected, tokens); @@ -111,11 +115,11 @@ void testCleansControlCharactersAndNormalizesWhitespace() { @Test void testRemovesPrivateUseAndUnassignedCharacters() { - final Tokenizer tokenizer = new BertTokenizer(VOCABULARY); + final WordpieceEncoder encoder = new WordpieceEncoder(VOCABULARY); // The reference implementation treats all C* categories as control // characters: private use (U+E000, Co) and noncharacters (U+FDD0, Cn) // are removed, joining the surrounding text into one OOV token. - final String[] tokens = tokenizer.tokenize("fox\ue000jumps and fox\ufdd0jumps"); + final String[] tokens = encoder.encodeToPieces("fox\ue000jumps and fox\ufdd0jumps"); final String[] expected = {"[CLS]", "[UNK]", "[UNK]", "[UNK]", "[SEP]"}; Assertions.assertArrayEquals(expected, tokens); @@ -123,19 +127,21 @@ void testRemovesPrivateUseAndUnassignedCharacters() { @Test void testRejectsNullSpecialTokens() { - Assertions.assertThrows(NullPointerException.class, - () -> new BertTokenizer(VOCABULARY, true, null, "[SEP]", "[UNK]")); - Assertions.assertThrows(NullPointerException.class, - () -> new BertTokenizer(VOCABULARY, true, "[CLS]", null, "[UNK]")); - Assertions.assertThrows(NullPointerException.class, - () -> new BertTokenizer(VOCABULARY, true, "[CLS]", "[SEP]", null)); + // The encoder's contract throws IllegalArgumentException where the removed class threw + // NullPointerException. + Assertions.assertThrows(IllegalArgumentException.class, + () -> new WordpieceEncoder(VOCABULARY, true, null, "[SEP]", "[UNK]")); + Assertions.assertThrows(IllegalArgumentException.class, + () -> new WordpieceEncoder(VOCABULARY, true, "[CLS]", null, "[UNK]")); + Assertions.assertThrows(IllegalArgumentException.class, + () -> new WordpieceEncoder(VOCABULARY, true, "[CLS]", "[SEP]", null)); } @Test void testCasedModeKeepsCaseAndAccents() { - final Tokenizer tokenizer = new BertTokenizer( - Set.of("The", "W\u00fcrttemberg", "fox"), false); - final String[] tokens = tokenizer.tokenize("The W\u00fcrttemberg fox"); + final WordpieceEncoder encoder = new WordpieceEncoder( + List.of("[CLS]", "[SEP]", "[UNK]", "The", "W\u00fcrttemberg", "fox"), false); + final String[] tokens = encoder.encodeToPieces("The W\u00fcrttemberg fox"); final String[] expected = {"[CLS]", "The", "W\u00fcrttemberg", "fox", "[SEP]"}; Assertions.assertArrayEquals(expected, tokens); @@ -143,20 +149,13 @@ void testCasedModeKeepsCaseAndAccents() { @Test void testCustomSpecialTokens() { - final Tokenizer tokenizer = new BertTokenizer(Set.of("the", "fox"), true, + final WordpieceEncoder encoder = new WordpieceEncoder( + List.of("", "", "", "the", "fox"), true, WordpieceTokenizer.ROBERTA_CLS_TOKEN, WordpieceTokenizer.ROBERTA_SEP_TOKEN, WordpieceTokenizer.ROBERTA_UNK_TOKEN); - final String[] tokens = tokenizer.tokenize("The unknown fox"); + final String[] tokens = encoder.encodeToPieces("The unknown fox"); final String[] expected = {"", "the", "", "fox", ""}; Assertions.assertArrayEquals(expected, tokens); } - - @Test - void testTokenizePosIsUnsupported() { - final Tokenizer tokenizer = new BertTokenizer(VOCABULARY); - Assertions.assertThrows(UnsupportedOperationException.class, - () -> tokenizer.tokenizePos("the fox")); - } - } diff --git a/opennlp-core/opennlp-runtime/src/test/java/opennlp/tools/tokenize/WordpieceEncoderTest.java b/opennlp-core/opennlp-runtime/src/test/java/opennlp/tools/tokenize/WordpieceEncoderTest.java new file mode 100644 index 0000000000..610bba5ed5 --- /dev/null +++ b/opennlp-core/opennlp-runtime/src/test/java/opennlp/tools/tokenize/WordpieceEncoderTest.java @@ -0,0 +1,218 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://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. + */ +package opennlp.tools.tokenize; + +import java.util.ArrayList; +import java.util.HashSet; +import java.util.List; +import java.util.Random; + +import org.junit.jupiter.api.Test; + +import static org.junit.jupiter.api.Assertions.assertArrayEquals; +import static org.junit.jupiter.api.Assertions.assertEquals; +import static org.junit.jupiter.api.Assertions.assertThrows; +import static org.junit.jupiter.api.Assertions.assertTrue; + +/** + * The encoder held against {@link ReferenceBertPipeline} for piece-sequence parity (the encoder's + * contract is "the same pipeline, plus ids and spans"), plus exact hand-computed span + * assertions through every normalization step that changes, inserts, or removes characters. + */ +class WordpieceEncoderTest { + + // Ids are indices: [PAD]=0, [UNK]=1, [CLS]=2, [SEP]=3, hello=4, world=5, ##s=6, won=7, + // ##der=8, ##ful=9, ca=10, ##fe=11, istanbul=12, U+4E2D=13, U+56FD=14, .=15, ,=16, !=17, + // he=18, ##llo=19, Greek "sofos" with a final sigma=20. + private static final List VOCAB = List.of( + "[PAD]", "[UNK]", "[CLS]", "[SEP]", "hello", "world", "##s", "won", "##der", "##ful", + "ca", "##fe", "istanbul", "\u4E2D", "\u56FD", ".", ",", "!", "he", "##llo", + "\u03C3\u03BF\u03C6\u03BF\u03C2"); + + private static WordpieceEncoder uncased() { + return new WordpieceEncoder(VOCAB); + } + + private static void assertPiece(SubwordPiece piece, String expectedPiece, int expectedId, + int expectedStart, int expectedEnd) { + assertEquals(expectedPiece, piece.piece()); + assertEquals(expectedId, piece.id()); + assertEquals(expectedStart, piece.start(), "start of " + piece); + assertEquals(expectedEnd, piece.end(), "end of " + piece); + } + + @Test + void testPieceSequenceMatchesTheReferencePipelineOnCuratedInputs() { + final ReferenceBertPipeline reference = new ReferenceBertPipeline(new HashSet<>(VOCAB), true); + final WordpieceEncoder encoder = uncased(); + final String[] inputs = { + "", + " ", + "Hello, WORLD!", + "Wonderful", + "hellos", + // An accented e, stripped by NFD decomposition. + "Caf\u00E9", + // The Turkish dotted capital I: lower cases to two chars, then the dot strips away. + "\u0130stanbul", + // CJK ideographs are isolated into single-character tokens. + "\u4E2D\u56FD is CJK", + // Greek upper case: the trailing sigma takes the contextual final-sigma mapping. + "\u03A3\u039F\u03A6\u039F\u03A3", + // The NBSP is whitespace in the BERT sense. + "hello\u00A0world", + // NUL and the zero-width space are removed by the cleaning stage. + "a\u0000b\u200Bc", + // An emoji: unknown to the vocabulary, and a surrogate pair. + "\uD83D\uDE00", + "!!!", + "a".repeat(101), + "he said: \u00ABhello\u00BB.", + }; + for (final String input : inputs) { + assertArrayEquals(reference.tokenize(input), encoder.encodeToPieces(input), + "parity broke on: " + input); + } + } + + @Test + void testPieceSequenceMatchesTheReferencePipelineOnRandomInputs() { + final int[] pool = {'a', 'b', 'A', 'B', 'z', ' ', ' ', '\t', 0x00A0, '.', '!', ',', + 0x0301, 0x00E9, 0x0130, 0x03A3, 0x03C3, 0x03BF, 0x4E2D, 0xFFFD, 0x200B, 0x1F600, 0}; + final Random random = new Random(42); + for (final boolean lowerCase : new boolean[] {true, false}) { + final ReferenceBertPipeline reference = + new ReferenceBertPipeline(new HashSet<>(VOCAB), lowerCase); + final WordpieceEncoder encoder = new WordpieceEncoder(VOCAB, lowerCase); + for (int round = 0; round < 400; round++) { + final StringBuilder text = new StringBuilder(); + final int length = random.nextInt(25); + for (int i = 0; i < length; i++) { + text.appendCodePoint(pool[random.nextInt(pool.length)]); + } + final String input = text.toString(); + assertArrayEquals(reference.tokenize(input), encoder.encodeToPieces(input), + "parity broke on: " + input); + + // Span invariants: within bounds and never moving backwards. + int previousStart = 0; + for (final SubwordPiece piece : encoder.encode(input)) { + assertTrue(piece.start() >= previousStart && piece.end() <= input.length(), + "span out of order or bounds in " + input + ": " + piece); + previousStart = piece.start(); + } + } + } + } + + @Test + void testSpansSurvivePunctuationIsolationAndCaseFolding() { + final List pieces = uncased().encode("Hello, WORLD!"); + assertEquals(6, pieces.size()); + assertPiece(pieces.get(0), "[CLS]", 2, 0, 0); + assertPiece(pieces.get(1), "hello", 4, 0, 5); + assertPiece(pieces.get(2), ",", 16, 5, 6); + assertPiece(pieces.get(3), "world", 5, 7, 12); + assertPiece(pieces.get(4), "!", 17, 12, 13); + assertPiece(pieces.get(5), "[SEP]", 3, 13, 13); + } + + @Test + void testSpansSurviveAccentStripping() { + // The accent is stripped by NFD, yet ##fe still covers the accented surface. + final List pieces = uncased().encode("Caf\u00E9"); + assertEquals(4, pieces.size()); + assertPiece(pieces.get(1), "ca", 10, 0, 2); + assertPiece(pieces.get(2), "##fe", 11, 2, 4); + } + + @Test + void testSpansSurviveLengthChangingLowerCasing() { + // The Turkish dotted capital I lower cases to two chars before the combining dot strips; + // the piece still covers the original eight chars. + final List pieces = uncased().encode("\u0130stanbul"); + assertEquals(3, pieces.size()); + assertPiece(pieces.get(1), "istanbul", 12, 0, 8); + } + + @Test + void testCjkIsolationYieldsOnePieceAndSpanPerIdeograph() { + final List pieces = uncased().encode("\u4E2D\u56FD"); + assertEquals(4, pieces.size()); + assertPiece(pieces.get(1), "\u4E2D", 13, 0, 1); + assertPiece(pieces.get(2), "\u56FD", 14, 1, 2); + } + + @Test + void testUnknownWordCoversItsWholeSurfaceIncludingRemovedChars() { + // NUL and the zero-width space are removed by cleaning, so one word "abc" remains; it is + // not representable and becomes the unknown piece spanning the full original surface. + final List pieces = uncased().encode("a\u0000b\u200Bc"); + assertEquals(3, pieces.size()); + assertPiece(pieces.get(1), "[UNK]", 1, 0, 5); + } + + @Test + void testContextualCaseMappingFallsBackToWordWideSpans() { + // Greek final sigma is a contextual mapping the per-char rerun cannot reproduce, so the + // word's pieces fall back to spanning the whole word; content parity is asserted in the + // differential tests above. + final List pieces = + uncased().encode("\u03A3\u039F\u03A6\u039F\u03A3"); + assertEquals(3, pieces.size()); + assertPiece(pieces.get(1), + "\u03C3\u03BF\u03C6\u03BF\u03C2", 20, 0, 5); + } + + @Test + void testEncodeToIdsCarriesVocabularyLineNumbers() { + assertArrayEquals(new int[] {2, 4, 5, 6, 3}, uncased().encodeToIds("Hello worldS")); + } + + @Test + void testCasedEncoderKeepsCase() { + final List vocabulary = new ArrayList<>(VOCAB); + vocabulary.add("Hello"); + final WordpieceEncoder cased = new WordpieceEncoder(vocabulary, false); + final List pieces = cased.encode("Hello hello"); + assertPiece(pieces.get(1), "Hello", vocabulary.size() - 1, 0, 5); + assertPiece(pieces.get(2), "hello", 4, 6, 11); + } + + @Test + void testEmptyAndBlankTextEncodeToTheFramePiecesOnly() { + for (final String input : new String[] {"", " "}) { + final List pieces = uncased().encode(input); + assertEquals(2, pieces.size()); + assertPiece(pieces.get(0), "[CLS]", 2, 0, 0); + assertPiece(pieces.get(1), "[SEP]", 3, input.length(), input.length()); + } + } + + @Test + void testValidationFailsLoudly() { + assertThrows(IllegalArgumentException.class, () -> new WordpieceEncoder(null)); + assertThrows(IllegalArgumentException.class, + () -> new WordpieceEncoder(List.of("[CLS]", "[SEP]"))); + assertThrows(IllegalArgumentException.class, + () -> new WordpieceEncoder(List.of("[CLS]", "[SEP]", "[UNK]", "dup", "dup"))); + final List withNull = new ArrayList<>(VOCAB); + withNull.add(null); + assertThrows(IllegalArgumentException.class, () -> new WordpieceEncoder(withNull)); + assertThrows(IllegalArgumentException.class, () -> uncased().encode(null)); + } +} From d961a65095caffdbf28d4f80ae24774406c6d460 Mon Sep 17 00:00:00 2001 From: Kristian Rickert Date: Sun, 12 Jul 2026 15:15:22 -0400 Subject: [PATCH 5/9] OPENNLP-1885: Document the hand-rolled protobuf reader rationale and refactor trigger --- .../opennlp/subword/sentencepiece/ModelProtoReader.java | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/ModelProtoReader.java b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/ModelProtoReader.java index 4e75f49c97..58f9dd83e1 100644 --- a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/ModelProtoReader.java +++ b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/ModelProtoReader.java @@ -28,6 +28,14 @@ * directly and keeps only the fields inference needs: the pieces with scores and types, the * normalizer spec, the handful of trainer-spec fields that change runtime behavior, and the * embedded self-test samples. Unknown fields are skipped, malformed input fails loudly.

+ * + *

This is a self-contained wire reader, not a performance optimization: model loading happens + * once and is not on any hot path. It exists so this module reads a protobuf-encoded file without + * adding a {@code protobuf-java} runtime dependency, which the project does not otherwise use, for + * a load-time parse of a schema that is stable in practice. If OpenNLP ever takes on a real + * protobuf dependency for other reasons, this hand-rolled reader should be retired in favor of it: + * generate from {@code sentencepiece_model.proto} (or parse via the descriptor API) and delete + * this class, since the dependency-avoidance rationale no longer holds.

*/ final class ModelProtoReader { From 029fa6c5945ff9eb341ab3b5adaccdc1d99aa150 Mon Sep 17 00:00:00 2001 From: Kristian Rickert Date: Sun, 12 Jul 2026 15:29:54 -0400 Subject: [PATCH 6/9] OPENNLP-1885: Trim commentary and tighten javadoc per review conventions --- .../tools/tokenize/WordpieceEncoder.java | 50 +++++++++++++------ .../src/main/java/opennlp/dl/AbstractDL.java | 10 ++++ .../java/opennlp/dl/EncoderTokenizer.java | 6 +++ .../subword/sentencepiece/BpeEncoder.java | 28 +++++++---- .../subword/sentencepiece/ByteBuilder.java | 30 +++++++++++ .../sentencepiece/DoubleArrayTrie.java | 7 ++- .../subword/sentencepiece/IntBuilder.java | 24 +++++++++ .../SentencePieceNormalizer.java | 48 ++++++++++++------ .../sentencepiece/SentencePieceTokenizer.java | 3 +- .../subword/sentencepiece/UnigramEncoder.java | 9 +--- .../subword/sentencepiece/Utf8Text.java | 9 ++-- 11 files changed, 168 insertions(+), 56 deletions(-) diff --git a/opennlp-api/src/main/java/opennlp/tools/tokenize/WordpieceEncoder.java b/opennlp-api/src/main/java/opennlp/tools/tokenize/WordpieceEncoder.java index e74c23205d..65c73dab0c 100644 --- a/opennlp-api/src/main/java/opennlp/tools/tokenize/WordpieceEncoder.java +++ b/opennlp-api/src/main/java/opennlp/tools/tokenize/WordpieceEncoder.java @@ -206,9 +206,15 @@ public List encode(CharSequence text) { return pieces; } - // Greedy longest-match wordpiece over one whitespace-delimited word, exactly as - // WordpieceTokenizer#tokenize segments it; pieces are emitted only if the whole word is - // representable, otherwise the word becomes a single unknown piece. + /** + * Greedily longest-match segments one whitespace-delimited word; the pieces are emitted only if + * the whole word is representable, otherwise the word becomes a single unknown piece. + * + * @param mapped The normalized text with per-character original-text ranges. + * @param from The inclusive start of the word in {@code mapped}. + * @param to The exclusive end of the word in {@code mapped}. + * @param pieces The output list to append to. + */ private void encodeWord(MappedText mapped, int from, int to, List pieces) { final int wordStart = mapped.starts[from]; final int wordEnd = mapped.ends[to - 1]; @@ -247,8 +253,10 @@ private void encodeWord(MappedText mapped, int from, int to, List } } - // The normalized text with, for every char, the original-text range it came from. Characters - // inserted by the pipeline (isolation spaces) carry an empty range at the insertion point. + /** + * The normalized text with, for every char, the original-text range it came from. Characters + * inserted by the pipeline (isolation spaces) carry an empty range at the insertion point. + */ private static final class MappedText { private char[] chars; private int[] starts; @@ -281,8 +289,13 @@ private void add(String s, int originalStart, int originalEnd) { } } - // Text cleaning and CJK isolation in one pass: both are per-code-point - // transforms, and a CJK code point is never dropped or whitespace, so the fusion is exact. + /** + * Cleans the text (control and whitespace normalization) and isolates CJK code points in one + * pass, recording the original-text range of every output character. + * + * @param original The original input text. + * @return The cleaned, CJK-isolated text with per-character ranges. + */ private static MappedText cleanAndIsolateCjk(String original) { final MappedText out = new MappedText(original.length() + 16); int i = 0; @@ -311,7 +324,13 @@ private static MappedText cleanAndIsolateCjk(String original) { return out; } - // BertNormalization#isolatePunctuation with the range of each char preserved. + /** + * Isolates punctuation, surrounding each punctuation code point with spaces, preserving the + * original-text range of every character. + * + * @param in The input text with per-character ranges. + * @return The punctuation-isolated text with per-character ranges. + */ private static MappedText isolatePunctuation(MappedText in) { final MappedText out = new MappedText(in.length + 16); int i = 0; @@ -334,13 +353,14 @@ private static MappedText isolatePunctuation(MappedText in) { return out; } - // Lower casing and accent stripping with ranges preserved. The content is computed - // with whole-run library calls, applied per whitespace run - // (equivalent on whole strings: no case mapping context crosses a space, and NFD - // composition is boundary-safe at a space). The per-char ranges are reconstructed from a - // per-code-point rerun of the same transforms; if a contextual case mapping (Greek final - // sigma) makes the rerun disagree with the authoritative content, every char of that run - // falls back to the run's full range, which widens spans but never misplaces them. + /** + * Lower cases and strips accents, preserving the original-text range of every character. When a + * contextual case mapping prevents a per-character range from being recovered, the whole + * whitespace run falls back to its full range, which widens spans but never misplaces them. + * + * @param in The input text with per-character ranges. + * @return The lower-cased, accent-stripped text with per-character ranges. + */ private static MappedText lowerCaseAndStripAccents(MappedText in) { final MappedText out = new MappedText(in.length + 16); int from = 0; diff --git a/opennlp-core/opennlp-ml/opennlp-dl/src/main/java/opennlp/dl/AbstractDL.java b/opennlp-core/opennlp-ml/opennlp-dl/src/main/java/opennlp/dl/AbstractDL.java index 2f5b4a936c..4ac5c4f147 100644 --- a/opennlp-core/opennlp-ml/opennlp-dl/src/main/java/opennlp/dl/AbstractDL.java +++ b/opennlp-core/opennlp-ml/opennlp-dl/src/main/java/opennlp/dl/AbstractDL.java @@ -257,6 +257,16 @@ protected Tokenizer createTokenizer( return createPipelineTokenizer(vocab, lowerCase); } + /** + * Builds the pipeline tokenizer, selecting the RoBERTa special tokens when the vocabulary + * carries them and the BERT defaults otherwise. + * + * @param vocab The vocabulary map. + * @param lowerCase {@code true} for uncased models, {@code false} for cased models. + * @return A configured {@link Tokenizer}. + * @throws IllegalArgumentException Thrown if the selected special tokens are not all present in + * the vocabulary. + */ static Tokenizer createPipelineTokenizer( final Map vocab, final boolean lowerCase) { if (vocab.containsKey( diff --git a/opennlp-core/opennlp-ml/opennlp-dl/src/main/java/opennlp/dl/EncoderTokenizer.java b/opennlp-core/opennlp-ml/opennlp-dl/src/main/java/opennlp/dl/EncoderTokenizer.java index 43943b010f..5422c6773d 100644 --- a/opennlp-core/opennlp-ml/opennlp-dl/src/main/java/opennlp/dl/EncoderTokenizer.java +++ b/opennlp-core/opennlp-ml/opennlp-dl/src/main/java/opennlp/dl/EncoderTokenizer.java @@ -29,10 +29,16 @@ final class EncoderTokenizer implements Tokenizer { private final WordpieceEncoder encoder; + /** + * Instantiates the adapter. + * + * @param encoder The encoder whose pieces this tokenizer returns. + */ EncoderTokenizer(final WordpieceEncoder encoder) { this.encoder = encoder; } + /** {@inheritDoc} */ @Override public String[] tokenize(final String text) { return encoder.encodeToPieces(text); diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/BpeEncoder.java b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/BpeEncoder.java index e7430032ae..c6ad75542c 100644 --- a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/BpeEncoder.java +++ b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/BpeEncoder.java @@ -28,11 +28,8 @@ * user-defined symbols, which are frozen whole) and adjacent pairs merge greedily, highest piece * score first, until no adjacent pair forms a vocabulary piece. * - *

This ports the reference implementation's agenda algorithm: candidate pairs sit in a - * priority queue ordered by score with ties broken towards the leftmost pair, stale entries are - * detected by a length check when popped, and merges that land on a piece marked unused are - * re-segmented back into their constituents afterwards. Only pieces of the normal, user-defined, - * and unused types participate in merges.

+ *

Only pieces of the normal, user-defined, and unused types participate in merges; a merge that + * lands on an unused piece is re-segmented back into its constituents.

*/ final class BpeEncoder { @@ -67,8 +64,10 @@ final class BpeEncoder { this.userDefinedMatcher = userDefinedMatcher; } - // A candidate merge of the symbols at indices left and right; size is the merged byte length - // used to detect staleness after either side has changed. + /** + * A candidate merge of the symbols at indices {@code left} and {@code right}; {@code size} is the + * merged byte length, used to detect staleness after either side has changed. + */ private record Pair(int left, int right, float score, int size) { } @@ -179,9 +178,18 @@ private void maybeAddPair(byte[] normalized, int[] from, int[] to, boolean[] fre } } - // Emits a symbol, splitting a piece of the unused type back into the pieces it was merged - // from. Positions are assigned by a running cursor; constituent byte lengths always sum to the - // merged length, so the cursor stays aligned with the normalized bytes. + /** + * Emits a symbol, splitting a piece of the unused type back into the pieces it was merged from. + * Positions are assigned by a running cursor; constituent byte lengths always sum to the merged + * length, so the cursor stays aligned with the normalized bytes. + * + * @param piece The piece content to emit. + * @param consumed The running byte cursor into the normalized text. + * @param depth The current recursion depth. + * @param revMerge The map from a merged piece to its two constituents. + * @param output The segment list to append to. + * @return The updated byte cursor. + */ private int resegment(String piece, int consumed, int depth, Map revMerge, List output) { final Integer mapped = pieces.get(piece); diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/ByteBuilder.java b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/ByteBuilder.java index 1d2c3ab952..95a02fddd5 100644 --- a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/ByteBuilder.java +++ b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/ByteBuilder.java @@ -24,10 +24,20 @@ final class ByteBuilder { private byte[] data; private int length; + /** + * Instantiates the buffer. + * + * @param capacity The initial capacity hint. + */ ByteBuilder(int capacity) { data = new byte[Math.max(capacity, 16)]; } + /** + * Appends one byte. + * + * @param b The byte to append. + */ void append(byte b) { if (length == data.length) { data = Arrays.copyOf(data, data.length + (data.length >> 1)); @@ -35,6 +45,13 @@ void append(byte b) { data[length++] = b; } + /** + * Appends a run of bytes. + * + * @param source The source array. + * @param from The inclusive start offset in {@code source}. + * @param count The number of bytes to append. + */ void append(byte[] source, int from, int count) { while (length + count > data.length) { data = Arrays.copyOf(data, data.length + (data.length >> 1)); @@ -43,14 +60,26 @@ void append(byte[] source, int from, int count) { length += count; } + /** {@return the number of valid bytes} */ int length() { return length; } + /** + * Shrinks the valid length. + * + * @param newLength The new length, not greater than the current length. + */ void truncate(int newLength) { length = newLength; } + /** + * Tests whether the valid bytes end with the given suffix. + * + * @param suffix The suffix to test. + * @return {@code true} when the buffer ends with {@code suffix}. + */ boolean endsWith(byte[] suffix) { if (length < suffix.length) { return false; @@ -58,6 +87,7 @@ boolean endsWith(byte[] suffix) { return Arrays.equals(data, length - suffix.length, length, suffix, 0, suffix.length); } + /** {@return a trimmed copy of the valid bytes} */ byte[] toArray() { return Arrays.copyOf(data, length); } diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/DoubleArrayTrie.java b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/DoubleArrayTrie.java index c88fbe161b..967b525fe5 100644 --- a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/DoubleArrayTrie.java +++ b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/DoubleArrayTrie.java @@ -104,7 +104,12 @@ boolean hasTransitionFromRoot(int b) { return (units[nodePos] & 0x800000FF) == b; } - // The offset from a unit to its children, as encoded by Darts-clone. + /** + * Returns the offset from a unit to its children, as encoded by Darts-clone. + * + * @param unit The unit word. + * @return The child offset. + */ private static int offset(int unit) { return (unit >>> 10) << ((unit & (1 << 9)) >>> 6); } diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/IntBuilder.java b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/IntBuilder.java index ec585685c1..25dad2ec92 100644 --- a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/IntBuilder.java +++ b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/IntBuilder.java @@ -24,10 +24,20 @@ final class IntBuilder { private int[] data; private int length; + /** + * Instantiates the buffer. + * + * @param capacity The initial capacity hint. + */ IntBuilder(int capacity) { data = new int[Math.max(capacity, 16)]; } + /** + * Appends one value. + * + * @param value The value to append. + */ void append(int value) { if (length == data.length) { data = Arrays.copyOf(data, data.length + (data.length >> 1)); @@ -35,6 +45,13 @@ void append(int value) { data[length++] = value; } + /** + * Reads a value by index. + * + * @param index An index in {@code [0, length())}. + * @return The value at {@code index}. + * @throws IndexOutOfBoundsException Thrown if {@code index} is out of range. + */ int get(int index) { if (index >= length) { throw new IndexOutOfBoundsException("index " + index + " is outside [0, " + length + ")"); @@ -42,14 +59,21 @@ int get(int index) { return data[index]; } + /** {@return the number of valid values} */ int length() { return length; } + /** + * Shrinks the valid length. + * + * @param newLength The new length, not greater than the current length. + */ void truncate(int newLength) { length = newLength; } + /** {@return a trimmed copy of the valid values} */ int[] toArray() { return Arrays.copyOf(data, length); } diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceNormalizer.java b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceNormalizer.java index d99d4b33ce..8c6ed5a4d7 100644 --- a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceNormalizer.java +++ b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceNormalizer.java @@ -25,9 +25,6 @@ * {@code normToOrig}, mapping every normalized byte to the offset of the original byte chunk it * was derived from, with one trailing entry for the end position; that map is what lets every * downstream piece report an exact span of the caller's text.

- * - *

This mirrors the reference implementation's normalizer semantics rule for rule, since parity - * of both the normalized bytes and the offset map is what the tests assert.

*/ final class SentencePieceNormalizer { @@ -118,10 +115,11 @@ final class SentencePieceNormalizer { record Normalized(byte[] bytes, int length, int[] normToOrig) { } - // One normalization step: `consumed` input bytes produced `data[from, to)`. The data array is - // the input itself (pass-through), the replacement blob, or the replacement character. One - // mutable scratch per normalize call, refilled per chunk, so the scan allocates nothing per - // code point. + /** + * One normalization step: {@code consumed} input bytes produced {@code data[from, to)}. The data + * array is the input itself (pass-through), the replacement blob, or the replacement character. + * A single mutable scratch is refilled per chunk so the scan allocates nothing per code point. + */ private static final class Chunk { private byte[] data; @@ -249,10 +247,17 @@ private static void appendSpace(ByteBuilder normalized, IntBuilder normToOrig, } } - // Fills the scratch with the normalized form of the longest applicable prefix of - // input[from, inputLength): a user-defined symbol passes through raw, otherwise the longest - // character-map rule applies, otherwise one code point passes through raw (or becomes U+FFFD - // when the lead byte is malformed). + /** + * Fills the scratch with the normalized form of the longest applicable prefix of + * {@code input[from, inputLength)}: a user-defined symbol passes through raw, otherwise the + * longest character-map rule applies, otherwise one code point passes through raw (or becomes + * U+FFFD when the lead byte is malformed). + * + * @param input The UTF-8 input buffer. + * @param inputLength The number of valid bytes in {@code input}. + * @param from The offset to normalize from. + * @param chunk The scratch to fill. + */ private void normalizePrefix(byte[] input, int inputLength, int from, Chunk chunk) { if (userDefinedMatcher != null) { final int matched = longestUserDefinedMatch(input, inputLength, from); @@ -314,8 +319,13 @@ private int longestUserDefinedMatch(byte[] input, int inputLength, int from) { return longest; } - // The byte length of a UTF-8 sequence by its lead byte, as the reference implementation - // computes it: trail and malformed lead bytes report one byte. + /** + * Returns the byte length of a UTF-8 sequence from its lead byte; trail and malformed lead bytes + * report one byte. + * + * @param lead The lead byte. + * @return The sequence length in bytes, from one to four. + */ static int utf8Length(byte lead) { final int high = (lead & 0xFF) >>> 4; if (high < 0xC) { @@ -328,9 +338,15 @@ static int utf8Length(byte lead) { }; } - // Checks a single code point for well-formedness: correct trail-byte count and no unpaired - // surrogate or out-of-range value. The public tokenizer API encodes its own well-formed UTF-8, - // so this only guards direct byte-level use. + /** + * Checks a single code point for well-formedness: correct trail-byte count and no unpaired + * surrogate or out-of-range value. + * + * @param input The UTF-8 input buffer. + * @param from The offset of the lead byte. + * @param length The candidate sequence length. + * @return {@code true} when the sequence is malformed. + */ private static boolean isMalformed(byte[] input, int from, int length) { if ((input[from] & 0x80) == 0) { return false; diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceTokenizer.java b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceTokenizer.java index 5b39c395a9..959ecff05f 100644 --- a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceTokenizer.java +++ b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceTokenizer.java @@ -487,11 +487,12 @@ private void checkId(int id) { } } - // The embedded self-test samples, exposed for the parity tests. + /** {@return the embedded self-test input samples} */ List selfTestInputs() { return selfTestInputs; } + /** {@return the embedded self-test expected segmentations} */ List selfTestExpected() { return selfTestExpected; } diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/UnigramEncoder.java b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/UnigramEncoder.java index 49ffcc2909..c934150d07 100644 --- a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/UnigramEncoder.java +++ b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/UnigramEncoder.java @@ -24,13 +24,8 @@ * Viterbi segmentation under a unigram language model: of all ways to cover the normalized text * with vocabulary pieces, it finds the one with the highest total log-probability. * - *

This is a port of the reference implementation's optimized single-pass decoder, which - * exploits the unigram independence assumption to keep only the best path ending at each byte - * position instead of a full lattice. Characters no piece covers fall back to the unknown id - * with a fixed penalty below the lowest piece score, and user-defined symbols receive a - * length-based bonus score so they always win. Tie-breaking and score arithmetic follow the - * reference exactly, including its occasional re-basing of accumulated scores on very long - * inputs, because segmentation parity is asserted against it.

+ *

Characters no piece covers fall back to the unknown id with a fixed penalty below the lowest + * piece score, and user-defined symbols receive a length-based bonus score so they always win.

*/ final class UnigramEncoder { diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/Utf8Text.java b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/Utf8Text.java index 6ebb080f5e..e830816e64 100644 --- a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/Utf8Text.java +++ b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/Utf8Text.java @@ -20,10 +20,9 @@ * A caller's text encoded as UTF-8, keeping the map from every byte offset back to the UTF-16 * offset it came from. * - *

The whole pipeline runs in byte space to match the reference implementation, but the spans - * reported to the caller must be UTF-16 offsets into the original {@code CharSequence}; this map - * converts them. An unpaired surrogate, which UTF-8 cannot represent, is encoded as U+FFFD, kept - * deterministic so that parity fixtures can cover it.

+ *

The pipeline runs in UTF-8 byte space, but the spans reported to the caller must be UTF-16 + * offsets into the original {@code CharSequence}; this map converts them. An unpaired surrogate, + * which UTF-8 cannot represent, is encoded as U+FFFD.

*/ final class Utf8Text { @@ -98,8 +97,6 @@ static Utf8Text of(CharSequence text) { c += charCount; } byteToChar[b] = charLength; - // The oversized buffers are kept and carried with an explicit length instead of being - // trimmed; the per-call copies were pure allocation traffic. return new Utf8Text(bytes, b, byteToChar, charLength); } From 5a70734e19d50c09e0f7a654e516b3cd9e986554 Mon Sep 17 00:00:00 2001 From: Kristian Rickert Date: Sun, 12 Jul 2026 20:45:43 -0400 Subject: [PATCH 7/9] OPENNLP-1885: Tighten javadoc to contracts and document helpers and overrides Applies the review conventions from the OPENNLP-1869 review: class javadoc states the contracts instead of design narrative, every override carries inheritDoc with its null contract, and the private helpers are documented. --- .../opennlp/tools/tokenize/SubwordPiece.java | 11 ++-- .../tools/tokenize/SubwordTokenizer.java | 11 ++-- .../tools/tokenize/WordpieceEncoder.java | 5 ++ .../sentencepiece/DoubleArrayTrie.java | 4 +- .../sentencepiece/ModelProtoReader.java | 66 +++++++++++++++---- .../subword/sentencepiece/PieceTrie.java | 30 +++++++-- .../SentencePieceNormalizer.java | 25 +++++++ .../sentencepiece/SentencePieceTokenizer.java | 45 +++++++++++-- .../subword/sentencepiece/UnigramEncoder.java | 3 +- 9 files changed, 161 insertions(+), 39 deletions(-) diff --git a/opennlp-api/src/main/java/opennlp/tools/tokenize/SubwordPiece.java b/opennlp-api/src/main/java/opennlp/tools/tokenize/SubwordPiece.java index 7f790650cf..032d04f1c3 100644 --- a/opennlp-api/src/main/java/opennlp/tools/tokenize/SubwordPiece.java +++ b/opennlp-api/src/main/java/opennlp/tools/tokenize/SubwordPiece.java @@ -22,12 +22,11 @@ * One subword unit produced by a {@link SubwordTokenizer}, carrying both the vocabulary view * (the piece string and its id) and the exact place in the caller's text it came from. * - *

The piece string is in the tokenizer's internal, normalized form (for example, a leading - * word-boundary marker instead of a space), so it is generally not a substring of the input. - * {@code start} and {@code end} are UTF-16 offsets into the original input text, so the surface - * that produced this piece is {@code text.subSequence(start, end)}. Pieces that carry no surface - * of their own (control symbols, or the fill bytes of a byte-fallback expansion) report an empty - * span, {@code start == end}.

+ *

The piece string is in the tokenizer's normalized form, so it is generally not a substring of + * the input. {@code start} and {@code end} are UTF-16 offsets into the original text, so the + * surface that produced this piece is {@code text.subSequence(start, end)}. Pieces that carry no + * surface of their own, such as control symbols or the fill bytes of a byte-fallback expansion, + * report an empty span with {@code start == end}.

* * @param piece The piece in the vocabulary's normalized form; never null or empty. * @param id The vocabulary id of the piece. diff --git a/opennlp-api/src/main/java/opennlp/tools/tokenize/SubwordTokenizer.java b/opennlp-api/src/main/java/opennlp/tools/tokenize/SubwordTokenizer.java index 5c371287f2..befcdd29ea 100644 --- a/opennlp-api/src/main/java/opennlp/tools/tokenize/SubwordTokenizer.java +++ b/opennlp-api/src/main/java/opennlp/tools/tokenize/SubwordTokenizer.java @@ -22,12 +22,11 @@ * Splits text into subword units against a fixed vocabulary, reporting for every unit its * vocabulary id and the exact span of the original text it covers. * - *

Subword tokenization is the input layer of modern sequence models: text is decomposed into - * pieces from a trained vocabulary so that any input, including words never seen in training, maps - * to a bounded id space. Unlike a linguistic {@link Tokenizer}, the segmentation is - * vocabulary-driven, and the pieces are in the model's normalized form rather than substrings of - * the input. The offsets carried by each {@link SubwordPiece} are what tie the two worlds - * together: they always refer to the caller's original text.

+ *

Subword tokenization splits text into pieces drawn from a trained vocabulary, so that any + * input, including words never seen in training, maps to a bounded id space. The segmentation is + * vocabulary-driven rather than linguistic, and each piece is in the model's normalized form, so a + * piece is generally not a substring of the input. The offsets carried by each + * {@link SubwordPiece} always refer to the caller's original text.

* *

Implementations are expected to be safe for concurrent use by multiple threads; any * implementation that is not must document it.

diff --git a/opennlp-api/src/main/java/opennlp/tools/tokenize/WordpieceEncoder.java b/opennlp-api/src/main/java/opennlp/tools/tokenize/WordpieceEncoder.java index 65c73dab0c..ab016bbaa4 100644 --- a/opennlp-api/src/main/java/opennlp/tools/tokenize/WordpieceEncoder.java +++ b/opennlp-api/src/main/java/opennlp/tools/tokenize/WordpieceEncoder.java @@ -171,6 +171,11 @@ private static int requiredId(Map ids, String specialToken) { return id; } + /** + * {@inheritDoc} + * + * @throws IllegalArgumentException Thrown if {@code text} is null. + */ @Override public List encode(CharSequence text) { if (text == null) { diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/DoubleArrayTrie.java b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/DoubleArrayTrie.java index 967b525fe5..0ffb5e8be4 100644 --- a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/DoubleArrayTrie.java +++ b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/DoubleArrayTrie.java @@ -60,8 +60,8 @@ final class DoubleArrayTrie { * key matches. Values are non-negative, so the result is negative only on no-match. */ long longestPrefixMatch(byte[] key, int from, int to) { - // The JVM's own bounds checks guard the walk; a well-formed trie never leaves the array, - // so the translation below is the fail-loud path for corrupt data, not a hot branch. + // The JVM's own bounds checks guard the walk; the catch below translates an out-of-range unit + // reference from corrupt data into a loud failure. final int[] u = units; try { long result = -1; diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/ModelProtoReader.java b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/ModelProtoReader.java index 58f9dd83e1..4122746cb0 100644 --- a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/ModelProtoReader.java +++ b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/ModelProtoReader.java @@ -23,19 +23,11 @@ /** * Reads the binary {@code ModelProto} serialization of a SentencePiece {@code .model} file. * - *

The format is standard protocol-buffer wire encoding of one flat message - * ({@code sentencepiece_model.proto}, Apache License 2.0), so this reader walks the tag stream + *

The format is the standard protocol-buffer wire encoding of one flat message + * ({@code sentencepiece_model.proto}, Apache License 2.0). This reader walks the tag stream * directly and keeps only the fields inference needs: the pieces with scores and types, the - * normalizer spec, the handful of trainer-spec fields that change runtime behavior, and the - * embedded self-test samples. Unknown fields are skipped, malformed input fails loudly.

- * - *

This is a self-contained wire reader, not a performance optimization: model loading happens - * once and is not on any hot path. It exists so this module reads a protobuf-encoded file without - * adding a {@code protobuf-java} runtime dependency, which the project does not otherwise use, for - * a load-time parse of a schema that is stable in practice. If OpenNLP ever takes on a real - * protobuf dependency for other reasons, this hand-rolled reader should be retired in favor of it: - * generate from {@code sentencepiece_model.proto} (or parse via the descriptor API) and delete - * this class, since the dependency-avoidance rationale no longer holds.

+ * normalizer spec, the trainer-spec fields that change runtime behavior, and the embedded + * self-test samples. Unknown fields are skipped, and malformed input fails loudly.

*/ final class ModelProtoReader { @@ -82,6 +74,12 @@ static RawModel read(byte[] data) { return model; } + /** + * Parses one {@code SentencePiece} sub-message and appends its piece, score, and type. + * + * @param model The model to append to. + * @param end The exclusive end offset of the sub-message payload. + */ private void piece(RawModel model, int end) { String piece = null; float score = 0; @@ -107,6 +105,12 @@ private void piece(RawModel model, int end) { model.types.add(type); } + /** + * Parses the {@code TrainerSpec} sub-message, keeping the fields that change runtime behavior. + * + * @param model The model to populate. + * @param end The exclusive end offset of the sub-message payload. + */ private void trainerSpec(RawModel model, int end) { while (pos < end) { final long tag = varint(); @@ -120,6 +124,13 @@ private void trainerSpec(RawModel model, int end) { } } + /** + * Parses the {@code NormalizerSpec} sub-message: the precompiled character map and the + * whitespace-handling flags. + * + * @param model The model to populate. + * @param end The exclusive end offset of the sub-message payload. + */ private void normalizerSpec(RawModel model, int end) { while (pos < end) { final long tag = varint(); @@ -133,6 +144,13 @@ private void normalizerSpec(RawModel model, int end) { } } + /** + * Parses the {@code SelfTestData} sub-message, collecting the input and expected-segmentation + * sample pairs. + * + * @param model The model to populate. + * @param end The exclusive end offset of the sub-message payload. + */ private void selfTestData(RawModel model, int end) { while (pos < end) { final long tag = varint(); @@ -158,7 +176,15 @@ private void selfTestData(RawModel model, int end) { } } - // Returns the exclusive end offset of a length-delimited payload, verifying the wire type. + /** + * Reads the length prefix of a length-delimited field and returns the exclusive end offset of its + * payload. + * + * @param tag The field tag, whose wire type must be length-delimited. + * @return The exclusive end offset of the payload. + * @throws IllegalArgumentException Thrown if the wire type is wrong or the length runs past the + * input. + */ private int lenPayload(long tag) { if ((tag & 7) != WIRE_LEN) { throw malformed("field " + (tag >>> 3) + " is not length-delimited"); @@ -203,6 +229,13 @@ private byte[] bytes(int end) { return b; } + /** + * Reads a base-128 varint from the current position, advancing past it. + * + * @return The decoded value. + * @throws IllegalArgumentException Thrown if the input ends mid-varint or the varint exceeds 64 + * bits. + */ private long varint() { long value = 0; for (int shift = 0; shift < 64; shift += 7) { @@ -218,6 +251,13 @@ private long varint() { throw malformed("varint exceeds 64 bits"); } + /** + * Skips the value of an unrecognized field according to its wire type. + * + * @param tag The field tag. + * @throws IllegalArgumentException Thrown if the wire type is unsupported or the value runs past + * the input. + */ private void skip(long tag) { switch ((int) (tag & 7)) { case WIRE_VARINT -> varint(); diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/PieceTrie.java b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/PieceTrie.java index 37c2ef7912..e296e05b3b 100644 --- a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/PieceTrie.java +++ b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/PieceTrie.java @@ -22,12 +22,10 @@ /** * An immutable byte-level trie over vocabulary pieces, packed into flat arrays. * - *

Encoding walks it one byte at a time ({@link #step(int, byte)}) while scanning the input, so - * every piece that starts at a given input position is enumerated in one forward pass; this is the - * lattice-population step of subword segmentation. This step sits in the innermost loop of the - * encoder, so wide nodes (the root and the first level of a real vocabulary) dispatch through a - * 256-entry direct table, one load per byte, and narrow nodes scan their short sorted label slice - * linearly; both layouts enumerate identical transitions.

+ *

Encoding walks it one byte at a time ({@link #step(int, byte)}), so every piece that starts + * at a given input position is enumerated in one forward pass. Wide nodes dispatch through a + * 256-entry direct table and narrow nodes scan a short sorted label slice; both layouts enumerate + * identical transitions.

*/ final class PieceTrie { @@ -158,6 +156,15 @@ private static final class Builder { this.order = order; } + /** + * Counts the nodes and edges of the subtrie for the sorted key range {@code [from, to)} at the + * given depth. + * + * @param from The inclusive start index into {@code order}. + * @param to The exclusive end index into {@code order}. + * @param depth The byte depth this node partitions on. + * @throws IllegalArgumentException Thrown if a key is defined more than once. + */ void count(int from, int to, int depth) { nodeCount++; int i = from; @@ -182,6 +189,7 @@ void count(int from, int to, int depth) { } } + /** Allocates the packed arrays to the node and edge counts gathered by {@link #count}. */ void allocate() { childStart = new int[nodeCount + 1]; labels = new byte[edgeCount]; @@ -189,6 +197,16 @@ void allocate() { values = new int[nodeCount]; } + /** + * Fills the packed arrays for the sorted key range {@code [from, to)} at the given depth and + * returns the node id assigned to it. + * + * @param from The inclusive start index into {@code order}. + * @param to The exclusive end index into {@code order}. + * @param depth The byte depth this node partitions on. + * @return The id of the node created for this range. + * @throws IllegalArgumentException Thrown if a key is defined more than once. + */ int fill(int from, int to, int depth) { final int node = nextNode++; values[node] = -1; diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceNormalizer.java b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceNormalizer.java index 8c6ed5a4d7..29e8e5e895 100644 --- a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceNormalizer.java +++ b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceNormalizer.java @@ -127,6 +127,7 @@ private static final class Chunk { private int to; private int consumed; + /** {@return whether this chunk is exactly one ASCII space byte} */ boolean isSingleSpace() { return to - from == 1 && data[from] == ' '; } @@ -304,6 +305,15 @@ private void normalizePrefix(byte[] input, int inputLength, int from, Chunk chun chunk.consumed = charLength; } + /** + * Returns the byte length of the longest user-defined symbol that is a prefix of + * {@code input[from, inputLength)}. + * + * @param input The UTF-8 input buffer. + * @param inputLength The number of valid bytes in {@code input}. + * @param from The offset to match from. + * @return The matched length in bytes, or zero when no user-defined symbol matches. + */ private int longestUserDefinedMatch(byte[] input, int inputLength, int from) { int node = userDefinedMatcher.root(); int longest = 0; @@ -364,6 +374,14 @@ private static boolean isMalformed(byte[] input, int from, int length) { || length != minimalUtf8Length(codePoint); } + /** + * Decodes the code point of a UTF-8 sequence of the given length. + * + * @param input The UTF-8 input buffer. + * @param from The offset of the lead byte. + * @param length The sequence length in bytes, from one to four. + * @return The decoded code point. + */ private static int codePointAt(byte[] input, int from, int length) { return switch (length) { case 1 -> input[from] & 0x7F; @@ -375,6 +393,13 @@ private static int codePointAt(byte[] input, int from, int length) { }; } + /** + * Returns the number of bytes the shortest UTF-8 encoding of a code point uses, which detects + * overlong encodings. + * + * @param codePoint The code point. + * @return The minimal encoding length in bytes, from one to four. + */ private static int minimalUtf8Length(int codePoint) { if (codePoint < 0x80) { return 1; diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceTokenizer.java b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceTokenizer.java index 959ecff05f..dd39b894dc 100644 --- a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceTokenizer.java +++ b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceTokenizer.java @@ -41,10 +41,9 @@ * reference implementation piece for piece and id for id, which is what makes the produced ids * valid inputs for models trained against the same vocabulary.

* - *

Beyond parity, every piece carries the exact span of the caller's original text it came - * from, mapped back through the model's own normalizer. The normalizer is also exposed on its own - * through {@link OffsetAwareNormalizer}, so the model's text normalization can be reused as an - * offset-aware step outside of tokenization.

+ *

Every piece carries the exact span of the caller's original text it came from, mapped back + * through the model's own normalizer. That normalizer is also exposed through + * {@link OffsetAwareNormalizer} for reuse as an offset-aware step outside tokenization.

* *

Instances are immutable after loading and safe for concurrent use by multiple threads.

*/ @@ -247,6 +246,11 @@ public static SentencePieceTokenizer load(InputStream in) throws IOException { return new SentencePieceTokenizer(ModelProtoReader.read(in.readAllBytes())); } + /** + * {@inheritDoc} + * + * @throws IllegalArgumentException Thrown if {@code text} is null. + */ @Override public List encode(CharSequence text) { if (text == null) { @@ -326,11 +330,21 @@ public List encode(CharSequence text) { return out; } + /** + * {@inheritDoc} + * + * @throws IllegalArgumentException Thrown if {@code text} is null. + */ @Override public CharSequence normalize(CharSequence text) { return normalizeAligned(text).normalized(); } + /** + * {@inheritDoc} + * + * @throws IllegalArgumentException Thrown if {@code text} is null. + */ @Override public AlignedText normalizeAligned(CharSequence text) { if (text == null) { @@ -376,6 +390,17 @@ public AlignedText normalizeAligned(CharSequence text) { return new AlignedText(text, normalized, builder.build(input.charLength())); } + /** + * Emits the pending alignment group as a replace run, preceded by a deletion for any original + * text skipped before it, and returns the advanced cursor. + * + * @param builder The alignment builder to append to. + * @param cursor The original-text offset reached so far. + * @param origStart The inclusive original-text start of the group. + * @param origEnd The exclusive original-text end of the group. + * @param chars The number of normalized chars in the group; zero flushes nothing. + * @return The original-text offset after the group. + */ private static int flushGroup(Alignment.Builder builder, int cursor, int origStart, int origEnd, int chars) { if (chars == 0) { @@ -480,6 +505,12 @@ public boolean isByte(int id) { return types[id] == TYPE_BYTE; } + /** + * Verifies that an id is a valid vocabulary id. + * + * @param id The id to check. + * @throws IllegalArgumentException Thrown if {@code id} is outside {@code [0, vocabularySize())}. + */ private void checkId(int id) { if (id < 0 || id >= pieces.length) { throw new IllegalArgumentException( @@ -507,6 +538,12 @@ List selfTestExpected() { } } + /** + * Parses a byte-fallback piece string of the form {@code <0xAB>} into its byte value. + * + * @param piece The piece string. + * @return The byte value in {@code [0, 255]}, or {@code -1} when the string is not a byte piece. + */ private static int parseBytePiece(String piece) { if (piece.length() != 6 || !piece.startsWith("<0x") || piece.charAt(5) != '>') { return -1; diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/UnigramEncoder.java b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/UnigramEncoder.java index c934150d07..c87662bef2 100644 --- a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/UnigramEncoder.java +++ b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/UnigramEncoder.java @@ -72,8 +72,7 @@ List encode(byte[] normalized, int size) { } // The best path ending at each byte position (exclusive end), interleaved as - // [startsAt, scoreBits, id] triples so a frontier update touches one cache line. Scores - // travel as raw float bits, a lossless round trip; all arithmetic happens on the floats. + // [startsAt, scoreBits, id] triples; scores travel as raw float bits, a lossless round trip. final int[] best = new int[3 * (size + 1)]; for (int i = 0; i <= size; i++) { best[3 * i] = -1; From b87d457aeea48d918e1ff0eb06ef33bab41addc7 Mon Sep 17 00:00:00 2001 From: Kristian Rickert Date: Mon, 13 Jul 2026 02:33:10 -0400 Subject: [PATCH 8/9] OPENNLP-1885: Declare serialVersionUID on SentencePieceTokenizer The tokenizer is Serializable through the OffsetAwareNormalizer contract but declared no serialVersionUID, which the compiler warns about. Added the serialver-computed value so it matches the convention used across the normalizer classes. --- .../opennlp/subword/sentencepiece/SentencePieceTokenizer.java | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceTokenizer.java b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceTokenizer.java index dd39b894dc..36d63c05e1 100644 --- a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceTokenizer.java +++ b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceTokenizer.java @@ -49,6 +49,10 @@ */ public final class SentencePieceTokenizer implements SubwordTokenizer, OffsetAwareNormalizer { + // Serializable through the OffsetAwareNormalizer contract; the model state is not itself + // serializable, so this only carries the serialver-computed value the convention expects. + private static final long serialVersionUID = -7114394869301531147L; + /** The segmentation algorithm a model was trained with. */ public enum Algorithm { /** Unigram language model, decoded by best-path search. */ From 161e799cca9310bb91c8bc6b4d78a1e40d96c9e4 Mon Sep 17 00:00:00 2001 From: Kristian Rickert Date: Mon, 13 Jul 2026 03:29:12 -0400 Subject: [PATCH 9/9] OPENNLP-1885: Trim residual commentary per review conventions --- .../tools/tokenize/SubwordTokenizer.java | 8 +++---- .../subword/sentencepiece/PieceTrie.java | 2 +- .../SentencePieceNormalizer.java | 8 +++---- .../sentencepiece/SentencePieceTokenizer.java | 22 +++++++------------ 4 files changed, 15 insertions(+), 25 deletions(-) diff --git a/opennlp-api/src/main/java/opennlp/tools/tokenize/SubwordTokenizer.java b/opennlp-api/src/main/java/opennlp/tools/tokenize/SubwordTokenizer.java index befcdd29ea..bd3cdf3984 100644 --- a/opennlp-api/src/main/java/opennlp/tools/tokenize/SubwordTokenizer.java +++ b/opennlp-api/src/main/java/opennlp/tools/tokenize/SubwordTokenizer.java @@ -22,11 +22,9 @@ * Splits text into subword units against a fixed vocabulary, reporting for every unit its * vocabulary id and the exact span of the original text it covers. * - *

Subword tokenization splits text into pieces drawn from a trained vocabulary, so that any - * input, including words never seen in training, maps to a bounded id space. The segmentation is - * vocabulary-driven rather than linguistic, and each piece is in the model's normalized form, so a - * piece is generally not a substring of the input. The offsets carried by each - * {@link SubwordPiece} always refer to the caller's original text.

+ *

The segmentation is vocabulary-driven rather than linguistic, and each piece is in the + * model's normalized form, so a piece is generally not a substring of the input. The offsets + * carried by each {@link SubwordPiece} always refer to the caller's original text.

* *

Implementations are expected to be safe for concurrent use by multiple threads; any * implementation that is not must document it.

diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/PieceTrie.java b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/PieceTrie.java index e296e05b3b..795a251bc8 100644 --- a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/PieceTrie.java +++ b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/PieceTrie.java @@ -33,7 +33,7 @@ final class PieceTrie { static final int DEAD = -1; // A node dispatches through a 256-entry slice of directPool when it has more children than - // this; below it, a linear scan of the sorted label slice wins on memory and is branch-cheap. + // this; otherwise a linear scan of the sorted label slice is used. private static final int DIRECT_THRESHOLD = 8; // Per node: the slice [childStart[n], childStart[n + 1]) of labels/childNodes, and the piece id diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceNormalizer.java b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceNormalizer.java index 29e8e5e895..4d5589514e 100644 --- a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceNormalizer.java +++ b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceNormalizer.java @@ -43,8 +43,7 @@ final class SentencePieceNormalizer { private final boolean treatWhitespaceAsSuffix; private final PieceTrie userDefinedMatcher; // For each possible first byte, whether any character-map rule or user-defined symbol starts - // with it. A clear bit proves normalizePrefix would pass the byte through raw, which lets the - // scan skip the whole prefix machinery for plain ASCII text. + // with it; a clear bit means normalizePrefix passes the byte through raw. private final boolean[] ruleLead = new boolean[256]; /** @@ -118,7 +117,6 @@ record Normalized(byte[] bytes, int length, int[] normToOrig) { /** * One normalization step: {@code consumed} input bytes produced {@code data[from, to)}. The data * array is the input itself (pass-through), the replacement blob, or the replacement character. - * A single mutable scratch is refilled per chunk so the scan allocates nothing per code point. */ private static final class Chunk { @@ -176,8 +174,8 @@ Normalized normalize(byte[] input, int inputLength) { boolean isPrevSpace = removeExtraWhitespaces; while (from < inputLength) { final int lead = input[from] & 0xFF; - // Fast path: an ASCII byte no rule starts with passes through raw; the chunk would be - // the byte itself, no leading-space stripping applies, and it does not end in a space. + // An ASCII byte no rule starts with passes through raw: the chunk is the byte itself, no + // leading-space stripping applies, and it does not end in a space. if (lead < 0x80 && lead != ' ' && !ruleLead[lead]) { normalized.append(input[from]); normToOrig.append(consumed); diff --git a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceTokenizer.java b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceTokenizer.java index 36d63c05e1..961bb58b3c 100644 --- a/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceTokenizer.java +++ b/opennlp-extensions/opennlp-subword/src/main/java/opennlp/subword/sentencepiece/SentencePieceTokenizer.java @@ -33,24 +33,20 @@ import opennlp.tools.util.normalizer.OffsetAwareNormalizer; /** - * A {@link SubwordTokenizer} over a trained SentencePiece model file, implemented purely in Java. - * - *

A {@code .model} file is self-contained: it carries the vocabulary with piece scores and - * types, the segmentation algorithm (unigram language model or byte-pair encoding), and the text - * normalizer the model was trained with. This class runs all three, so its output matches the - * reference implementation piece for piece and id for id, which is what makes the produced ids - * valid inputs for models trained against the same vocabulary.

+ * A {@link SubwordTokenizer} over a trained SentencePiece {@code .model} file, implemented purely + * in Java. The file carries the vocabulary with piece scores and types, the segmentation algorithm + * (unigram language model or byte-pair encoding), and the text normalizer, all of which this class + * runs. * *

Every piece carries the exact span of the caller's original text it came from, mapped back - * through the model's own normalizer. That normalizer is also exposed through - * {@link OffsetAwareNormalizer} for reuse as an offset-aware step outside tokenization.

+ * through the model's own normalizer, which is also exposed through {@link OffsetAwareNormalizer} + * for reuse outside tokenization.

* *

Instances are immutable after loading and safe for concurrent use by multiple threads.

*/ public final class SentencePieceTokenizer implements SubwordTokenizer, OffsetAwareNormalizer { - // Serializable through the OffsetAwareNormalizer contract; the model state is not itself - // serializable, so this only carries the serialver-computed value the convention expects. + // Serializable through the OffsetAwareNormalizer contract. private static final long serialVersionUID = -7114394869301531147L; /** The segmentation algorithm a model was trained with. */ @@ -280,9 +276,7 @@ public List encode(CharSequence text) { for (final Segment segment : segments) { final boolean isUnk = segment.id() == unkId; final boolean isControl = types[segment.id()] == TYPE_CONTROL; - // A non-unknown segment's bytes are exactly the vocabulary piece's bytes (that is what - // the match meant), so the vocabulary string is reused; only unknown segments carry - // surface content that needs decoding. + // A non-unknown segment reuses its vocabulary string; only unknown segments need decoding. final String piece = isUnk ? new String(norm, segment.from(), segment.to() - segment.from(), StandardCharsets.UTF_8) : pieces[segment.id()];

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