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141 lines
5.3 KiB
141 lines
5.3 KiB
/*
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* Copyright (C) 2018 The Android Open Source Project
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef LIBTEXTCLASSIFIER_UTILS_BERT_TOKENIZER_H_
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#define LIBTEXTCLASSIFIER_UTILS_BERT_TOKENIZER_H_
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#include <fstream>
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#include <string>
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#include <vector>
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#include "utils/wordpiece_tokenizer.h"
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#include "absl/container/flat_hash_map.h"
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#include "tensorflow_lite_support/cc/text/tokenizers/tokenizer.h"
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#include "tensorflow_lite_support/cc/utils/common_utils.h"
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namespace libtextclassifier3 {
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using ::tflite::support::text::tokenizer::TokenizerResult;
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using ::tflite::support::utils::LoadVocabFromBuffer;
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using ::tflite::support::utils::LoadVocabFromFile;
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constexpr int kDefaultMaxBytesPerToken = 100;
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constexpr int kDefaultMaxCharsPerSubToken = 100;
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constexpr char kDefaultSuffixIndicator[] = "##";
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constexpr bool kDefaultUseUnknownToken = true;
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constexpr char kDefaultUnknownToken[] = "[UNK]";
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constexpr bool kDefaultSplitUnknownChars = false;
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// Result of wordpiece tokenization including subwords and offsets.
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// Example:
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// input: tokenize me please
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// subwords: token ##ize me plea ##se
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// wp_begin_offset: [0, 5, 9, 12, 16]
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// wp_end_offset: [ 5, 8, 11, 16, 18]
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// row_lengths: [2, 1, 1]
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struct WordpieceTokenizerResult
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: tflite::support::text::tokenizer::TokenizerResult {
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std::vector<int> wp_begin_offset;
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std::vector<int> wp_end_offset;
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std::vector<int> row_lengths;
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};
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// Options to create a BertTokenizer.
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struct BertTokenizerOptions {
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int max_bytes_per_token = kDefaultMaxBytesPerToken;
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int max_chars_per_subtoken = kDefaultMaxCharsPerSubToken;
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std::string suffix_indicator = kDefaultSuffixIndicator;
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bool use_unknown_token = kDefaultUseUnknownToken;
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std::string unknown_token = kDefaultUnknownToken;
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bool split_unknown_chars = kDefaultSplitUnknownChars;
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};
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// A flat-hash-map based implementation of WordpieceVocab, used in
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// BertTokenizer to invoke tensorflow::text::WordpieceTokenize within.
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class FlatHashMapBackedWordpiece : public WordpieceVocab {
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public:
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explicit FlatHashMapBackedWordpiece(const std::vector<std::string>& vocab);
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LookupStatus Contains(absl::string_view key, bool* value) const override;
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bool LookupId(absl::string_view key, int* result) const;
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bool LookupWord(int vocab_id, absl::string_view* result) const;
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int VocabularySize() const { return vocab_.size(); }
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private:
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// All words indexed position in vocabulary file.
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std::vector<std::string> vocab_;
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absl::flat_hash_map<absl::string_view, int> index_map_;
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};
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// Wordpiece tokenizer for bert models. Initialized with a vocab file or vector.
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class BertTokenizer : public tflite::support::text::tokenizer::Tokenizer {
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public:
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// Initialize the tokenizer from vocab vector and tokenizer configs.
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explicit BertTokenizer(const std::vector<std::string>& vocab,
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const BertTokenizerOptions& options = {})
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: vocab_{FlatHashMapBackedWordpiece(vocab)}, options_{options} {}
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// Initialize the tokenizer from file path to vocab and tokenizer configs.
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explicit BertTokenizer(const std::string& path_to_vocab,
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const BertTokenizerOptions& options = {})
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: BertTokenizer(LoadVocabFromFile(path_to_vocab), options) {}
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// Initialize the tokenizer from buffer and size of vocab and tokenizer
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// configs.
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BertTokenizer(const char* vocab_buffer_data, size_t vocab_buffer_size,
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const BertTokenizerOptions& options = {})
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: BertTokenizer(LoadVocabFromBuffer(vocab_buffer_data, vocab_buffer_size),
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options) {}
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// Perform tokenization, first tokenize the input and then find the subwords.
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// return tokenized results containing the subwords.
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TokenizerResult Tokenize(const std::string& input) override;
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// Perform tokenization on a single token, return tokenized results containing
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// the subwords.
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TokenizerResult TokenizeSingleToken(const std::string& token);
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// Perform tokenization, return tokenized results containing the subwords.
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TokenizerResult Tokenize(const std::vector<std::string>& tokens);
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// Check if a certain key is included in the vocab.
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LookupStatus Contains(const absl::string_view key, bool* value) const {
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return vocab_.Contains(key, value);
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}
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// Find the id of a wordpiece.
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bool LookupId(absl::string_view key, int* result) const override {
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return vocab_.LookupId(key, result);
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}
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// Find the wordpiece from an id.
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bool LookupWord(int vocab_id, absl::string_view* result) const override {
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return vocab_.LookupWord(vocab_id, result);
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}
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int VocabularySize() const { return vocab_.VocabularySize(); }
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static std::vector<std::string> PreTokenize(const absl::string_view input);
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private:
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FlatHashMapBackedWordpiece vocab_;
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BertTokenizerOptions options_;
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};
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} // namespace libtextclassifier3
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#endif // LIBTEXTCLASSIFIER_UTILS_BERT_TOKENIZER_H_
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