152 lines
6.2 KiB
C++
152 lines
6.2 KiB
C++
/* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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 MEDIAPIPE_TASKS_CC_TEXT_TOKENIZERS_BERT_TOKENIZER_H_
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#define MEDIAPIPE_TASKS_CC_TEXT_TOKENIZERS_BERT_TOKENIZER_H_
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#include <cstddef>
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#include <fstream>
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#include <string>
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#include <vector>
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#include "absl/container/flat_hash_map.h"
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#include "absl/strings/string_view.h"
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#include "mediapipe/tasks/cc/text/tokenizers/tokenizer.h"
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#include "mediapipe/tasks/cc/text/utils/vocab_utils.h"
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#include "re2/re2.h"
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#include "tensorflow_text/core/kernels/wordpiece_tokenizer.h"
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namespace mediapipe {
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namespace tasks {
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namespace text {
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namespace tokenizers {
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constexpr char kDefaultDelimRe[] =
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R"((\s+|[!-/]|[:-@]|[\[-`]|[{-~]|[\p{P}]|[\x{4E00}-\x{9FFF}]|[\x{3400}-\x{4DBF}]|[\x{20000}-\x{2A6DF}]|[\x{2A700}-\x{2B73F}]|[\x{2B740}-\x{2B81F}]|[\x{2B820}-\x{2CEAF}]|[\x{F900}-\x{FAFF}]|[\x{2F800}-\x{2FA1F}]))";
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constexpr char kDefaultIncludeDelimRe[] =
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R"(([!-/]|[:-@]|[\[-`]|[{-~]|[\p{P}]|[\x{4E00}-\x{9FFF}]|[\x{3400}-\x{4DBF}]|[\x{20000}-\x{2A6DF}]|[\x{2A700}-\x{2B73F}]|[\x{2B740}-\x{2B81F}]|[\x{2B820}-\x{2CEAF}]|[\x{F900}-\x{FAFF}]|[\x{2F800}-\x{2FA1F}]))";
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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 : 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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std::string delim_str = kDefaultDelimRe;
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std::string include_delim_str = kDefaultIncludeDelimRe;
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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 tensorflow::text::WordpieceVocab {
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public:
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explicit FlatHashMapBackedWordpiece(const std::vector<std::string>& vocab);
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tensorflow::text::LookupStatus Contains(absl::string_view key,
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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 mediapipe::tasks::text::tokenizers::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)},
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options_{options},
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delim_re_{options.delim_str},
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include_delim_re_{options.include_delim_str} {}
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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(mediapipe::tasks::text::LoadVocabFromFile(path_to_vocab),
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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(mediapipe::tasks::text::LoadVocabFromBuffer(
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vocab_buffer_data, vocab_buffer_size),
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options) {}
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// Perform tokenization, return tokenized results containing the subwords.
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TokenizerResult Tokenize(const std::string& input) override;
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// Perform tokenization, return wordpiece-specific tokenized result including
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// subwords and offsets
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WordpieceTokenizerResult TokenizeWordpiece(const std::string& input) const;
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// Check if a certain key is included in the vocab.
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tensorflow::text::LookupStatus Contains(const absl::string_view key,
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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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private:
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mediapipe::tasks::text::tokenizers::FlatHashMapBackedWordpiece vocab_;
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BertTokenizerOptions options_;
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RE2 delim_re_;
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RE2 include_delim_re_;
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};
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} // namespace tokenizers
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} // namespace text
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} // namespace tasks
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} // namespace mediapipe
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#endif // MEDIAPIPE_TASKS_CC_TEXT_TOKENIZERS_BERT_TOKENIZER_H_
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