# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. # # Licensed 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. import json import os import shutil from typing import Dict, List, Optional from paddle.utils import try_import from paddlenlp.transformers import ( BasicTokenizer, PretrainedTokenizer, WordpieceTokenizer, ) __all__ = [ "SkepTokenizer", ] def bytes_to_unicode(): """ Returns list of utf-8 byte and a corresponding list of unicode strings. The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. This is a signficant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup tables between utf-8 bytes and unicode strings. And avoids mapping to whitespace/control characters the bpe code barfs on. """ bs = list(range(33, 126 + 1)) + list(range(161, 172 + 1)) + list(range(174, 255 + 1)) cs = bs[:] n = 0 for b in range(2**8): if b not in bs: bs.append(b) cs.append(2**8 + n) n += 1 cs = [chr(n) for n in cs] return dict(zip(bs, cs)) def get_pairs(word): """Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings). """ pairs = set() prev_char = word[0] for char in word[1:]: pairs.add((prev_char, char)) prev_char = char return pairs class BpeEncoder(object): """BpeEncoder""" def __init__(self, encoder_json_file, vocab_bpe_file, errors="replace", unk_token="<|endoftext|>", **kwargs): """ Constructs a BpeEncoder. Args: encoder_json_file (`str`): The path to bpe encode json file. vocab_bpe_file (`str`): The path to bpe vocab file. errors (`str`): the error handler unk_token (`str`): the unk token """ self.encoder = self.__get_encoder(encoder_json_file) self.decoder = {v: k for k, v in self.encoder.items()} self.errors = errors # how to handle errors in decoding self.byte_encoder = bytes_to_unicode() self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} self.bpe_ranks = self.__get_bpe_ranks(vocab_bpe_file) self.unk_token = unk_token self.cache = {} re = try_import("regex") self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""") def __get_encoder(self, encoder_json_file): with open(encoder_json_file, "r") as f: encoder = json.load(f) return encoder def __get_bpe_ranks(self, vocab_bpe_file): with open(vocab_bpe_file, "r", encoding="utf-8") as f: bpe_data = f.read() bpe_merges = [tuple(merge_str.split()) for merge_str in bpe_data.split("\n")[1:-1]] bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges)))) return bpe_ranks def bpe(self, token): """ bpe """ if token in self.cache: return self.cache[token] word = tuple(token) pairs = get_pairs(word) if not pairs: return token while True: bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf"))) if bigram not in self.bpe_ranks: break first, second = bigram new_word = [] i = 0 while i < len(word): try: j = word.index(first, i) new_word.extend(word[i:j]) i = j except Exception: new_word.extend(word[i:]) break if word[i] == first and i < len(word) - 1 and word[i + 1] == second: new_word.append(first + second) i += 2 else: new_word.append(word[i]) i += 1 new_word = tuple(new_word) word = new_word if len(word) == 1: break else: pairs = get_pairs(word) word = " ".join(word) self.cache[token] = word return word def encode(self, text: str) -> List[int]: """ encode the text to token_ids TODO(wj-Mcat): to be deprecated """ bpe_tokens = [] re = try_import("regex") for token in re.findall(self.pat, text): token = "".join(self.byte_encoder[b] for b in token.encode("utf-8")) bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(" ")) return bpe_tokens def decode(self, tokens: List[str]) -> str: """ decode TODO(wj-Mcat): to be deprecated """ text = "".join([self.decoder[token] for token in tokens]) text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors) return text def _tokenize(self, text: str) -> List[str]: """tokenize text into tokens with bpe algo Args: text (str): the content of text Returns: List[str]: the sub token of text """ bpe_tokens = [] re = try_import("regex") for token in re.findall(self.pat, text): token = "".join(self.byte_encoder[b] for b in token.encode("utf-8")) bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" ")) return bpe_tokens def _convert_token_to_id(self, token: str) -> int: """Converts a token (str) in an id using the vocab.""" return self.encoder.get(token, self.encoder.get(self.unk_token)) def _convert_id_to_token(self, index: int) -> str: """Converts an index (integer) in a token (str) using the vocab.""" return self.decoder.get(index) def convert_tokens_to_string(self, tokens: List[str]) -> str: """Converts a sequence of tokens (string) in a single string.""" text = "".join(tokens) text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors) return text class SkepTokenizer(PretrainedTokenizer): r""" Constructs a Skep tokenizer. It uses a basic tokenizer to do punctuation splitting, lower casing and so on, and follows a WordPiece tokenizer to tokenize as subwords. This tokenizer inherits from :class:`~paddlenlp.transformers.tokenizer_utils.PretrainedTokenizer` which contains most of the main methods. For more information regarding those methods, please refer to this superclass. Args: vocab_file (str): The vocabulary file path (ends with '.txt') required to instantiate a `WordpieceTokenizer`. bpe_vocab_file (str, optional): The vocabulary file path of a `BpeTokenizer`. Defaults to `None`. bpe_json_file (str, optional): The json file path of a `BpeTokenizer`. Defaults to `None`. use_bpe_encoder (bool, optional): Whether or not to use BPE Encoder. Defaults to `False`. need_token_type_id (bool, optional): Whether or not to use token type id. Defaults to `True`. add_two_sep_token_inter (bool, optional): Whether or not to add two different `sep_token`. Defaults to `False`. unk_token (str, optional): The special token for unknown words. Defaults to "[UNK]". sep_token (str, optional): The special token for separator token. Defaults to "[SEP]". pad_token (str, optional): The special token for padding. Defaults to "[PAD]". cls_token (str, optional): The special token for cls. Defaults to "[CLS]". mask_token (str, optional): The special token for mask. Defaults to "[MASK]". Examples: .. code-block:: from paddlenlp.transformers import SkepTokenizer tokenizer = SkepTokenizer.from_pretrained('skep_ernie_2.0_large_en') encoded_inputs = tokenizer('He was a puppeteer') # encoded_inputs: # { # 'input_ids': [101, 2002, 2001, 1037, 13997, 11510, 102], # 'token_type_ids': [0, 0, 0, 0, 0, 0, 0] # } """ resource_files_names = { "vocab_file": "vocab.txt", "bpe_vocab_file": "vocab.bpe", "bpe_json_file": "encoder.json", } # for save_pretrained pretrained_resource_files_map = { "vocab_file": { "skep_ernie_1.0_large_ch": "https://bj.bcebos.com/paddlenlp/models/transformers/skep/skep_ernie_1.0_large_ch.vocab.txt", "skep_ernie_2.0_large_en": "https://bj.bcebos.com/paddlenlp/models/transformers/skep/skep_ernie_2.0_large_en.vocab.txt", "skep_roberta_large_en": "https://bj.bcebos.com/paddlenlp/models/transformers/skep/skep_roberta_large_en.vocab.txt", }, "bpe_vocab_file": { "skep_ernie_1.0_large_ch": None, "skep_ernie_2.0_large_en": None, "skep_roberta_large_en": "https://bj.bcebos.com/paddlenlp/models/transformers/skep/skep_roberta_large_en.vocab.bpe", }, "bpe_json_file": { "skep_ernie_1.0_large_ch": None, "skep_ernie_2.0_large_en": None, "skep_roberta_large_en": "https://bj.bcebos.com/paddlenlp/models/transformers/skep/skep_roberta_large_en.encoder.json", }, } max_model_input_sizes = { "skep_ernie_1.0_large_ch": 512, "skep_ernie_2.0_large_en": 512, "skep_roberta_large_en": 514, } pretrained_init_configuration = { "skep_ernie_1.0_large_ch": { "do_lower_case": True, "use_bpe_encoder": False, "need_token_type_id": True, "add_two_sep_token_inter": False, }, "skep_ernie_2.0_large_en": { "do_lower_case": True, "use_bpe_encoder": False, "need_token_type_id": True, "add_two_sep_token_inter": False, }, "skep_roberta_large_en": { "do_lower_case": True, "use_bpe_encoder": True, "need_token_type_id": False, "add_two_sep_token_inter": True, }, } def __init__( self, vocab_file, bpe_vocab_file=None, bpe_json_file=None, do_lower_case=True, use_bpe_encoder=False, need_token_type_id=True, add_two_sep_token_inter=False, unk_token="[UNK]", sep_token="[SEP]", pad_token="[PAD]", cls_token="[CLS]", mask_token="[MASK]", **kwargs ): if not os.path.isfile(vocab_file): raise ValueError( "Can't find a vocabulary file at path '{}'. To load the " "vocabulary from a pretrained model please use " "`tokenizer = SkepTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`".format(vocab_file) ) self.vocab_file = vocab_file self.bpe_vocab_file = bpe_vocab_file self.bpe_json_file = bpe_json_file self.vocab = self.load_vocabulary( vocab_file, unk_token=unk_token, pad_token=pad_token, bos_token=cls_token, eos_token=sep_token, mask_token=mask_token, ) self.use_bpe_encoder = use_bpe_encoder self.need_token_type_id = need_token_type_id self.add_two_sep_token_inter = add_two_sep_token_inter if not self.use_bpe_encoder: self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case) self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=unk_token) else: assert (bpe_vocab_file and bpe_json_file) is not None, "bpe_vocab_file and bpe_json_file must be not None." if os.path.isfile(bpe_vocab_file) and os.path.isfile(bpe_json_file): self.bpe_tokenizer = BpeEncoder(bpe_json_file, bpe_vocab_file, unk_token=unk_token) @property def vocab_size(self): r""" Return the size of vocabulary. Returns: int: the size of vocabulary. """ return len(self.vocab) def _tokenize(self, text): r""" End-to-end tokenization for Skep models. Args: text (str): The text to be tokenized. Returns: list: A list of string representing converted tokens. """ split_tokens = [] if not self.use_bpe_encoder: for token in self.basic_tokenizer.tokenize(text): for sub_token in self.wordpiece_tokenizer.tokenize(token): split_tokens.append(sub_token) else: for token in self.bpe_tokenizer._tokenize(text): split_tokens.append(str(token)) return split_tokens def num_special_tokens_to_add(self, pair=False): r""" Returns the number of added tokens when encoding a sequence with special tokens. Args: pair (bool, optional): Returns the number of added tokens in the case of a sequence pair if set to True, returns the number of added tokens in the case of a single sequence if set to False. Defaults to False. Returns: int: Number of tokens added to sequences """ token_ids_0 = [] token_ids_1 = [] return len(self.build_inputs_with_special_tokens(token_ids_0, token_ids_1 if pair else None)) def build_offset_mapping_with_special_tokens(self, offset_mapping_0, offset_mapping_1=None): """ Build offset map from a pair of offset map by concatenating and adding offsets of special tokens. Should be overridden in a subclass if the model has a special way of building those. Args: offset_mapping_0 (List[tuple]): List of char offsets to which the special tokens will be added. offset_mapping_1 (List[tuple], optional): Optional second list of char offsets for offset mapping pairs. Returns: List[tuple]: List of char offsets with the appropriate offsets of special tokens. """ if offset_mapping_1 is None: return [(0, 0)] + offset_mapping_0 + [(0, 0)] return [(0, 0)] + offset_mapping_0 + [(0, 0)] + offset_mapping_1 + [(0, 0)] def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): r""" Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A skep_ernie_1.0_large_ch/skep_ernie_2.0_large_en sequence has the following format: - single sequence: ``[CLS] X [SEP]`` - pair of sequences: ``[CLS] A [SEP] B [SEP]`` A skep_roberta_large_en sequence has the following format: - single sequence: ``[CLS] X [SEP]`` - pair of sequences: ``[CLS] A [SEP] [SEP] B [SEP]`` Args: token_ids_0 (List[int]): List of IDs to which the special tokens will be added. token_ids_1 (List[int], optional): Optional second list of IDs for sequence pairs. Defaults to `None`. Returns: list[int]: List of input_id with the appropriate special tokens. """ if not self.add_two_sep_token_inter: if token_ids_1 is None: return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] _cls = [self.cls_token_id] _sep = [self.sep_token_id] return _cls + token_ids_0 + _sep + token_ids_1 + _sep else: if token_ids_1 is None: return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] _cls = [self.cls_token_id] _sep = [self.sep_token_id] return _cls + token_ids_0 + _sep + _sep + token_ids_1 + _sep def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None): r""" Create a mask from the two sequences passed to be used in a sequence-pair classification task. A skep_ernie_1.0_large_ch/skep_ernie_2.0_large_en sequence pair mask has the following format: :: 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second sequence | If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s). note: There is no need token type ids for skep_roberta_large_ch model. Args: token_ids_0 (List[int]): List of IDs. token_ids_1 (List[int], optional): Optional second list of IDs for sequence pairs. Defaults to `None`. Returns: List[int]: List of token_type_id according to the given sequence(s). """ if self.need_token_type_id: _sep = [self.sep_token_id] _cls = [self.cls_token_id] if token_ids_1 is None: return len(_cls + token_ids_0 + _sep) * [0] return len(_cls + token_ids_0 + _sep) * [0] + len(token_ids_1 + _sep) * [1] else: # For model skep-roberta-large-en, token type ids is no need. return None def save_resources(self, save_directory): """ Save tokenizer related resources to files under `save_directory`. Args: save_directory (str): Directory to save files into. """ for name, file_name in self.resource_files_names.items(): save_path = os.path.join(save_directory, file_name) source_file = getattr(self, name, None) if not source_file: continue if os.path.abspath(source_file) != os.path.abspath(save_path): shutil.copyfile(source_file, save_path) def convert_tokens_to_string(self, tokens: List[str]): """ Converts a sequence of tokens (list of string) in a single string. Args: tokens (list): A list of string representing tokens to be converted. Returns: str: Converted string from tokens. Examples: .. code-block:: from paddlenlp.transformers import RoFormerTokenizer tokenizer = RoFormerTokenizer.from_pretrained('roformer-chinese-base') tokens = tokenizer.tokenize('欢迎使用百度飞桨') #['欢迎', '使用', '百度', '飞', '桨'] strings = tokenizer.convert_tokens_to_string(tokens) #'欢迎 使用 百度 飞 桨' """ # to handle the bpe and wordpiece case if hasattr(self, "wordpiece_tokenizer"): return " ".join(tokens).replace(" ##", "").strip() else: return self.bpe_tokenizer.convert_tokens_to_string(tokens) def _convert_token_to_id(self, token: str) -> int: """Converts a token (str) in an id using the vocab.""" if self.use_bpe_encoder: return self.bpe_tokenizer._convert_token_to_id(token) return super()._convert_token_to_id(token) def _convert_id_to_token(self, index: int) -> str: """Converts an index (integer) in a token (str) using the vocab.""" if self.use_bpe_encoder: return self.bpe_tokenizer._convert_id_to_token(index) return super()._convert_id_to_token(index) def get_special_tokens_mask( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False, ) -> List[int]: """ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer `prepare_for_model` method. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. already_has_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not the token list is already formatted with special tokens for the model. Returns: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True, ) if token_ids_1 is not None: return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1] return [1] + ([0] * len(token_ids_0)) + [1] def get_vocab(self) -> Dict[str, int]: """ Returns the vocabulary as a dictionary of token to index. `tokenizer.get_vocab()[token]` is equivalent to `tokenizer.convert_tokens_to_ids(token)` when `token` is in the vocab. Returns: `Dict[str, int]`: The vocabulary. """ return dict(self.vocab.token_to_idx, **self.added_tokens_encoder)