# coding=utf-8 # Copyright 2022 The HuggingFace Team and Microsoft Research AI4Science. All rights reserved. # # 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. """Tokenization classes for BioGPT.""" import json import os from typing import List, Optional, Tuple from ...tokenization_utils import PreTrainedTokenizer from ...utils import logging logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = { "vocab_file": "vocab.json", "merges_file": "merges.txt", } PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json", }, "merges_file": {"microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt"}, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "microsoft/biogpt": 1024, } 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 BioGptTokenizer(PreTrainedTokenizer): """ Construct an FAIRSEQ Transformer tokenizer. Moses tokenization followed by Byte-Pair Encoding. This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (`str`): Path to the vocabulary file. merges_file (`str`): Merges file. unk_token (`str`, *optional*, defaults to `""`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. bos_token (`str`, *optional*, defaults to `""`): The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the `cls_token`. eos_token (`str`, *optional*, defaults to `""`): The end of sequence token. When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the `sep_token`. sep_token (`str`, *optional*, defaults to `""`): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. pad_token (`str`, *optional*, defaults to `""`): The token used for padding, for example when batching sequences of different lengths. """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = ["input_ids", "attention_mask"] def __init__( self, vocab_file, merges_file, unk_token="", bos_token="", eos_token="", sep_token="", pad_token="", **kwargs, ): try: import sacremoses except ImportError: raise ImportError( "You need to install sacremoses to use BioGptTokenizer. " "See https://pypi.org/project/sacremoses/ for installation." ) self.lang = "en" self.sm = sacremoses # cache of sm.MosesTokenizer instance self.cache_moses_tokenizer = {} self.cache_moses_detokenizer = {} """ Initialisation""" with open(vocab_file, encoding="utf-8") as vocab_handle: self.encoder = json.load(vocab_handle) self.decoder = {v: k for k, v in self.encoder.items()} with open(merges_file, encoding="utf-8") as merges_handle: merges = merges_handle.read().split("\n")[:-1] merges = [tuple(merge.split()[:2]) for merge in merges] self.bpe_ranks = dict(zip(merges, range(len(merges)))) self.cache = {} super().__init__( bos_token=bos_token, eos_token=eos_token, sep_token=sep_token, unk_token=unk_token, pad_token=pad_token, **kwargs, ) @property def vocab_size(self): """Returns vocab size""" return len(self.encoder) def get_vocab(self): return dict(self.encoder, **self.added_tokens_encoder) def moses_tokenize(self, text, lang): if lang not in self.cache_moses_tokenizer: moses_tokenizer = self.sm.MosesTokenizer(lang=lang) self.cache_moses_tokenizer[lang] = moses_tokenizer return self.cache_moses_tokenizer[lang].tokenize( text, aggressive_dash_splits=True, return_str=False, escape=True ) def moses_detokenize(self, tokens, lang): if lang not in self.cache_moses_detokenizer: moses_detokenizer = self.sm.MosesDetokenizer(lang=lang) self.cache_moses_detokenizer[lang] = moses_detokenizer return self.cache_moses_detokenizer[lang].detokenize(tokens) def bpe(self, token): word = tuple(token[:-1]) + (token[-1] + "",) if token in self.cache: return self.cache[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) except ValueError: new_word.extend(word[i:]) break else: new_word.extend(word[i:j]) i = j 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) if word == "\n ": word = "\n" self.cache[token] = word return word def _tokenize(self, text, bypass_tokenizer=False): """Returns a tokenized string.""" if bypass_tokenizer: text = text.split() else: text = self.moses_tokenize(text, self.lang) split_tokens = [] for token in text: if token: split_tokens.extend(list(self.bpe(token).split(" "))) return split_tokens def _convert_token_to_id(self, token): """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): """Converts an index (integer) in a token (str) using the vocab.""" return self.decoder.get(index, self.unk_token) def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (string) in a single string.""" # remove BPE tokens = [t.replace(" ", "").replace("", " ") for t in tokens] tokens = "".join(tokens).split() # detokenize text = self.moses_detokenize(tokens, self.lang) return text def build_inputs_with_special_tokens( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A BioGPT sequence has the following format: - single sequence: ` X ` - pair of sequences: ` A B ` 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. Returns: `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. """ if token_ids_1 is None: return [self.sep_token_id] + token_ids_0 sep = [self.sep_token_id] return sep + token_ids_0 + sep + token_ids_1 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: `List[int]`: 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 ) # no bos used in fairseq if token_ids_1 is not None: return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) return [1] + ([0] * len(token_ids_0)) def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. A FAIRSEQ Transformer 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). Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s). """ sep = [self.sep_token_id] # no bos used in fairseq if token_ids_1 is None: return len(token_ids_0 + sep) * [0] return len(token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1] def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) merge_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] ) with open(vocab_file, "w", encoding="utf-8") as f: f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n") index = 0 with open(merge_file, "w", encoding="utf-8") as writer: for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]): if index != token_index: logger.warning( f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive." " Please check that the tokenizer is not corrupted!" ) index = token_index writer.write(" ".join(bpe_tokens) + "\n") index += 1 return vocab_file, merge_file def __getstate__(self): state = self.__dict__.copy() state["sm"] = None return state def __setstate__(self, d): self.__dict__ = d try: import sacremoses except ImportError: raise ImportError( "You need to install sacremoses to use XLMTokenizer. " "See https://pypi.org/project/sacremoses/ for installation." ) self.sm = sacremoses