paddlenlp.transformers.bigbird.tokenizer 源代码

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2021 Google Research 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

import re
import warnings

import numpy as np
import sentencepiece as spm

from import Vocab

from ..albert.tokenizer import AlbertEnglishTokenizer

__all__ = ["BigBirdTokenizer"]

PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"bigbird-base-uncased": 4096}

[文档]class BigBirdTokenizer(AlbertEnglishTokenizer): """ Constructs an BigBird tokenizer based on `SentencePiece <>`__. 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: sentencepiece_model_file (str): The vocabulary file (ends with '.spm') required to instantiate a `SentencePiece <>`__ tokenizer. do_lower_case (bool): Whether the text strips accents and convert to Whether or not to lowercase the input when tokenizing. Defaults to`True`. unk_token (str): A special token representing the *unknown (out-of-vocabulary)* token. An unknown token is set to be `unk_token` inorder to be converted to an ID. Defaults to "[UNK]". sep_token (str): A special token separating two different sentences in the same input. Defaults to "[SEP]". pad_token (str): A special token used to make arrays of tokens the same size for batching purposes. Defaults to "[PAD]". cls_token (str): A special token used for sequence classification. It is the last token of the sequence when built with special tokens. Defaults to "[CLS]". mask_token (str): A special token representing a masked token. This is the token used in the masked language modeling task which the model tries to predict the original unmasked ones. Defaults to "[MASK]". Raises: ValueError: If file sentencepiece_model_file doesn't exist. """ resource_files_names = { "sentencepiece_model_file": "sentencepiece_gpt2.model", } # for save_pretrained pretrained_resource_files_map = { "sentencepiece_model_file": { "bigbird-base-uncased": "", }, } pretrained_init_configuration = { "bigbird-base-uncased": {"do_lower_case": False}, } max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES def __init__( self, sentencepiece_model_file, do_lower_case=False, remove_space=True, keep_accents=True, eos_token="</s>", unk_token="<unk>", sep_token="[SEP]", pad_token="[PAD]", cls_token="[CLS]", mask_token="[MASK]", extra_ids=100, additional_special_tokens=[], sp_model_kwargs=None, encoding="utf8", **kwargs ): self.do_lower_case = do_lower_case self.remove_space = remove_space self.keep_accents = keep_accents self.extra_ids = extra_ids self.sentencepiece_model_file = sentencepiece_model_file self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) self.sp_model.Load(sentencepiece_model_file) self.encoding = encoding vocab_dict = {} for id in range(self.sp_model.get_piece_size()): vocab_dict[self.sp_model.id_to_piece(id)] = id vocab_ = Vocab.from_dict(vocab_dict, unk_token=unk_token) self.start_word_tokens = np.array([vocab_._idx_to_token[i][0] == "▁" for i in range(0, len(vocab_))]) self.unk_token = unk_token self.mask_id = vocab_dict[mask_token] if mask_token in vocab_dict else 0 self.unk_id = vocab_dict[unk_token] if unk_token in vocab_dict else 0 self.cls_id = vocab_dict[cls_token] if cls_token in vocab_dict else 0 self.sep_id = vocab_dict[sep_token] if sep_token in vocab_dict else 0 self.pad_id = vocab_dict[pad_token] if pad_token in vocab_dict else 0 def __call__( self, text, text_pair=None, max_length=None, stride=0, is_split_into_words=False, padding=None, truncation="longest_first", return_position_ids=False, return_token_type_ids=False, return_attention_mask=True, return_length=False, return_overflowing_tokens=False, return_special_tokens_mask=False, **kwargs ): if "pad_to_max_seq_len" in kwargs and padding is None: pad_to_max_seq_len = kwargs.pop("pad_to_max_seq_len") padding = "max_length" if pad_to_max_seq_len else False elif padding is None: padding = False if "max_seq_len" in kwargs and max_length is None: max_length = kwargs["max_seq_len"] if "truncation_strategy" in kwargs and kwargs["truncation_strategy"] != "longest_first": truncation = kwargs["truncation_strategy"] return super(BigBirdTokenizer, self).__call__( text=text, text_pair=text_pair, max_length=max_length, stride=stride, is_split_into_words=is_split_into_words, padding=padding, truncation=truncation, return_position_ids=return_position_ids, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_length=return_length, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, **kwargs, ) @property def vocab_size(self): return len(self.sp_model) + self.extra_ids def _add_eos_if_not_present(self, token_ids): """Do not add eos again if user already added it.""" if len(token_ids) > 0 and token_ids[-1] == self.eos_token_id: warnings.warn( f"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated eos tokens being added." ) return token_ids else: return token_ids + [self.eos_token_id]
[文档] def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1): """ Build model inputs from a sequence or a pair of sequence. An BigBird sequence has the following format: - single sequence: ``X </s>`` - pair of sequences: ``A </s> B </s>`` 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. """ token_ids_0 = self._add_eos_if_not_present(token_ids_0) if token_ids_1 is None: return token_ids_0 else: token_ids_1 = self._add_eos_if_not_present(token_ids_1) return token_ids_0 + token_ids_1
[文档] 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 offset_mapping_0 + [(0, 0)] return offset_mapping_0 + [(0, 0)] + offset_mapping_1 + [(0, 0)]
[文档] def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None): """ Create a mask from the two sequences. 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_id according to the given sequence(s). """ eos = [self.eos_token_id] if token_ids_1 is None: return len(token_ids_0 + eos) * [0] return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
[文档] def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False): """ Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer ``encode`` methods. Args: token_ids_0 (List[int]): List of ids of the first sequence. token_ids_1 (List[int], optional): List of ids of the second sequence. already_has_special_tokens (bool, optional): Whether or not the token list is already formatted with special tokens for the model. Defaults to None. Returns: List[int]: The 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, ) # normal case: some special tokens if token_ids_1 is None: return ([0] * len(token_ids_0)) + [1] return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
[文档] def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (string) in a single string.""" current_sub_tokens = [] out_string = "" for token in tokens: # make sure that special tokens are not decoded using sentencepiece model if token in self.all_special_tokens: out_string += self.sp_model.decode_pieces(current_sub_tokens) + token + " " current_sub_tokens = [] else: current_sub_tokens.append(token) out_string += self.sp_model.decode_pieces(current_sub_tokens) return out_string.strip()
def _convert_token_to_id(self, token): """Converts a token (str) in an id using the vocab.""" if token.startswith("<extra_id_"): match = re.match(r"<extra_id_(\d+)>", token) num = int( return self.vocab_size - num - 1 return self.sp_model.piece_to_id(token) def _convert_id_to_token(self, index): """Converts an index (integer) in a token (str) using the vocab.""" if index < self.sp_model.get_piece_size(): token = self.sp_model.IdToPiece(index) else: token = f"<extra_id_{self.vocab_size - 1 - index}>" return token def _encode(self, text, max_seq_len=None, max_pred_len=None, masked_lm_prob=0.15): """ Returns a tuple containing the encoded sequence and mask information. Args: text (str,list[str] or list[int]): The first sequence to be encoded. This can be a string, a list of strings (tokenized string using the `tokenize` method) or a list of integers (tokenized string ids using the `convert_tokens_to_ids` method) max_seq_len (int, optional): If set to a number, will limit the total sequence returned so that it has a maximum length. If set to None, will not limit the total sequence. Defaults to None. max_pred_len (int, optional): If set to a number, will limit the mask sequence returned so that it has a maximum prediction length. If set to None, will not limit the mask sequence. masked_lm_prob (float, optional): The probability of the token to be masked. Defaults to `0.15`. Returns: tuple: Returns tuple (span_ids, masked_lm_positions, masked_lm_ids, masked_lm_weights). """ def get_input_ids(text): if isinstance(text, str): text = re.sub("[\n]+", "", text) tokens = self._tokenize(text) return self.convert_tokens_to_ids(tokens) elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], str): return self.convert_tokens_to_ids(text) elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], int): return text else: raise ValueError( "Input is not valid. Should be a string, a list/tuple of strings or a list/tuple of integers." ) ids = get_input_ids(text) # Find the span for in the text max_seq_len = len(ids) if max_seq_len is None else max_seq_len max_pred_len = len(ids) if max_pred_len is None else max_pred_len end_pos = max_seq_len - 2 + np.random.randint(max(1, len(ids) - max_seq_len - 2)) start_pos = max(0, end_pos - max_seq_len + 2) span_ids = ids[start_pos:end_pos] word_begin_flag = self.start_word_tokens[span_ids] word_begin_pos = np.flatnonzero(word_begin_flag).astype(np.int32) if word_begin_pos.size == 0: word_begin_pos = np.arange(len(span_ids), dtype=np.int32) word_begin_flag = np.logical_not(word_begin_flag) first_start_pos = word_begin_pos[0] span_ids = span_ids[first_start_pos:] num_tokens = len(span_ids) word_begin_pos = word_begin_pos - first_start_pos words = np.split(np.arange(len(span_ids), dtype="int32"), word_begin_pos)[1:] assert len(words) == len(word_begin_pos) num_to_predict = min(max_pred_len, max(1, int(round(len(word_begin_pos) * masked_lm_prob)))) masked_lm_positions = np.concatenate( np.random.choice(np.array([[]] + words, dtype=np.object)[1:], num_to_predict, replace=False), 0 ) if len(masked_lm_positions) > max_pred_len: masked_lm_positions = masked_lm_positions[: max_pred_len + 1] truncate_masking_flag = np.flatnonzero(word_begin_flag[masked_lm_positions]) if len(truncate_masking_flag) == 0: truncate_masking_index = max_pred_len else: truncate_masking_index = truncate_masking_flag[-1] masked_lm_positions = masked_lm_positions[:truncate_masking_index] span_ids = np.array(span_ids, dtype="int32") masked_lm_positions = np.sort(masked_lm_positions) masked_lm_ids = np.array(span_ids)[masked_lm_positions] random_prob = np.random.rand(len(masked_lm_positions)) mask_pos = masked_lm_positions[random_prob < 0.8] random_pos = masked_lm_positions[random_prob > 0.9] span_ids[mask_pos] = self.mask_id span_ids[random_pos] = np.random.randint(self.unk_id + 1, self.vocab_size, len(random_pos), dtype=np.int32) span_ids = np.concatenate( [np.array([self.cls_id], dtype=np.int32), span_ids, np.array([self.sep_id], dtype=np.int32)] ) padding_len = max_seq_len - num_tokens - 2 span_ids = np.pad(span_ids, [0, padding_len], "constant") pred_padding_len = max_pred_len - len(masked_lm_positions) masked_lm_weights = np.pad( np.ones_like(masked_lm_positions, dtype=np.float32), [0, pred_padding_len], "constant" ) masked_lm_positions = np.pad(masked_lm_positions + 1, [0, pred_padding_len], "constant") masked_lm_ids = np.pad(masked_lm_ids, [0, pred_padding_len], "constant") return span_ids, masked_lm_positions, masked_lm_ids, masked_lm_weights