paddlenlp.transformers.bigbird.tokenizer 源代码

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# Copyright 2021 Google Research and The HuggingFace Inc. team.
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import io
import os
import six
import re
import numpy as np

import sentencepiece as spm
from paddlenlp.data.vocab import Vocab

from .. import PretrainedTokenizer, AddedToken

__all__ = ['BigBirdTokenizer']


[文档]class BigBirdTokenizer(PretrainedTokenizer): """ Constructs an BigBird tokenizer based on `SentencePiece <https://github.com/google/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 <https://github.com/google/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": "https://bj.bcebos.com/paddlenlp/models/transformers/bigbird/sentencepiece_gpt2.model", }, } pretrained_init_configuration = { "bigbird-base-uncased": { "do_lower_case": True }, } def __init__(self, sentencepiece_model_file, do_lower_case=True, encoding="utf8", unk_token="<unk>", sep_token="[SEP]", pad_token="[PAD]", cls_token="[CLS]", mask_token="[MASK]", **kwargs): if not os.path.isfile(sentencepiece_model_file): raise ValueError( "Can't find a vocabulary file at path '{}'. To load the " "vocabulary from a pretrained model please use " "`tokenizer = BigBirdTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" .format(sentencepiece_model_file)) self.encoding = encoding self.sp_model = spm.SentencePieceProcessor() if os.path.isfile(sentencepiece_model_file): self.sp_model.Load(sentencepiece_model_file) vocab_dict = {} for id in range(self.sp_model.get_piece_size()): vocab_dict[self.sp_model.id_to_piece(id)] = id self.vocab = Vocab.from_dict(vocab_dict, unk_token=unk_token) self.start_word_tokens = np.array([ self.vocab._idx_to_token[i][0] == "▁" for i in range(0, len(self.vocab)) ]) self.unk_token = unk_token self.mask_id = vocab_dict[mask_token] self.unk_id = vocab_dict[unk_token] self.cls_id = vocab_dict[cls_token] self.sep_id = vocab_dict[sep_token] self.pad_id = vocab_dict[pad_token] if pad_token in vocab_dict else 0 unk_token = AddedToken( unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token pad_token = AddedToken( pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token cls_token = AddedToken( cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token sep_token = AddedToken( sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token # Mask token behave like a normal word, i.e. include the space before it mask_token = AddedToken( mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token self._build_special_tokens_map_extended( sep_token=sep_token, cls_token=cls_token, unk_token=unk_token, pad_token=pad_token, mask_token=mask_token) @property def vocab_size(self): """ Return the size of vocabulary. Returns: int: The size of vocabulary. """ return len(self.vocab) def _tokenize(self, text): """ End-to-end tokenization for BigBird models. Args: text (str): The text to be tokenized. Returns: List: A list of string representing converted tokens. """ if len(text) == 0: return [] if not isinstance(text, six.string_types): text = text.decode(self.encoding) tokens = self.sp_model.EncodeAsPieces(text) in_vocab_tokens = [] for token in tokens: if token in self.vocab: in_vocab_tokens.append(token) else: in_vocab_tokens.append(self.unk_token) return in_vocab_tokens def __call__(self, text, pair_text=None): """ Converts a string to a list of tokens. Args: text (str): The text to be tokenized. pair_text(str): The pair text to be tokenized. Returns: List(str): A list of string representing converted tokens. Examples: .. code-block:: from paddlenlp.transformers import BigBirdTokenizer tokenizer = BigBirdTokenizer.from_pretrained('bigbird-base-uncased') tokens = tokenizer('He was a puppeteer') ''' ['▁He', '▁was', '▁a', '▁puppet', 'eer'] ''' """ return self._tokenize(text)
[文档] def convert_tokens_to_string(self, tokens): """ Converts a sequence of tokens (list of string) to a single string. Since the usage of WordPiece introducing `##` to concat subwords, also removes `##` when converting. 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 BigBirdTokenizer tokenizer = BigBirdTokenizer.from_pretrained('bert-base-uncased') tokens = tokenizer('He was a puppeteer') strings = tokenizer.convert_tokens_to_string(tokens) """ out_string = " ".join(tokens).replace(" ##", "").strip() return out_string
[文档] 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
[文档] def num_special_tokens_to_add(self, pair=False): """ Returns the number of added tokens when encoding a sequence with special tokens. Args: pair(bool): Whether the input is a sequence pair or a single sequence. Defaults to `False` and the input is a single sequence. 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))
[文档] def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A BigBird sequence has the following format: - single sequence: ``[CLS] X [SEP]`` - pair of sequences: ``[CLS] A [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 token_ids_1 is None: return [self.cls_id] + token_ids_0 + [self.sep_id] _cls = [self.cls_id] _sep = [self.sep_id] return _cls + token_ids_0 + _sep + token_ids_1 + _sep