# 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
#
# 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 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']
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"bigbird-base-uncased": 4096}
[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
},
}
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
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))