# 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 re
import warnings
import numpy as np
import sentencepiece as spm
from paddlenlp.data.vocab import Vocab
from ..albert.tokenizer import AlbertEnglishTokenizer
__all__ = ["BigBirdTokenizer"]
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"bigbird-base-uncased": 4096}
[docs]class BigBirdTokenizer(AlbertEnglishTokenizer):
"""
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": 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]
[docs] 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)]
[docs] 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]
[docs] 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]
[docs] 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(match.group(1))
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
