/
tokenization_pegasus_fast.py
230 lines (189 loc) · 10.2 KB
/
tokenization_pegasus_fast.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
# coding=utf-8
# Copyright 2020 Google 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.
""" Tokenization class for model PEGASUS."""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
PegasusTokenizer = None
logger = logging.get_logger(__name__)
SPIECE_UNDERLINE = "▁"
VOCAB_FILES_NAMES = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"},
"tokenizer_file": {
"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"google/pegasus-xsum": 512,
}
class PegasusTokenizerFast(PreTrainedTokenizerFast):
r"""
Construct a "fast" PEGASUS tokenizer (backed by HuggingFace's *tokenizers* library). Based on
[Unigram](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=unigram#models).
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
contains the vocabulary necessary to instantiate a tokenizer.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
<Tip>
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`.
</Tip>
unk_token (`str`, *optional*, defaults to `"<unk>"`):
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.
mask_token (`str`, *optional*, defaults to `"<mask_2>"`):
The token used for masking single token values. This is the token used when training this model with masked
language modeling (MLM). This is the token that the PEGASUS encoder will try to predict during pretraining.
It corresponds to *[MASK2]* in [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive
Summarization](https://arxiv.org/pdf/1912.08777.pdf).
mask_token_sent (`str`, *optional*, defaults to `"<mask_1>"`):
The token used for masking whole target sentences. This is the token used when training this model with gap
sentences generation (GSG). This is the sentence that the PEGASUS decoder will try to predict during
pretraining. It corresponds to *[MASK1]* in [PEGASUS: Pre-training with Extracted Gap-sentences for
Abstractive Summarization](https://arxiv.org/pdf/1912.08777.pdf).
additional_special_tokens (`List[str]`, *optional*):
Additional special tokens used by the tokenizer. If no additional_special_tokens are provided <mask_2> and
<unk_2, ..., unk_102> are used as additional special tokens corresponding to the [original PEGASUS
tokenizer](https://github.com/google-research/pegasus/blob/939830367bcf411193d2b5eca2f2f90f3f9260ca/pegasus/ops/pretrain_parsing_ops.cc#L66)
that uses the tokens 2 - 104 only for pretraining
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
slow_tokenizer_class = PegasusTokenizer
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
vocab_file=None,
tokenizer_file=None,
pad_token="<pad>",
eos_token="</s>",
unk_token="<unk>",
mask_token="<mask_2>",
mask_token_sent="<mask_1>",
additional_special_tokens=None,
offset=103, # entries 2 - 104 are only used for pretraining
**kwargs,
):
self.offset = offset
if additional_special_tokens is not None:
if not isinstance(additional_special_tokens, list):
raise TypeError(
f"additional_special_tokens should be of type {type(list)}, but is"
f" {type(additional_special_tokens)}"
)
additional_special_tokens_extended = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
f"<unk_{i}>" for i in range(len(additional_special_tokens_extended), self.offset - 1)
]
if len(set(additional_special_tokens_extended)) != len(additional_special_tokens_extended):
raise ValueError(
"Please make sure that the provided additional_special_tokens do not contain an incorrectly"
f" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."
)
additional_special_tokens = additional_special_tokens_extended
else:
additional_special_tokens = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [f"<unk_{i}>" for i in range(2, self.offset)]
# pegasus was design to support changing the index of the first tokens. If one of the padding/eos/unk/mask token
# is different from default, we must rebuild the vocab
from_slow = kwargs.pop("from_slow", None)
from_slow = from_slow or str(pad_token) != "<pad>" or str(eos_token) != "</s>" or str(unk_token) != "<unk>"
kwargs.pop("added_tokens_decoder", {})
super().__init__(
vocab_file,
tokenizer_file=tokenizer_file,
pad_token=pad_token,
eos_token=eos_token,
unk_token=unk_token,
mask_token=mask_token,
mask_token_sent=mask_token_sent,
offset=offset,
additional_special_tokens=additional_special_tokens,
from_slow=from_slow,
**kwargs,
)
self.vocab_file = vocab_file
@property
def can_save_slow_tokenizer(self) -> bool:
return os.path.isfile(self.vocab_file) if self.vocab_file else False
def _special_token_mask(self, seq):
all_special_ids = set(self.all_special_ids) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id) # <unk> is only sometimes special
if all_special_ids != set(range(len(self.additional_special_tokens) + 3)):
raise ValueError(
"There should be 3 special tokens: mask_token, pad_token, and eos_token +"
f" {len(self.additional_special_tokens)} additional_special_tokens, but got {all_special_ids}"
)
return [1 if x in all_special_ids else 0 for x in seq]
def get_special_tokens_mask(
self, token_ids_0: List, token_ids_1: Optional[List] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""Get list where entries are [1] if a token is [eos] or [pad] else 0."""
if already_has_special_tokens:
return self._special_token_mask(token_ids_0)
elif token_ids_1 is None:
return self._special_token_mask(token_ids_0) + [1]
else:
return self._special_token_mask(token_ids_0 + token_ids_1) + [1]
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None) -> List[int]:
"""
Build model inputs from a sequence by adding eos to the end. no bos token is added to the front.
- single sequence: `X </s>`
- pair of sequences: `A B </s>` (not intended use)
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 token_ids_0 + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_0 + token_ids_1 + [self.eos_token_id]
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer."
)
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
out_vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
)
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
copyfile(self.vocab_file, out_vocab_file)
return (out_vocab_file,)