File size: 12,517 Bytes
a93efa9 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 |
"""Tokenization classes for ChatGLM."""
import sys
import unicodedata
from typing import List, Optional, Union
from functools import lru_cache
import os
import collections
import re
from transformers.tokenization_utils import PreTrainedTokenizer
from icetk.text_tokenizer import TextTokenizer
from icetk.utils import auto_create
import icetk.sentencepiece_model_pb2 as sp_model
from transformers.utils import logging
logger = logging.get_logger(__name__)
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"THUDM/chatglm-6b": 2048,
}
class SPTokenizer:
def __init__(
self,
vocab_file,
max_blank_length=80,
byte_fallback=True,
):
assert vocab_file is not None
self.vocab_file = vocab_file
self.special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "<unused_0>", "<sop>", "<eop>", "<ENC>", "<dBLOCK>"]
self.max_blank_length = max_blank_length
self.byte_fallback = byte_fallback
self.text_tokenizer = self._build_text_tokenizer(encode_special_tokens=False)
self.special_text_tokenizer = self._build_text_tokenizer(encode_special_tokens=True)
@staticmethod
def _configure_tokenizer(
text_tokenizer: TextTokenizer,
special_tokens: List[str],
max_blank_length: int,
byte_fallback: bool,
encode_special_tokens=False,
):
# special token
special_token_type = 4 if encode_special_tokens else 3 # 3 - CONTROL, 4 - USER_DEFINE
for token in special_tokens:
text_tokenizer.proto.pieces.append(
sp_model.ModelProto.SentencePiece(piece=token, score=0.0, type=special_token_type)
)
# whitespaces
for token in [SPTokenizer.get_tab_token()] + [
SPTokenizer.get_blank_token(i) for i in range(2, max_blank_length + 1)
]:
text_tokenizer.proto.pieces.append(sp_model.ModelProto.SentencePiece(piece=token, score=0.0, type=4))
# byte fallback
if byte_fallback:
text_tokenizer.proto.trainer_spec.byte_fallback = True
for i in range(256):
text_tokenizer.proto.pieces.append(
sp_model.ModelProto.SentencePiece(piece="<0x{:02X}>".format(i), score=0.0, type=6)
)
text_tokenizer.refresh()
def _build_text_tokenizer(self, encode_special_tokens=False):
tokenizer = TextTokenizer(self.vocab_file)
self._configure_tokenizer(
tokenizer, self.special_tokens, self.max_blank_length, self.byte_fallback, encode_special_tokens
)
return tokenizer
def _get_text_tokenizer(self, encode_special_tokens=False):
if encode_special_tokens:
return self.special_text_tokenizer
else:
return self.text_tokenizer
@staticmethod
def get_blank_token(length: int):
assert length >= 2
return f"<|blank_{length}|>"
@staticmethod
def get_tab_token():
return f"<|tab|>"
@property
def num_image_tokens(self):
return 20000
@property
def num_text_tokens(self):
return self.text_tokenizer.num_tokens
@property
def num_tokens(self):
return self.num_image_tokens + self.num_text_tokens
@staticmethod
def _encode_whitespaces(text: str, max_len: int = 80):
text = text.replace("\t", SPTokenizer.get_tab_token())
for i in range(max_len, 1, -1):
text = text.replace(" " * i, SPTokenizer.get_blank_token(i))
return text
def _preprocess(self, text: str, linebreak=True, whitespaces=True):
if linebreak:
text = text.replace("\n", "<n>")
if whitespaces:
text = self._encode_whitespaces(text, max_len=self.max_blank_length)
return text
def encode(
self, text: str, linebreak=True, whitespaces=True, special_tokens=False, add_dummy_prefix=True
) -> List[int]:
"""
@param text: Text to encode.
@param linebreak: Whether to encode newline (\n) in text.
@param whitespaces: Whether to encode multiple whitespaces or tab in text, useful for source code encoding.
@param special_tokens: Whether to encode special token ([MASK], [gMASK], etc.) in text.
@param add_dummy_prefix: Whether to add dummy blank space in the beginning.
"""
text = self._preprocess(text, linebreak, whitespaces)
if not add_dummy_prefix:
text = "<n>" + text
tmp = self._get_text_tokenizer(encode_special_tokens=special_tokens).encode(text)
tokens = [x + self.num_image_tokens for x in tmp]
return tokens if add_dummy_prefix else tokens[2:]
def decode(self, text_ids: List[int], special_tokens=False) -> str:
ids = [int(_id) - self.num_image_tokens for _id in text_ids]
ids = [_id for _id in ids if _id >= 0]
text = self._get_text_tokenizer(encode_special_tokens=special_tokens).decode(ids)
text = text.replace("<n>", "\n")
text = text.replace(SPTokenizer.get_tab_token(), "\t")
for i in range(2, self.max_blank_length + 1):
text = text.replace(self.get_blank_token(i), " " * i)
return text
def tokenize(
self, text: str, linebreak=True, whitespaces=True, special_tokens=False, add_dummy_prefix=True
) -> List[str]:
"""
@param text: Text to encode.
@param linebreak: Whether to encode newline (\n) in text.
@param whitespaces: Whether to encode multiple whitespaces or tab in text, useful for source code encoding.
@param special_tokens: Whether to encode special token ([MASK], [gMASK], etc.) in text.
@param add_dummy_prefix: Whether to add dummy blank space in the beginning.
"""
text = self._preprocess(text, linebreak, whitespaces)
if not add_dummy_prefix:
text = "<n>" + text
tokens = self._get_text_tokenizer(encode_special_tokens=special_tokens).tokenize(text)
return tokens if add_dummy_prefix else tokens[2:]
def __getitem__(self, x: Union[int, str]):
if isinstance(x, int):
if x < self.num_image_tokens:
return "<image_{}>".format(x)
else:
return self.text_tokenizer.convert_id_to_token(x - self.num_image_tokens)
elif isinstance(x, str):
if x.startswith("<image_") and x.endswith(">") and x[7:-1].isdigit():
return int(x[7:-1])
else:
return self.text_tokenizer.convert_token_to_id(x) + self.num_image_tokens
else:
raise ValueError("The key should be str or int.")
class ChatGLMTokenizer(PreTrainedTokenizer):
"""
Construct a ChatGLM tokenizer. Based on byte-level Byte-Pair-Encoding.
Args:
vocab_file (`str`):
Path to the vocabulary file.
"""
vocab_files_names = {"vocab_file": "ice_text.model"}
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names = ["input_ids"]
def __init__(
self,
vocab_file,
do_lower_case=False,
remove_space=False,
bos_token='sop',
eos_token='eos',
eop_token='eop',
mask_token='[MASK]',
gmask_token='[gMASK]',
padding_side="left",
**kwargs
) -> None:
super().__init__(
do_lower_case=do_lower_case,
remove_space=remove_space,
padding_side=padding_side,
**kwargs
)
self.do_lower_case = do_lower_case
self.remove_space = remove_space
self.vocab_file = vocab_file
self.bos_token = bos_token
self.eos_token = eos_token
self.eop_token = eop_token
self.mask_token = mask_token
self.gMASK_token = gmask_token
self.sp_tokenizer = SPTokenizer(vocab_file)
""" Initialisation """
@property
def eop_token_id(self) -> Optional[int]:
"""
`Optional[int]`: Id of the end of sentence token in the vocabulary. Returns `None` if the token has not been
set.
"""
if self.eop_token is None:
return None
return self.convert_tokens_to_ids(self.eop_token)
@property
def vocab_size(self):
""" Returns vocab size """
return self.sp_tokenizer.num_tokens
def get_vocab(self):
""" Returns vocab as a dict """
vocab = {self._convert_id_to_token(i): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def preprocess_text(self, inputs):
if self.remove_space:
outputs = " ".join(inputs.strip().split())
else:
outputs = inputs
if self.do_lower_case:
outputs = outputs.lower()
return outputs
def _tokenize(self, text, **kwargs):
""" Returns a tokenized string. """
text = self.preprocess_text(text)
seq = self.sp_tokenizer.tokenize(text)
return seq
def decode(
self,
token_ids: Union[List[int], List[List[int]]],
skip_special_tokens: bool = False,
clean_up_tokenization_spaces: bool = True,
spaces_between_special_tokens: bool = True,
**kwargs
) -> str:
if isinstance(token_ids[0], list):
tokens = []
for single_token_ids in token_ids:
if self.pad_token_id in single_token_ids: # remove pad
single_token_ids = list(filter((self.pad_token_id).__ne__, single_token_ids))
tokens.append(self.sp_tokenizer.decode(single_token_ids))
return (tokens)
else:
if self.pad_token_id in token_ids: # remove pad
token_ids = list(filter((self.pad_token_id).__ne__, token_ids))
return self.sp_tokenizer.decode(token_ids)
def _convert_token_to_id(self, token):
""" Converts a token (str) in an id using the vocab. """
return self.sp_tokenizer[token]
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
return self.sp_tokenizer[index]
def save_vocabulary(self, save_directory, filename_prefix=None):
"""
Save the vocabulary and special tokens file to a directory.
Args:
save_directory (`str`):
The directory in which to save the vocabulary.
filename_prefix (`str`, *optional*):
An optional prefix to add to the named of the saved files.
Returns:
`Tuple(str)`: Paths to the files saved.
"""
if os.path.isdir(save_directory):
vocab_file = os.path.join(
save_directory, VOCAB_FILES_NAMES["vocab_file"]
)
else:
vocab_file = save_directory
with open(self.vocab_file, 'rb') as fin:
proto_str = fin.read()
with open(vocab_file, "wb") as writer:
writer.write(proto_str)
return (vocab_file,)
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A BERT 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.
Returns:
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
if token_ids_1 is not None:
token_ids_0 += token_ids_1
mask_ids = self.sp_tokenizer[self.mask_token]
gmask_ids = self.sp_tokenizer[self.gMASK_token]
if mask_ids not in token_ids_0 and gmask_ids not in token_ids_0:
token_ids_0 += [gmask_ids]
if token_ids_0[-1] != mask_ids and token_ids_0[-1] != gmask_ids:
token_ids_0 += [self.sp_tokenizer[self.eos_token]]
token_ids_0 += [self.sp_tokenizer[self.bos_token]]
return token_ids_0
|