Upload tokenizer
Browse files- qwen.tiktoken +0 -0
- special_tokens_map.json +4 -0
- tokenization_qwen.py +276 -0
- tokenizer_config.json +16 -0
qwen.tiktoken
ADDED
The diff for this file is too large to render.
See raw diff
|
|
special_tokens_map.json
ADDED
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"eos_token": "<|im_end|>",
|
3 |
+
"pad_token": "<|endoftext|>"
|
4 |
+
}
|
tokenization_qwen.py
ADDED
@@ -0,0 +1,276 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Copyright (c) Alibaba Cloud.
|
2 |
+
#
|
3 |
+
# This source code is licensed under the license found in the
|
4 |
+
# LICENSE file in the root directory of this source tree.
|
5 |
+
|
6 |
+
"""Tokenization classes for QWen."""
|
7 |
+
|
8 |
+
import base64
|
9 |
+
import logging
|
10 |
+
import os
|
11 |
+
import unicodedata
|
12 |
+
from typing import Collection, Dict, List, Set, Tuple, Union
|
13 |
+
|
14 |
+
import tiktoken
|
15 |
+
from transformers import PreTrainedTokenizer, AddedToken
|
16 |
+
|
17 |
+
logger = logging.getLogger(__name__)
|
18 |
+
|
19 |
+
|
20 |
+
VOCAB_FILES_NAMES = {"vocab_file": "qwen.tiktoken"}
|
21 |
+
|
22 |
+
PAT_STR = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"""
|
23 |
+
ENDOFTEXT = "<|endoftext|>"
|
24 |
+
IMSTART = "<|im_start|>"
|
25 |
+
IMEND = "<|im_end|>"
|
26 |
+
# as the default behavior is changed to allow special tokens in
|
27 |
+
# regular texts, the surface forms of special tokens need to be
|
28 |
+
# as different as possible to minimize the impact
|
29 |
+
EXTRAS = tuple((f"<|extra_{i}|>" for i in range(205)))
|
30 |
+
# changed to use actual index to avoid misconfiguration with vocabulary expansion
|
31 |
+
SPECIAL_START_ID = 151643
|
32 |
+
SPECIAL_TOKENS = tuple(
|
33 |
+
enumerate(
|
34 |
+
(
|
35 |
+
(
|
36 |
+
ENDOFTEXT,
|
37 |
+
IMSTART,
|
38 |
+
IMEND,
|
39 |
+
)
|
40 |
+
+ EXTRAS
|
41 |
+
),
|
42 |
+
start=SPECIAL_START_ID,
|
43 |
+
)
|
44 |
+
)
|
45 |
+
SPECIAL_TOKENS_SET = set(t for i, t in SPECIAL_TOKENS)
|
46 |
+
|
47 |
+
|
48 |
+
def _load_tiktoken_bpe(tiktoken_bpe_file: str) -> Dict[bytes, int]:
|
49 |
+
with open(tiktoken_bpe_file, "rb") as f:
|
50 |
+
contents = f.read()
|
51 |
+
return {
|
52 |
+
base64.b64decode(token): int(rank)
|
53 |
+
for token, rank in (line.split() for line in contents.splitlines() if line)
|
54 |
+
}
|
55 |
+
|
56 |
+
|
57 |
+
class QWenTokenizer(PreTrainedTokenizer):
|
58 |
+
"""QWen tokenizer."""
|
59 |
+
|
60 |
+
vocab_files_names = VOCAB_FILES_NAMES
|
61 |
+
|
62 |
+
def __init__(
|
63 |
+
self,
|
64 |
+
vocab_file,
|
65 |
+
errors="replace",
|
66 |
+
extra_vocab_file=None,
|
67 |
+
**kwargs,
|
68 |
+
):
|
69 |
+
super().__init__(**kwargs)
|
70 |
+
|
71 |
+
# how to handle errors in decoding UTF-8 byte sequences
|
72 |
+
# use ignore if you are in streaming inference
|
73 |
+
self.errors = errors
|
74 |
+
|
75 |
+
self.mergeable_ranks = _load_tiktoken_bpe(vocab_file) # type: Dict[bytes, int]
|
76 |
+
self.special_tokens = {
|
77 |
+
token: index
|
78 |
+
for index, token in SPECIAL_TOKENS
|
79 |
+
}
|
80 |
+
|
81 |
+
# try load extra vocab from file
|
82 |
+
if extra_vocab_file is not None:
|
83 |
+
used_ids = set(self.mergeable_ranks.values()) | set(self.special_tokens.values())
|
84 |
+
extra_mergeable_ranks = _load_tiktoken_bpe(extra_vocab_file)
|
85 |
+
for token, index in extra_mergeable_ranks.items():
|
86 |
+
if token in self.mergeable_ranks:
|
87 |
+
logger.info(f"extra token {token} exists, skipping")
|
88 |
+
continue
|
89 |
+
if index in used_ids:
|
90 |
+
logger.info(f'the index {index} for extra token {token} exists, skipping')
|
91 |
+
continue
|
92 |
+
self.mergeable_ranks[token] = index
|
93 |
+
# the index may be sparse after this, but don't worry tiktoken.Encoding will handle this
|
94 |
+
|
95 |
+
enc = tiktoken.Encoding(
|
96 |
+
"Qwen",
|
97 |
+
pat_str=PAT_STR,
|
98 |
+
mergeable_ranks=self.mergeable_ranks,
|
99 |
+
special_tokens=self.special_tokens,
|
100 |
+
)
|
101 |
+
assert (
|
102 |
+
len(self.mergeable_ranks) + len(self.special_tokens) == enc.n_vocab
|
103 |
+
), f"{len(self.mergeable_ranks) + len(self.special_tokens)} != {enc.n_vocab} in encoding"
|
104 |
+
|
105 |
+
self.decoder = {
|
106 |
+
v: k for k, v in self.mergeable_ranks.items()
|
107 |
+
} # type: dict[int, bytes|str]
|
108 |
+
self.decoder.update({v: k for k, v in self.special_tokens.items()})
|
109 |
+
|
110 |
+
self.tokenizer = enc # type: tiktoken.Encoding
|
111 |
+
|
112 |
+
self.eod_id = self.tokenizer.eot_token
|
113 |
+
self.im_start_id = self.special_tokens[IMSTART]
|
114 |
+
self.im_end_id = self.special_tokens[IMEND]
|
115 |
+
|
116 |
+
def __getstate__(self):
|
117 |
+
# for pickle lovers
|
118 |
+
state = self.__dict__.copy()
|
119 |
+
del state["tokenizer"]
|
120 |
+
return state
|
121 |
+
|
122 |
+
def __setstate__(self, state):
|
123 |
+
# tokenizer is not python native; don't pass it; rebuild it
|
124 |
+
self.__dict__.update(state)
|
125 |
+
enc = tiktoken.Encoding(
|
126 |
+
"Qwen",
|
127 |
+
pat_str=PAT_STR,
|
128 |
+
mergeable_ranks=self.mergeable_ranks,
|
129 |
+
special_tokens=self.special_tokens,
|
130 |
+
)
|
131 |
+
self.tokenizer = enc
|
132 |
+
|
133 |
+
def __len__(self) -> int:
|
134 |
+
return self.tokenizer.n_vocab
|
135 |
+
|
136 |
+
def get_vocab(self) -> Dict[bytes, int]:
|
137 |
+
return self.mergeable_ranks
|
138 |
+
|
139 |
+
def convert_tokens_to_ids(
|
140 |
+
self, tokens: Union[bytes, str, List[Union[bytes, str]]]
|
141 |
+
) -> List[int]:
|
142 |
+
ids = []
|
143 |
+
if isinstance(tokens, (str, bytes)):
|
144 |
+
if tokens in self.special_tokens:
|
145 |
+
return self.special_tokens[tokens]
|
146 |
+
else:
|
147 |
+
return self.mergeable_ranks.get(tokens)
|
148 |
+
for token in tokens:
|
149 |
+
if token in self.special_tokens:
|
150 |
+
ids.append(self.special_tokens[token])
|
151 |
+
else:
|
152 |
+
ids.append(self.mergeable_ranks.get(token))
|
153 |
+
return ids
|
154 |
+
|
155 |
+
def _add_tokens(
|
156 |
+
self,
|
157 |
+
new_tokens: Union[List[str], List[AddedToken]],
|
158 |
+
special_tokens: bool = False,
|
159 |
+
) -> int:
|
160 |
+
if not special_tokens and new_tokens:
|
161 |
+
raise ValueError("Adding regular tokens is not supported")
|
162 |
+
for token in new_tokens:
|
163 |
+
surface_form = token.content if isinstance(token, AddedToken) else token
|
164 |
+
if surface_form not in SPECIAL_TOKENS_SET:
|
165 |
+
raise ValueError("Adding unknown special tokens is not supported")
|
166 |
+
return 0
|
167 |
+
|
168 |
+
def save_vocabulary(self, save_directory: str, **kwargs) -> Tuple[str]:
|
169 |
+
"""
|
170 |
+
Save only the vocabulary of the tokenizer (vocabulary).
|
171 |
+
|
172 |
+
Returns:
|
173 |
+
`Tuple(str)`: Paths to the files saved.
|
174 |
+
"""
|
175 |
+
file_path = os.path.join(save_directory, "qwen.tiktoken")
|
176 |
+
with open(file_path, "w", encoding="utf8") as w:
|
177 |
+
for k, v in self.mergeable_ranks.items():
|
178 |
+
line = base64.b64encode(k).decode("utf8") + " " + str(v) + "\n"
|
179 |
+
w.write(line)
|
180 |
+
return (file_path,)
|
181 |
+
|
182 |
+
def tokenize(
|
183 |
+
self,
|
184 |
+
text: str,
|
185 |
+
allowed_special: Union[Set, str] = "all",
|
186 |
+
disallowed_special: Union[Collection, str] = (),
|
187 |
+
**kwargs,
|
188 |
+
) -> List[Union[bytes, str]]:
|
189 |
+
"""
|
190 |
+
Converts a string in a sequence of tokens.
|
191 |
+
|
192 |
+
Args:
|
193 |
+
text (`str`):
|
194 |
+
The sequence to be encoded.
|
195 |
+
allowed_special (`Literal["all"]` or `set`):
|
196 |
+
The surface forms of the tokens to be encoded as special tokens in regular texts.
|
197 |
+
Default to "all".
|
198 |
+
disallowed_special (`Literal["all"]` or `Collection`):
|
199 |
+
The surface forms of the tokens that should not be in regular texts and trigger errors.
|
200 |
+
Default to an empty tuple.
|
201 |
+
|
202 |
+
kwargs (additional keyword arguments, *optional*):
|
203 |
+
Will be passed to the underlying model specific encode method.
|
204 |
+
|
205 |
+
Returns:
|
206 |
+
`List[bytes|str]`: The list of tokens.
|
207 |
+
"""
|
208 |
+
tokens = []
|
209 |
+
text = unicodedata.normalize("NFC", text)
|
210 |
+
|
211 |
+
# this implementation takes a detour: text -> token id -> token surface forms
|
212 |
+
for t in self.tokenizer.encode(
|
213 |
+
text, allowed_special=allowed_special, disallowed_special=disallowed_special
|
214 |
+
):
|
215 |
+
tokens.append(self.decoder[t])
|
216 |
+
return tokens
|
217 |
+
|
218 |
+
def convert_tokens_to_string(self, tokens: List[Union[bytes, str]]) -> str:
|
219 |
+
"""
|
220 |
+
Converts a sequence of tokens in a single string.
|
221 |
+
"""
|
222 |
+
text = ""
|
223 |
+
temp = b""
|
224 |
+
for t in tokens:
|
225 |
+
if isinstance(t, str):
|
226 |
+
if temp:
|
227 |
+
text += temp.decode("utf-8", errors=self.errors)
|
228 |
+
temp = b""
|
229 |
+
text += t
|
230 |
+
elif isinstance(t, bytes):
|
231 |
+
temp += t
|
232 |
+
else:
|
233 |
+
raise TypeError("token should only be of type types or str")
|
234 |
+
if temp:
|
235 |
+
text += temp.decode("utf-8", errors=self.errors)
|
236 |
+
return text
|
237 |
+
|
238 |
+
@property
|
239 |
+
def vocab_size(self):
|
240 |
+
return self.tokenizer.n_vocab
|
241 |
+
|
242 |
+
def _convert_id_to_token(self, index: int) -> Union[bytes, str]:
|
243 |
+
"""Converts an id to a token, special tokens included"""
|
244 |
+
if index in self.decoder:
|
245 |
+
return self.decoder[index]
|
246 |
+
raise ValueError("unknown ids")
|
247 |
+
|
248 |
+
def _convert_token_to_id(self, token: Union[bytes, str]) -> int:
|
249 |
+
"""Converts a token to an id using the vocab, special tokens included"""
|
250 |
+
if token in self.special_tokens:
|
251 |
+
return self.special_tokens[token]
|
252 |
+
if token in self.mergeable_ranks:
|
253 |
+
return self.mergeable_ranks[token]
|
254 |
+
raise ValueError("unknown token")
|
255 |
+
|
256 |
+
def _tokenize(self, text: str, **kwargs):
|
257 |
+
"""
|
258 |
+
Converts a string in a sequence of tokens (string), using the tokenizer. Split in words for word-based
|
259 |
+
vocabulary or sub-words for sub-word-based vocabularies (BPE/SentencePieces/WordPieces).
|
260 |
+
|
261 |
+
Do NOT take care of added tokens.
|
262 |
+
"""
|
263 |
+
raise NotImplementedError
|
264 |
+
|
265 |
+
def _decode(
|
266 |
+
self,
|
267 |
+
token_ids: Union[int, List[int]],
|
268 |
+
skip_special_tokens: bool = False,
|
269 |
+
errors: str = None,
|
270 |
+
**kwargs,
|
271 |
+
) -> str:
|
272 |
+
if isinstance(token_ids, int):
|
273 |
+
token_ids = [token_ids]
|
274 |
+
if skip_special_tokens:
|
275 |
+
token_ids = [i for i in token_ids if i < self.eod_id]
|
276 |
+
return self.tokenizer.decode(token_ids, errors=errors or self.errors)
|
tokenizer_config.json
ADDED
@@ -0,0 +1,16 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"added_tokens_decoder": {},
|
3 |
+
"auto_map": {
|
4 |
+
"AutoTokenizer": [
|
5 |
+
"tokenization_qwen.QWenTokenizer",
|
6 |
+
null
|
7 |
+
]
|
8 |
+
},
|
9 |
+
"clean_up_tokenization_spaces": true,
|
10 |
+
"eos_token": "<|im_end|>",
|
11 |
+
"model_max_length": 32768,
|
12 |
+
"pad_token": "<|endoftext|>",
|
13 |
+
"padding_side": "right",
|
14 |
+
"split_special_tokens": false,
|
15 |
+
"tokenizer_class": "QWenTokenizer"
|
16 |
+
}
|