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+ }
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+ }
qwen.tiktoken ADDED
The diff for this file is too large to render. See raw diff
 
special_tokens_map.json ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ {
2
+ "bos_token": "<|endoftext|>",
3
+ "eos_token": "<|endoftext|>",
4
+ "unk_token": "<|endoftext|>"
5
+ }
tokenization_qwen.py ADDED
@@ -0,0 +1,230 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+ SPECIAL_TOKENS = (
31
+ ENDOFTEXT,
32
+ IMSTART,
33
+ IMEND,
34
+ ) + EXTRAS
35
+
36
+
37
+ def _load_tiktoken_bpe(tiktoken_bpe_file: str) -> Dict[bytes, int]:
38
+ with open(tiktoken_bpe_file, "rb") as f:
39
+ contents = f.read()
40
+ return {
41
+ base64.b64decode(token): int(rank)
42
+ for token, rank in (line.split() for line in contents.splitlines() if line)
43
+ }
44
+
45
+ class QWenTokenizer(PreTrainedTokenizer):
46
+ """QWen tokenizer."""
47
+
48
+ vocab_files_names = VOCAB_FILES_NAMES
49
+
50
+ def __init__(
51
+ self,
52
+ vocab_file,
53
+ errors="replace",
54
+ **kwargs,
55
+ ):
56
+ super().__init__(**kwargs)
57
+
58
+ self.errors = errors # how to handle errors in decoding
59
+
60
+ self.mergeable_ranks = _load_tiktoken_bpe(vocab_file) # type: dict[bytes, int]
61
+ self.special_tokens = {
62
+ token: index
63
+ for index, token in enumerate(
64
+ SPECIAL_TOKENS, start=len(self.mergeable_ranks)
65
+ )
66
+ }
67
+
68
+ enc = tiktoken.Encoding(
69
+ "Qwen",
70
+ pat_str=PAT_STR,
71
+ mergeable_ranks=self.mergeable_ranks,
72
+ special_tokens=self.special_tokens,
73
+ )
74
+ assert (
75
+ len(self.mergeable_ranks) + len(self.special_tokens) == enc.n_vocab
76
+ ), f"{len(self.mergeable_ranks) + len(self.special_tokens)} != {enc.n_vocab} in encoding"
77
+
78
+ self.decoder = {
79
+ v: k for k, v in self.mergeable_ranks.items()
80
+ } # type: dict[int, bytes|str]
81
+ self.decoder.update({v: k for k, v in self.special_tokens.items()})
82
+
83
+ self.tokenizer = enc # type: tiktoken.Encoding
84
+
85
+ self.eod_id = self.tokenizer.eot_token
86
+ self.im_start_id = self.special_tokens[IMSTART]
87
+ self.im_end_id = self.special_tokens[IMEND]
88
+
89
+ def __len__(self) -> int:
90
+ return self.tokenizer.n_vocab
91
+
92
+ def get_vocab(self) -> Dict[bytes, int]:
93
+ return self.mergeable_ranks
94
+
95
+ def convert_tokens_to_ids(
96
+ self, tokens: Union[bytes, str, List[Union[bytes, str]]]
97
+ ) -> List[int]:
98
+ ids = []
99
+ if isinstance(tokens, (str, bytes)):
100
+ if tokens in self.special_tokens:
101
+ return self.special_tokens[tokens]
102
+ else:
103
+ return self.mergeable_ranks.get(tokens)
104
+ for token in tokens:
105
+ if token in self.special_tokens:
106
+ ids.append(self.special_tokens[token])
107
+ else:
108
+ ids.append(self.mergeable_ranks.get(token))
109
+ return ids
110
+
111
+ def _add_tokens(self, new_tokens: Union[List[str], List[AddedToken]], special_tokens: bool = False) -> int:
112
+ if not special_tokens and new_tokens:
113
+ raise ValueError('Adding regular tokens is not supported')
114
+ for token in new_tokens:
115
+ surface_form = token.content if isinstance(token, AddedToken) else token
116
+ if surface_form not in SPECIAL_TOKENS:
117
+ raise ValueError('Adding unknown special tokens is not supported')
118
+ return 0
119
+
120
+ def save_vocabulary(self, save_directory: str, **kwargs) -> Tuple[str]:
121
+ """
122
+ Save only the vocabulary of the tokenizer (vocabulary).
123
+
124
+ Returns:
125
+ `Tuple(str)`: Paths to the files saved.
126
+ """
127
+ file_path = os.path.join(save_directory, "qwen.tiktoken")
128
+ with open(file_path, "w", encoding="utf8") as w:
129
+ for k, v in self.mergeable_ranks.items():
130
+ line = base64.b64encode(k).decode("utf8") + " " + str(v) + "\n"
131
+ w.write(line)
132
+ return (file_path,)
133
+
134
+ def tokenize(
135
+ self,
136
+ text: str,
137
+ allowed_special: Union[Set, str] = "all",
138
+ disallowed_special: Union[Collection, str] = (),
139
+ **kwargs,
140
+ ) -> List[Union[bytes, str]]:
141
+ """
142
+ Converts a string in a sequence of tokens.
143
+
144
+ Args:
145
+ text (`str`):
146
+ The sequence to be encoded.
147
+ allowed_special (`Literal["all"]` or `set`):
148
+ The surface forms of the tokens to be encoded as special tokens in regular texts.
149
+ Default to "all".
150
+ disallowed_special (`Literal["all"]` or `Collection`):
151
+ The surface forms of the tokens that should not be in regular texts and trigger errors.
152
+ Default to an empty tuple.
153
+
154
+ kwargs (additional keyword arguments, *optional*):
155
+ Will be passed to the underlying model specific encode method.
156
+
157
+ Returns:
158
+ `List[bytes|str]`: The list of tokens.
159
+ """
160
+
161
+
162
+ tokens = []
163
+ text = unicodedata.normalize("NFC", text)
164
+
165
+ # this implementation takes a detour: text -> token id -> token surface forms
166
+ for t in self.tokenizer.encode(
167
+ text, allowed_special=allowed_special, disallowed_special=disallowed_special
168
+ ):
169
+ tokens.append(self.decoder[t])
170
+ return tokens
171
+
172
+ def convert_tokens_to_string(self, tokens: List[Union[bytes, str]]) -> str:
173
+ """
174
+ Converts a sequence of tokens in a single string.
175
+ """
176
+ text = ""
177
+ temp = b""
178
+ for t in tokens:
179
+ if isinstance(t, str):
180
+ if temp:
181
+ text += temp.decode("utf-8", errors=self.errors)
182
+ temp = b""
183
+ text += t
184
+ elif isinstance(t, bytes):
185
+ temp += t
186
+ else:
187
+ raise TypeError("token should only be of type types or str")
188
+ if temp:
189
+ text += temp.decode("utf-8", errors=self.errors)
190
+ return text
191
+
192
+ @property
193
+ def vocab_size(self):
194
+ return self.tokenizer.n_vocab
195
+
196
+ def _convert_id_to_token(self, index: int) -> Union[bytes, str]:
197
+ """Converts an id to a token, special tokens included"""
198
+ if index in self.decoder:
199
+ return self.decoder[index]
200
+ raise ValueError("unknown ids")
201
+
202
+ def _convert_token_to_id(self, token: Union[bytes, str]) -> int:
203
+ """Converts a token to an id using the vocab, special tokens included"""
204
+ if token in self.special_tokens:
205
+ return self.special_tokens[token]
206
+ if token in self.mergeable_ranks:
207
+ return self.mergeable_ranks[token]
208
+ raise ValueError("unknown token")
209
+
210
+ def _tokenize(self, text: str, **kwargs):
211
+ """
212
+ Converts a string in a sequence of tokens (string), using the tokenizer. Split in words for word-based
213
+ vocabulary or sub-words for sub-word-based vocabularies (BPE/SentencePieces/WordPieces).
214
+
215
+ Do NOT take care of added tokens.
216
+ """
217
+ raise NotImplementedError
218
+
219
+ def _decode(
220
+ self,
221
+ token_ids: Union[int, List[int]],
222
+ skip_special_tokens: bool = False,
223
+ errors: str = None,
224
+ **kwargs,
225
+ ) -> str:
226
+ if isinstance(token_ids, int):
227
+ token_ids = [token_ids]
228
+ if skip_special_tokens:
229
+ token_ids = [i for i in token_ids if i < self.eod_id]
230
+ return self.tokenizer.decode(token_ids, errors=errors or self.errors)
tokenizer_config.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "model_max_length": 999999999999999999,
3
+ "tokenizer_class": "QWenTokenizer",
4
+ "auto_map": {
5
+ "AutoTokenizer": [
6
+ "tokenization_qwen.QWenTokenizer",
7
+ null
8
+ ]
9
+ }
10
+ }