340 lines
14 KiB
Python
340 lines
14 KiB
Python
|
# coding=utf-8
|
||
|
|
# Copyright 2024 The Qwen team, Alibaba Group and The HuggingFace Inc. team. All rights reserved.
|
||
|
|
#
|
||
|
|
# 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 classes for Qwen2."""
|
||
|
|
|
||
|
|
import json
|
||
|
|
import os
|
||
|
|
import unicodedata
|
||
|
|
from functools import lru_cache
|
||
|
|
from typing import Optional, Tuple
|
||
|
|
|
||
|
|
import regex as re
|
||
|
|
|
||
|
|
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
|
||
|
|
from ...utils import logging
|
||
|
|
|
||
|
|
|
||
|
|
logger = logging.get_logger(__name__)
|
||
|
|
|
||
|
|
VOCAB_FILES_NAMES = {
|
||
|
|
"vocab_file": "vocab.json",
|
||
|
|
"merges_file": "merges.txt",
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
MAX_MODEL_INPUT_SIZES = {"qwen/qwen-tokenizer": 32768}
|
||
|
|
|
||
|
|
PRETOKENIZE_REGEX = 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+"""
|
||
|
|
|
||
|
|
|
||
|
|
@lru_cache()
|
||
|
|
# Copied from transformers.models.gpt2.tokenization_gpt2.bytes_to_unicode
|
||
|
|
def bytes_to_unicode():
|
||
|
|
"""
|
||
|
|
Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control
|
||
|
|
characters the bpe code barfs on.
|
||
|
|
|
||
|
|
The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab
|
||
|
|
if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for
|
||
|
|
decent coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup
|
||
|
|
tables between utf-8 bytes and unicode strings.
|
||
|
|
"""
|
||
|
|
bs = (
|
||
|
|
list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1))
|
||
|
|
)
|
||
|
|
cs = bs[:]
|
||
|
|
n = 0
|
||
|
|
for b in range(2**8):
|
||
|
|
if b not in bs:
|
||
|
|
bs.append(b)
|
||
|
|
cs.append(2**8 + n)
|
||
|
|
n += 1
|
||
|
|
cs = [chr(n) for n in cs]
|
||
|
|
return dict(zip(bs, cs))
|
||
|
|
|
||
|
|
|
||
|
|
# Copied from transformers.models.gpt2.tokenization_gpt2.get_pairs
|
||
|
|
def get_pairs(word):
|
||
|
|
"""
|
||
|
|
Return set of symbol pairs in a word.
|
||
|
|
|
||
|
|
Word is represented as tuple of symbols (symbols being variable-length strings).
|
||
|
|
"""
|
||
|
|
pairs = set()
|
||
|
|
prev_char = word[0]
|
||
|
|
for char in word[1:]:
|
||
|
|
pairs.add((prev_char, char))
|
||
|
|
prev_char = char
|
||
|
|
return pairs
|
||
|
|
|
||
|
|
|
||
|
|
class Qwen2Tokenizer(PreTrainedTokenizer):
|
||
|
|
"""
|
||
|
|
Construct a Qwen2 tokenizer. Based on byte-level Byte-Pair-Encoding.
|
||
|
|
|
||
|
|
Same with GPT2Tokenizer, this tokenizer has been trained to treat spaces like parts of the tokens so a word will
|
||
|
|
be encoded differently whether it is at the beginning of the sentence (without space) or not:
|
||
|
|
|
||
|
|
```python
|
||
|
|
>>> from transformers import Qwen2Tokenizer
|
||
|
|
|
||
|
|
>>> tokenizer = Qwen2Tokenizer.from_pretrained("Qwen/Qwen-tokenizer")
|
||
|
|
>>> tokenizer("Hello world")["input_ids"]
|
||
|
|
[9707, 1879]
|
||
|
|
|
||
|
|
>>> tokenizer(" Hello world")["input_ids"]
|
||
|
|
[21927, 1879]
|
||
|
|
```
|
||
|
|
This is expected.
|
||
|
|
|
||
|
|
You should not use GPT2Tokenizer instead, because of the different pretokenization rules.
|
||
|
|
|
||
|
|
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
|
||
|
|
this superclass for more information regarding those methods.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
vocab_file (`str`):
|
||
|
|
Path to the vocabulary file.
|
||
|
|
merges_file (`str`):
|
||
|
|
Path to the merges file.
|
||
|
|
errors (`str`, *optional*, defaults to `"replace"`):
|
||
|
|
Paradigm to follow when decoding bytes to UTF-8. See
|
||
|
|
[bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
|
||
|
|
unk_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
|
||
|
|
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.
|
||
|
|
bos_token (`str`, *optional*):
|
||
|
|
The beginning of sequence token. Not applicable for this tokenizer.
|
||
|
|
eos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
|
||
|
|
The end of sequence token.
|
||
|
|
pad_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
|
||
|
|
The token used for padding, for example when batching sequences of different lengths.
|
||
|
|
clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
|
||
|
|
Whether or not the model should cleanup the spaces that were added when splitting the input text during the
|
||
|
|
tokenization process. Not applicable to this tokenizer, since tokenization does not add spaces.
|
||
|
|
split_special_tokens (`bool`, *optional*, defaults to `False`):
|
||
|
|
Whether or not the special tokens should be split during the tokenization process. The default behavior is
|
||
|
|
to not split special tokens. This means that if `<|endoftext|>` is the `eos_token`, then `tokenizer.tokenize("<|endoftext|>") =
|
||
|
|
['<|endoftext|>`]. Otherwise, if `split_special_tokens=True`, then `tokenizer.tokenize("<|endoftext|>")` will be give `['<',
|
||
|
|
'|', 'endo', 'ft', 'ext', '|', '>']`. This argument is only supported for `slow` tokenizers for the moment.
|
||
|
|
"""
|
||
|
|
|
||
|
|
vocab_files_names = VOCAB_FILES_NAMES
|
||
|
|
model_input_names = ["input_ids", "attention_mask"]
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
vocab_file,
|
||
|
|
merges_file,
|
||
|
|
errors="replace",
|
||
|
|
unk_token="<|endoftext|>",
|
||
|
|
bos_token=None,
|
||
|
|
eos_token="<|endoftext|>",
|
||
|
|
pad_token="<|endoftext|>",
|
||
|
|
clean_up_tokenization_spaces=False,
|
||
|
|
split_special_tokens=False,
|
||
|
|
**kwargs,
|
||
|
|
):
|
||
|
|
# Qwen vocab does not contain control tokens; added tokens need to be special
|
||
|
|
bos_token = (
|
||
|
|
AddedToken(bos_token, lstrip=False, rstrip=False, special=True, normalized=False)
|
||
|
|
if isinstance(bos_token, str)
|
||
|
|
else bos_token
|
||
|
|
)
|
||
|
|
eos_token = (
|
||
|
|
AddedToken(eos_token, lstrip=False, rstrip=False, special=True, normalized=False)
|
||
|
|
if isinstance(eos_token, str)
|
||
|
|
else eos_token
|
||
|
|
)
|
||
|
|
unk_token = (
|
||
|
|
AddedToken(unk_token, lstrip=False, rstrip=False, special=True, normalized=False)
|
||
|
|
if isinstance(unk_token, str)
|
||
|
|
else unk_token
|
||
|
|
)
|
||
|
|
pad_token = (
|
||
|
|
AddedToken(pad_token, lstrip=False, rstrip=False, special=True, normalized=False)
|
||
|
|
if isinstance(pad_token, str)
|
||
|
|
else pad_token
|
||
|
|
)
|
||
|
|
|
||
|
|
with open(vocab_file, encoding="utf-8") as vocab_handle:
|
||
|
|
self.encoder = json.load(vocab_handle)
|
||
|
|
self.decoder = {v: k for k, v in self.encoder.items()}
|
||
|
|
self.errors = errors # how to handle errors in decoding
|
||
|
|
self.byte_encoder = bytes_to_unicode()
|
||
|
|
self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
|
||
|
|
bpe_merges = []
|
||
|
|
with open(merges_file, encoding="utf-8") as merges_handle:
|
||
|
|
for i, line in enumerate(merges_handle):
|
||
|
|
line = line.strip()
|
||
|
|
if (i == 0 and line.startswith("#version:")) or not line:
|
||
|
|
continue
|
||
|
|
bpe_merges.append(tuple(line.split()))
|
||
|
|
self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
|
||
|
|
# NOTE: the cache can grow without bound and will get really large for long running processes
|
||
|
|
# (esp. for texts of language that do not use space between word, e.g. Chinese); technically
|
||
|
|
# not a memory leak but appears as one.
|
||
|
|
# GPT2Tokenizer has the same problem, so let's be consistent.
|
||
|
|
self.cache = {}
|
||
|
|
|
||
|
|
self.pat = re.compile(PRETOKENIZE_REGEX)
|
||
|
|
|
||
|
|
if kwargs.get("add_prefix_space", False):
|
||
|
|
logger.warning_once(
|
||
|
|
f"{self.__class__.__name} does not support `add_prefix_space`, setting it to True has no effect."
|
||
|
|
)
|
||
|
|
|
||
|
|
super().__init__(
|
||
|
|
errors=errors,
|
||
|
|
bos_token=bos_token,
|
||
|
|
eos_token=eos_token,
|
||
|
|
pad_token=pad_token,
|
||
|
|
unk_token=unk_token,
|
||
|
|
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
|
||
|
|
split_special_tokens=split_special_tokens,
|
||
|
|
**kwargs,
|
||
|
|
)
|
||
|
|
|
||
|
|
@property
|
||
|
|
def vocab_size(self) -> int:
|
||
|
|
return len(self.encoder)
|
||
|
|
|
||
|
|
# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.get_vocab
|
||
|
|
def get_vocab(self):
|
||
|
|
return dict(self.encoder, **self.added_tokens_encoder)
|
||
|
|
|
||
|
|
# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.bpe
|
||
|
|
def bpe(self, token):
|
||
|
|
if token in self.cache:
|
||
|
|
return self.cache[token]
|
||
|
|
word = tuple(token)
|
||
|
|
pairs = get_pairs(word)
|
||
|
|
|
||
|
|
if not pairs:
|
||
|
|
return token
|
||
|
|
|
||
|
|
while True:
|
||
|
|
bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
|
||
|
|
if bigram not in self.bpe_ranks:
|
||
|
|
break
|
||
|
|
first, second = bigram
|
||
|
|
new_word = []
|
||
|
|
i = 0
|
||
|
|
while i < len(word):
|
||
|
|
try:
|
||
|
|
j = word.index(first, i)
|
||
|
|
except ValueError:
|
||
|
|
new_word.extend(word[i:])
|
||
|
|
break
|
||
|
|
else:
|
||
|
|
new_word.extend(word[i:j])
|
||
|
|
i = j
|
||
|
|
|
||
|
|
if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
|
||
|
|
new_word.append(first + second)
|
||
|
|
i += 2
|
||
|
|
else:
|
||
|
|
new_word.append(word[i])
|
||
|
|
i += 1
|
||
|
|
new_word = tuple(new_word)
|
||
|
|
word = new_word
|
||
|
|
if len(word) == 1:
|
||
|
|
break
|
||
|
|
else:
|
||
|
|
pairs = get_pairs(word)
|
||
|
|
word = " ".join(word)
|
||
|
|
self.cache[token] = word
|
||
|
|
return word
|
||
|
|
|
||
|
|
# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer._tokenize
|
||
|
|
def _tokenize(self, text):
|
||
|
|
"""Tokenize a string."""
|
||
|
|
bpe_tokens = []
|
||
|
|
for token in re.findall(self.pat, text):
|
||
|
|
token = "".join(
|
||
|
|
self.byte_encoder[b] for b in token.encode("utf-8")
|
||
|
|
) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
|
||
|
|
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" "))
|
||
|
|
return bpe_tokens
|
||
|
|
|
||
|
|
# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer._convert_token_to_id
|
||
|
|
def _convert_token_to_id(self, token):
|
||
|
|
"""Converts a token (str) in an id using the vocab."""
|
||
|
|
return self.encoder.get(token, self.encoder.get(self.unk_token))
|
||
|
|
|
||
|
|
# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer._convert_id_to_token
|
||
|
|
def _convert_id_to_token(self, index):
|
||
|
|
"""Converts an index (integer) in a token (str) using the vocab."""
|
||
|
|
return self.decoder.get(index)
|
||
|
|
|
||
|
|
# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.convert_tokens_to_string
|
||
|
|
def convert_tokens_to_string(self, tokens):
|
||
|
|
"""Converts a sequence of tokens (string) in a single string."""
|
||
|
|
text = "".join(tokens)
|
||
|
|
text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
|
||
|
|
return text
|
||
|
|
|
||
|
|
def decode(
|
||
|
|
self,
|
||
|
|
token_ids,
|
||
|
|
skip_special_tokens: bool = False,
|
||
|
|
clean_up_tokenization_spaces: Optional[bool] = False,
|
||
|
|
spaces_between_special_tokens: bool = False,
|
||
|
|
**kwargs,
|
||
|
|
) -> str:
|
||
|
|
# `spaces_between_special_tokens` defaults to True for _decode in slow tokenizers
|
||
|
|
# and cannot be configured elsewhere, but it should default to False for Qwen2Tokenizer
|
||
|
|
return super().decode(
|
||
|
|
token_ids,
|
||
|
|
skip_special_tokens=skip_special_tokens,
|
||
|
|
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
|
||
|
|
spaces_between_special_tokens=spaces_between_special_tokens,
|
||
|
|
**kwargs,
|
||
|
|
)
|
||
|
|
|
||
|
|
# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.save_vocabulary
|
||
|
|
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
|
||
|
|
if not os.path.isdir(save_directory):
|
||
|
|
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
|
||
|
|
return
|
||
|
|
vocab_file = os.path.join(
|
||
|
|
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
|
||
|
|
)
|
||
|
|
merge_file = os.path.join(
|
||
|
|
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
|
||
|
|
)
|
||
|
|
|
||
|
|
with open(vocab_file, "w", encoding="utf-8") as f:
|
||
|
|
f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
|
||
|
|
|
||
|
|
index = 0
|
||
|
|
with open(merge_file, "w", encoding="utf-8") as writer:
|
||
|
|
writer.write("#version: 0.2\n")
|
||
|
|
for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
|
||
|
|
if index != token_index:
|
||
|
|
logger.warning(
|
||
|
|
f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
|
||
|
|
" Please check that the tokenizer is not corrupted!"
|
||
|
|
)
|
||
|
|
index = token_index
|
||
|
|
writer.write(" ".join(bpe_tokens) + "\n")
|
||
|
|
index += 1
|
||
|
|
|
||
|
|
return vocab_file, merge_file
|
||
|
|
|
||
|
|
def prepare_for_tokenization(self, text, **kwargs):
|
||
|
|
text = unicodedata.normalize("NFC", text)
|
||
|
|
return (text, kwargs)
|