# Copyright 2023 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
""" Bloom Tokenzier """
import json
import os
from functools import lru_cache
import regex as re
from mindformers.models.base_tokenizer import Tokenizer
from mindformers.tools.register import MindFormerRegister, MindFormerModuleType
from mindformers.tools.logger import logger
from mindformers.mindformer_book import MindFormerBook
__all__ = ['BloomTokenizer']
@lru_cache()
def bytes_to_unicode():
"""
bytes to unicode
"""
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(i) for i in cs]
return dict(zip(bs, cs))
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
[文档]@MindFormerRegister.register(MindFormerModuleType.TOKENIZER)
class BloomTokenizer(Tokenizer):
r"""
Tokenize the input string and convert them into the ids. The tokenizer use the sentence piece internally.
Args:
vocab_file(str): The vocabulary file path.
unk_token(str): The token that represents the unknown. Default "<|unk|>".
bos_token(str): The token that represents the begin-of-sentence. Default "<|s|>"".
eos_token(str): The token that represents the end-of-sentence. Default "<|/s|>".
pad_token(str): The token that represents the pad. Default "<|pad|>".
add_prefix_space(bool): whether to add a whitespace in the front of text. Default "False"
**kwargs: Other kwargs that will be passed into the base class of the `Tokenizer`.
Examples:
>>> from mindformers import BloomTokenizer
>>> tokenizer = BloomTokenizer.from_pretrained("bloom_560m")
>>> res = tokenizer("Hello world", add_special_tokens=False)
>>> print(res)
{'input_ids': [59414, 8876], 'token_type_ids': [0, 0], 'attention_mask': [1, 1]}
Outputs:
A dict contains the processed ids, attention_mask that specific by the member `MODEL_INPUT_NAME`
of the subclass.
"""
VOCAB_FILES = {'vocab_file': 'tokenizer.json'}
FILE_LIST = ['tokenizer_config.json']
_support_list = MindFormerBook.get_tokenizer_support_list()['bloom']
def __init__(
self,
vocab_file,
unk_token="<|unk|>",
bos_token="<|s|>",
eos_token="<|/s|>",
pad_token="<|pad|>",
add_prefix_space=False, **kwargs):
super(BloomTokenizer, self).__init__(
unk_token=unk_token, bos_token=bos_token,
eos_token=eos_token, pad_token=pad_token, **kwargs)
with open(vocab_file, 'r', encoding="utf-8") as vocab_handle:
self.encoder = json.load(vocab_handle)["model"]["vocab"]
self.decoder = {v: k for k, v in self.encoder.items()}
with open(vocab_file, 'r', encoding="utf-8") as vocab_handle:
bpe_merges = json.load(vocab_handle)["model"]["merges"]
self.the_unk_token = unk_token
bpe_merges = [tuple(merge.split()) for merge in bpe_merges]
self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
self.byte_encoder = bytes_to_unicode()
self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
self.add_prefix_space = add_prefix_space
self.cache = {}
self._unk_token_id = 0
self._bos_token_id = 1
self._eos_token_id = 2
self._pad_token_id = 3
[文档] def bpe(self, token):
""" bpe encode """
if token in self.cache:
return self.cache[token]
word = tuple(token)
pairs = get_pairs(token)
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 + 1 < len(word) 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
def tokenize(self, text):
if not isinstance(text, str):
raise ValueError("Text should be type str, but found type", type(text))
return self._tokenize(text)
def _tokenize(self, text, **kwargs):
""" Tokenize a string using bpe encode. """
text = self.prepare_for_tokenization(text, is_pretokenized=False)
bpe_tokens = []
for token in re.findall(self.pat, text):
token = "".join(
self.byte_encoder[b] for b in token.encode("utf-8")
)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" "))
return bpe_tokens
def _convert_tokens_to_ids(self, tokens):
""" the index of the tokens in the vocabulary. """
if isinstance(tokens, str):
return self.encoder.get(tokens, self.encoder.get(self.the_unk_token))
output = []
for token in tokens:
output.append(self.encoder.get(token, self.encoder.get(self.the_unk_token)))
return output
def _convert_ids_to_tokens(self, ids):
""" return the origin bpe tokens according to ids """
if isinstance(ids, int):
return self.decoder.get(ids)
if isinstance(ids, list):
output = []
for item in ids:
output.append(self.decoder.get(item))
return output
raise TypeError(f"The type of ids should be int or list, but found {type(ids)}.")
def _convert_tokens_to_string(self, tokens):
""" return a string according to the list of tokens"""
text = "".join(tokens)
text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors='ignore')
return text
[文档] def convert_tokens_to_string(self, tokens):
"""Convert the tokens to the string"""
return self._convert_tokens_to_string(tokens)
[文档] def prepare_for_tokenization(self, text, is_pretokenized=False, **kwargs):
""" whether to add a whitespace in the front of text """
add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space)
if is_pretokenized or add_prefix_space:
text = " " + text
return text
[文档] def save_vocabulary(self, save_directory, filename_prefix):
"""write the word to the files"""
if filename_prefix.endswith("json"):
vocab_file = os.path.join(save_directory, filename_prefix)
with open(vocab_file, "w", encoding="utf-8") as f:
f.write(json.dumps(self.encoder))
else:
vocab_file = os.path.join(save_directory, filename_prefix)
index = 0
with open(vocab_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(
"Saving vocabulary to %s: BPE merge indices are not consecutive."
" Please check that the tokenizer is not corrupted!", vocab_file
)
index = token_index
writer.write(" ".join(bpe_tokens) + "\n")
index += 1
return vocab_file
@property
def vocab_size(self):
"""Get the vocab size of the """
return len(self.decoder)
@property
def unk_token_id(self):
return self._unk_token_id
@property
def bos_token_id(self):
return self._bos_token_id
@property
def eos_token_id(self):
return self._eos_token_id
@property
def pad_token_id(self):
return self._pad_token_id