# Copyright (c) 2021 PaddlePaddle Authors. 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.
import os
from functools import lru_cache
from collections import namedtuple
import json
import jieba
import shutil
from paddle.utils import try_import
from .. import PretrainedTokenizer
from ..tokenizer_utils import convert_to_unicode, whitespace_tokenize,\
_is_whitespace, _is_control, _is_punctuation
__all__ = [
'GPT2Tokenizer',
'GPT2ChineseTokenizer',
]
COMMAND_TUPLE = namedtuple('CommandToken', ('name', 'token', 'Id'))
TYPE_TUPLE = namedtuple('TypeToken', ('name', 'token', 'Id'))
class CommandToken(object):
def __init__(self, name, token, Id):
self.name = name
self.token = token
self.Id = Id
def __str__(self):
return str(COMMAND_TUPLE(self.name, self.token, self.Id))
@lru_cache()
def bytes_to_unicode():
"""
Returns list of utf-8 byte and a corresponding list of unicode strings.
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 signficant percentage of your normal, say, 32K bpe vocab.
To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
And avoids mapping to whitespace/control characters the bpe code barfs on.
"""
_chr = chr
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))
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
[docs]class GPT2ChineseTokenizer(PretrainedTokenizer):
"""
Constructs a GPT2 Chinese tokenizer. It uses a basic tokenizer to do punctuation
splitting, lower casing and so on, and follows a WordPiece tokenizer to
tokenize as subwords.
"""
resource_files_names = {
"vocab_file": "vocab.json",
"model_file": "sentencepiece.model"
} # for save_pretrained
pretrained_resource_files_map = {
"vocab_file": {
"gpt2-base-cn":
"https://paddlenlp.bj.bcebos.com/models/transformers/gpt2/gpt2-base-cn-vocab.json",
},
"model_file": {
"gpt2-base-cn":
"https://paddlenlp.bj.bcebos.com/models/transformers/gpt2/gpt2-base-cn-sentencepiece.model"
}
}
pretrained_init_configuration = {"gpt2-base-cn": {"do_lower_case": True}, }
def __init__(self,
vocab_file,
model_file,
do_lower_case=True,
max_len=512,
bod_id="<bod>",
eod_id="<eod>",
max_length=None):
self._vocab_file = vocab_file
self._model_file = model_file
if not os.path.isfile(vocab_file):
raise ValueError(
"Can't find a vocabulary file at path '{}'. To load the "
"vocabulary from a pretrained model please use "
"`tokenizer = GPT2Tokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
.format(vocab_file))
self.max_len = max_len if max_len is not None else int(1e12)
self.encoder = json.load(open(vocab_file))
self.decoder = {v: k for k, v in self.encoder.items()}
mod = try_import("sentencepiece")
self.sp = mod.SentencePieceProcessor(model_file=model_file)
self.translator = str.maketrans(" \n", "\u2582\u2583")
[docs] def tokenize(self, text):
""" Tokenize a string. """
seg_list = [
x.translate(self.translator) for x in jieba.cut(text, cut_all=False)
]
new_seg = " ".join(seg_list)
return self.sp.encode(new_seg)
[docs] def encode(self, text):
return self.convert_tokens_to_ids(text)
def decode(self, tokens):
return self.convert_ids_to_tokens(tokens)
[docs] def convert_tokens_to_ids(self, text):
res = self.tokenize(text)
return res
[docs] def convert_ids_to_tokens(self, tokens):
text = self.sp.decode(tokens)
text = text.replace(' ', '').replace('\u2582', ' ').replace('\u2583',
'\n')
return text
[docs] def save_resources(self, save_directory):
"""
Save tokenizer related resources to files under `save_directory`.
Args:
save_directory (str): Directory to save files into.
"""
for name, file_name in self.resource_files_names.items():
save_path = os.path.join(save_directory, file_name)
shutil.copyfile(getattr(self, "_%s" % name), save_path)
[docs]class GPT2Tokenizer(PretrainedTokenizer):
resource_files_names = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt"
} # for save_pretrained
pretrained_resource_files_map = {
"vocab_file": {
"gpt2-large-en":
"http://paddlenlp.bj.bcebos.com/models/transformers/gpt2/gpt2-large-en-vocab.json",
"gpt2-medium-en":
"http://paddlenlp.bj.bcebos.com/models/transformers/gpt2/gpt2-medium-en-vocab.json",
"gpt2-small-en":
"http://paddlenlp.bj.bcebos.com/models/transformers/gpt2/gpt2-small-en-vocab.json",
},
"merges_file": {
"gpt2-large-en":
"http://paddlenlp.bj.bcebos.com/models/transformers/gpt2/gpt2-large-en-merges.txt",
"gpt2-medium-en":
"http://paddlenlp.bj.bcebos.com/models/transformers/gpt2/gpt2-medium-en-merges.txt",
"gpt2-small-en":
"http://paddlenlp.bj.bcebos.com/models/transformers/gpt2/gpt2-small-en-merges.txt",
}
}
pretrained_init_configuration = {
"gpt2-large-en": {
"do_lower_case": True
},
"gpt2-medium-en": {
"do_lower_case": True
},
"gpt2-small-en": {
"do_lower_case": True
},
}
def __init__(self,
vocab_file,
merges_file,
errors='replace',
special_tokens=None,
max_len=None,
do_lower_case=True):
self._vocab_file = vocab_file
self._merges_file = merges_file
self.max_len = int(1e12)
self.num_command_tokens = 2
self.num_type_tokens = 2
self.encoder = json.load(open(vocab_file))
self.decoder = {v: k for k, v in self.encoder.items()}
# construct the command tokens
self._command_tokens = [
CommandToken('pad', '<|endoftext|>', self.encoder['<|endoftext|>']),
CommandToken('eod', '<|endoftext|>', self.encoder['<|endoftext|>']),
]
self.command_name_map = {tok.name: tok for tok in self._command_tokens}
self.command_token_map = {
tok.token: tok
for tok in self._command_tokens
}
self.command_id_map = {tok.Id: tok for tok in self._command_tokens}
self.num_tokens = len(self.encoder)
self.num_text_tokens = self.num_tokens - 1
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_data = open(merges_file, encoding='utf-8').read().split('\n')[1:-1]
bpe_merges = [tuple(merge.split()) for merge in bpe_data]
self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
self.cache = {}
re = try_import("regex")
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.special_tokens = {}
self.special_tokens_decoder = {}
self.set_special_tokens(special_tokens)
def __len__(self):
return len(self.encoder) + len(self.special_tokens)
[docs] def set_special_tokens(self, special_tokens):
""" Add a list of additional tokens to the encoder.
The additional tokens are indexed starting from the last index of the
current vocabulary in the order of the `special_tokens` list.
"""
if not special_tokens:
self.special_tokens = {}
self.special_tokens_decoder = {}
return
self.special_tokens = dict((tok, len(self.encoder) + i)
for i, tok in enumerate(special_tokens))
self.special_tokens_decoder = {
v: k
for k, v in self.special_tokens.items()
}
logger.info("Special tokens {}".format(self.special_tokens))
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)
new_word.extend(word[i:j])
i = j
except:
new_word.extend(word[i:])
break
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
[docs] def tokenize(self, text):
""" Tokenize a string. """
bpe_tokens = []
re = try_import("regex")
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
[docs] def convert_tokens_to_ids(self, tokens):
""" Converts a sequence of tokens into ids using the vocab. """
ids = []
if isinstance(tokens, str):
if tokens in self.special_tokens:
return self.special_tokens[tokens]
else:
return self.encoder.get(tokens, 0)
for token in tokens:
if token in self.special_tokens:
ids.append(self.special_tokens[token])
else:
ids.append(self.encoder.get(token, 0))
if len(ids) > self.max_len:
logger.warning(
"Token indices sequence length is longer than the specified maximum "
" sequence length for this OpenAI GPT model ({} > {}). Running this"
" sequence through the model will result in indexing errors".
format(len(ids), self.max_len))
return ids
[docs] def convert_ids_to_tokens(self, ids, skip_special_tokens=False):
tokens = []
for i in ids:
if i in self.special_tokens_decoder:
if not skip_special_tokens:
tokens.append(self.special_tokens_decoder[i])
else:
tokens.append(self.decoder[i])
return tokens
[docs] def encode(self, text, fn=None):
processed_text = text
if fn is not None:
processed_text = fn(text)
ids = self.convert_tokens_to_ids(self.tokenize(processed_text))
return ids
def decode(self, tokens):
# TODO
text = ''.join([self.decoder[token] for token in tokens])
text = bytearray([self.byte_decoder[c] for c in text]).decode(
'utf-8', errors=self.errors)
return text
[docs] def save_resources(self, save_directory):
"""
Save tokenizer related resources to files under `save_directory`.
Args:
save_directory (str): Directory to save files into.
"""
for name, file_name in self.resource_files_names.items():
save_path = os.path.join(save_directory, file_name)
shutil.copyfile(getattr(self, "_%s" % name), save_path)