# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
#
# 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 json
import os
import shutil
from typing import Dict, List, Optional
from paddle.utils import try_import
from paddlenlp.transformers import (
BasicTokenizer,
PretrainedTokenizer,
WordpieceTokenizer,
)
__all__ = [
"SkepTokenizer",
]
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.
"""
bs = list(range(33, 126 + 1)) + list(range(161, 172 + 1)) + list(range(174, 255 + 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
class BpeEncoder(object):
"""BpeEncoder"""
def __init__(self, encoder_json_file, vocab_bpe_file, errors="replace", unk_token="<|endoftext|>", **kwargs):
"""
Constructs a BpeEncoder.
Args:
encoder_json_file (`str`): The path to bpe encode json file.
vocab_bpe_file (`str`): The path to bpe vocab file.
errors (`str`): the error handler
unk_token (`str`): the unk token
"""
self.encoder = self.__get_encoder(encoder_json_file)
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()}
self.bpe_ranks = self.__get_bpe_ranks(vocab_bpe_file)
self.unk_token = unk_token
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+""")
def __get_encoder(self, encoder_json_file):
with open(encoder_json_file, "r") as f:
encoder = json.load(f)
return encoder
def __get_bpe_ranks(self, vocab_bpe_file):
with open(vocab_bpe_file, "r", encoding="utf-8") as f:
bpe_data = f.read()
bpe_merges = [tuple(merge_str.split()) for merge_str in bpe_data.split("\n")[1:-1]]
bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
return bpe_ranks
def bpe(self, token):
"""
bpe
"""
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 Exception:
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
def encode(self, text: str) -> List[int]:
"""
encode the text to token_ids
TODO(wj-Mcat): to be deprecated
"""
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(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(" "))
return bpe_tokens
def decode(self, tokens: List[str]) -> str:
"""
decode
TODO(wj-Mcat): to be deprecated
"""
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
def _tokenize(self, text: str) -> List[str]:
"""tokenize text into tokens with bpe algo
Args:
text (str): the content of text
Returns:
List[str]: the sub token of text
"""
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
def _convert_token_to_id(self, token: str) -> int:
"""Converts a token (str) in an id using the vocab."""
return self.encoder.get(token, self.encoder.get(self.unk_token))
def _convert_id_to_token(self, index: int) -> str:
"""Converts an index (integer) in a token (str) using the vocab."""
return self.decoder.get(index)
def convert_tokens_to_string(self, tokens: List[str]) -> str:
"""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
[文档]class SkepTokenizer(PretrainedTokenizer):
r"""
Constructs a Skep tokenizer. It uses a basic tokenizer to do punctuation
splitting, lower casing and so on, and follows a WordPiece tokenizer to
tokenize as subwords.
This tokenizer inherits from :class:`~paddlenlp.transformers.tokenizer_utils.PretrainedTokenizer`
which contains most of the main methods. For more information regarding those methods,
please refer to this superclass.
Args:
vocab_file (str):
The vocabulary file path (ends with '.txt') required to instantiate
a `WordpieceTokenizer`.
bpe_vocab_file (str, optional):
The vocabulary file path of a `BpeTokenizer`. Defaults to `None`.
bpe_json_file (str, optional):
The json file path of a `BpeTokenizer`. Defaults to `None`.
use_bpe_encoder (bool, optional):
Whether or not to use BPE Encoder. Defaults to `False`.
need_token_type_id (bool, optional):
Whether or not to use token type id. Defaults to `True`.
add_two_sep_token_inter (bool, optional):
Whether or not to add two different `sep_token`. Defaults to `False`.
unk_token (str, optional):
The special token for unknown words.
Defaults to "[UNK]".
sep_token (str, optional):
The special token for separator token.
Defaults to "[SEP]".
pad_token (str, optional):
The special token for padding.
Defaults to "[PAD]".
cls_token (str, optional):
The special token for cls.
Defaults to "[CLS]".
mask_token (str, optional):
The special token for mask.
Defaults to "[MASK]".
Examples:
.. code-block::
from paddlenlp.transformers import SkepTokenizer
tokenizer = SkepTokenizer.from_pretrained('skep_ernie_2.0_large_en')
encoded_inputs = tokenizer('He was a puppeteer')
# encoded_inputs:
# {
# 'input_ids': [101, 2002, 2001, 1037, 13997, 11510, 102],
# 'token_type_ids': [0, 0, 0, 0, 0, 0, 0]
# }
"""
resource_files_names = {
"vocab_file": "vocab.txt",
"bpe_vocab_file": "vocab.bpe",
"bpe_json_file": "encoder.json",
} # for save_pretrained
pretrained_resource_files_map = {
"vocab_file": {
"skep_ernie_1.0_large_ch": "https://bj.bcebos.com/paddlenlp/models/transformers/skep/skep_ernie_1.0_large_ch.vocab.txt",
"skep_ernie_2.0_large_en": "https://bj.bcebos.com/paddlenlp/models/transformers/skep/skep_ernie_2.0_large_en.vocab.txt",
"skep_roberta_large_en": "https://bj.bcebos.com/paddlenlp/models/transformers/skep/skep_roberta_large_en.vocab.txt",
},
"bpe_vocab_file": {
"skep_ernie_1.0_large_ch": None,
"skep_ernie_2.0_large_en": None,
"skep_roberta_large_en": "https://bj.bcebos.com/paddlenlp/models/transformers/skep/skep_roberta_large_en.vocab.bpe",
},
"bpe_json_file": {
"skep_ernie_1.0_large_ch": None,
"skep_ernie_2.0_large_en": None,
"skep_roberta_large_en": "https://bj.bcebos.com/paddlenlp/models/transformers/skep/skep_roberta_large_en.encoder.json",
},
}
max_model_input_sizes = {
"skep_ernie_1.0_large_ch": 512,
"skep_ernie_2.0_large_en": 512,
"skep_roberta_large_en": 514,
}
pretrained_init_configuration = {
"skep_ernie_1.0_large_ch": {
"do_lower_case": True,
"use_bpe_encoder": False,
"need_token_type_id": True,
"add_two_sep_token_inter": False,
},
"skep_ernie_2.0_large_en": {
"do_lower_case": True,
"use_bpe_encoder": False,
"need_token_type_id": True,
"add_two_sep_token_inter": False,
},
"skep_roberta_large_en": {
"do_lower_case": True,
"use_bpe_encoder": True,
"need_token_type_id": False,
"add_two_sep_token_inter": True,
},
}
def __init__(
self,
vocab_file,
bpe_vocab_file=None,
bpe_json_file=None,
do_lower_case=True,
use_bpe_encoder=False,
need_token_type_id=True,
add_two_sep_token_inter=False,
unk_token="[UNK]",
sep_token="[SEP]",
pad_token="[PAD]",
cls_token="[CLS]",
mask_token="[MASK]",
**kwargs
):
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 = SkepTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`".format(vocab_file)
)
self.vocab_file = vocab_file
self.bpe_vocab_file = bpe_vocab_file
self.bpe_json_file = bpe_json_file
self.vocab = self.load_vocabulary(
vocab_file,
unk_token=unk_token,
pad_token=pad_token,
bos_token=cls_token,
eos_token=sep_token,
mask_token=mask_token,
)
self.use_bpe_encoder = use_bpe_encoder
self.need_token_type_id = need_token_type_id
self.add_two_sep_token_inter = add_two_sep_token_inter
if not self.use_bpe_encoder:
self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=unk_token)
else:
assert (bpe_vocab_file and bpe_json_file) is not None, "bpe_vocab_file and bpe_json_file must be not None."
if os.path.isfile(bpe_vocab_file) and os.path.isfile(bpe_json_file):
self.bpe_tokenizer = BpeEncoder(bpe_json_file, bpe_vocab_file, unk_token=unk_token)
@property
def vocab_size(self):
r"""
Return the size of vocabulary.
Returns:
int: the size of vocabulary.
"""
return len(self.vocab)
def _tokenize(self, text):
r"""
End-to-end tokenization for Skep models.
Args:
text (str): The text to be tokenized.
Returns:
list: A list of string representing converted tokens.
"""
split_tokens = []
if not self.use_bpe_encoder:
for token in self.basic_tokenizer.tokenize(text):
for sub_token in self.wordpiece_tokenizer.tokenize(token):
split_tokens.append(sub_token)
else:
for token in self.bpe_tokenizer._tokenize(text):
split_tokens.append(str(token))
return split_tokens
[文档] def num_special_tokens_to_add(self, pair=False):
r"""
Returns the number of added tokens when encoding a sequence with special tokens.
Args:
pair (bool, optional):
Returns the number of added tokens in the case of a sequence
pair if set to True, returns the number of added tokens in the case of a single sequence if set to False.
Defaults to False.
Returns:
int: Number of tokens added to sequences
"""
token_ids_0 = []
token_ids_1 = []
return len(self.build_inputs_with_special_tokens(token_ids_0, token_ids_1 if pair else None))
[文档] def build_offset_mapping_with_special_tokens(self, offset_mapping_0, offset_mapping_1=None):
"""
Build offset map from a pair of offset map by concatenating and adding offsets of special tokens.
Should be overridden in a subclass if the model has a special way of building those.
Args:
offset_mapping_0 (List[tuple]):
List of char offsets to which the special tokens will be added.
offset_mapping_1 (List[tuple], optional):
Optional second list of char offsets for offset mapping pairs.
Returns:
List[tuple]: List of char offsets with the appropriate offsets of special tokens.
"""
if offset_mapping_1 is None:
return [(0, 0)] + offset_mapping_0 + [(0, 0)]
return [(0, 0)] + offset_mapping_0 + [(0, 0)] + offset_mapping_1 + [(0, 0)]
[文档] def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None):
r"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task.
A skep_ernie_1.0_large_ch/skep_ernie_2.0_large_en sequence pair mask has the following format:
::
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence | second sequence |
If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
note: There is no need token type ids for skep_roberta_large_ch model.
Args:
token_ids_0 (List[int]):
List of IDs.
token_ids_1 (List[int], optional):
Optional second list of IDs for sequence pairs.
Defaults to `None`.
Returns:
List[int]: List of token_type_id according to the given sequence(s).
"""
if self.need_token_type_id:
_sep = [self.sep_token_id]
_cls = [self.cls_token_id]
if token_ids_1 is None:
return len(_cls + token_ids_0 + _sep) * [0]
return len(_cls + token_ids_0 + _sep) * [0] + len(token_ids_1 + _sep) * [1]
else:
# For model skep-roberta-large-en, token type ids is no need.
return None
[文档] 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)
source_file = getattr(self, name, None)
if not source_file:
continue
if os.path.abspath(source_file) != os.path.abspath(save_path):
shutil.copyfile(source_file, save_path)
[文档] def convert_tokens_to_string(self, tokens: List[str]):
"""
Converts a sequence of tokens (list of string) in a single string.
Args:
tokens (list): A list of string representing tokens to be converted.
Returns:
str: Converted string from tokens.
Examples:
.. code-block::
from paddlenlp.transformers import RoFormerTokenizer
tokenizer = RoFormerTokenizer.from_pretrained('roformer-chinese-base')
tokens = tokenizer.tokenize('欢迎使用百度飞桨')
#['欢迎', '使用', '百度', '飞', '桨']
strings = tokenizer.convert_tokens_to_string(tokens)
#'欢迎 使用 百度 飞 桨'
"""
# to handle the bpe and wordpiece case
if hasattr(self, "wordpiece_tokenizer"):
return " ".join(tokens).replace(" ##", "").strip()
else:
return self.bpe_tokenizer.convert_tokens_to_string(tokens)
def _convert_token_to_id(self, token: str) -> int:
"""Converts a token (str) in an id using the vocab."""
if self.use_bpe_encoder:
return self.bpe_tokenizer._convert_token_to_id(token)
return super()._convert_token_to_id(token)
def _convert_id_to_token(self, index: int) -> str:
"""Converts an index (integer) in a token (str) using the vocab."""
if self.use_bpe_encoder:
return self.bpe_tokenizer._convert_id_to_token(index)
return super()._convert_id_to_token(index)
[文档] def get_special_tokens_mask(
self,
token_ids_0: List[int],
token_ids_1: Optional[List[int]] = None,
already_has_special_tokens: bool = False,
) -> List[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` method.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0,
token_ids_1=token_ids_1,
already_has_special_tokens=True,
)
if token_ids_1 is not None:
return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
return [1] + ([0] * len(token_ids_0)) + [1]
[文档] def get_vocab(self) -> Dict[str, int]:
"""
Returns the vocabulary as a dictionary of token to index.
`tokenizer.get_vocab()[token]` is equivalent to `tokenizer.convert_tokens_to_ids(token)` when `token` is in the
vocab.
Returns:
`Dict[str, int]`: The vocabulary.
"""
return dict(self.vocab.token_to_idx, **self.added_tokens_encoder)
