|
|
|
|
|
import os
|
|
from shutil import copyfile
|
|
from typing import Any, Dict, List, Optional, Tuple
|
|
|
|
import sentencepiece as spm
|
|
from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
|
|
from transformers.utils import logging
|
|
|
|
|
|
logger = logging.get_logger(__name__)
|
|
|
|
VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
|
|
|
|
PRETRAINED_VOCAB_FILES_MAP = {
|
|
"vocab_file": {},
|
|
"tokenizer_file": {},
|
|
}
|
|
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {}
|
|
|
|
|
|
class BaichuanTokenizer(PreTrainedTokenizer):
|
|
"""
|
|
Construct a Baichuan tokenizer. Based on byte-level Byte-Pair-Encoding.
|
|
|
|
Args:
|
|
vocab_file (`str`):
|
|
Path to the vocabulary file.
|
|
"""
|
|
|
|
vocab_files_names = VOCAB_FILES_NAMES
|
|
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
|
|
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
|
|
model_input_names = ["input_ids", "attention_mask"]
|
|
|
|
def __init__(
|
|
self,
|
|
vocab_file,
|
|
unk_token="<unk>",
|
|
bos_token="<s>",
|
|
eos_token="</s>",
|
|
pad_token=None,
|
|
sp_model_kwargs: Optional[Dict[str, Any]] = None,
|
|
add_bos_token=True,
|
|
add_eos_token=False,
|
|
clean_up_tokenization_spaces=False,
|
|
**kwargs,
|
|
):
|
|
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
|
|
bos_token = (
|
|
AddedToken(bos_token, lstrip=False, rstrip=False)
|
|
if isinstance(bos_token, str)
|
|
else bos_token
|
|
)
|
|
eos_token = (
|
|
AddedToken(eos_token, lstrip=False, rstrip=False)
|
|
if isinstance(eos_token, str)
|
|
else eos_token
|
|
)
|
|
unk_token = (
|
|
AddedToken(unk_token, lstrip=False, rstrip=False)
|
|
if isinstance(unk_token, str)
|
|
else unk_token
|
|
)
|
|
pad_token = (
|
|
AddedToken(pad_token, lstrip=False, rstrip=False)
|
|
if isinstance(pad_token, str)
|
|
else pad_token
|
|
)
|
|
|
|
self.vocab_file = vocab_file
|
|
self.add_bos_token = add_bos_token
|
|
self.add_eos_token = add_eos_token
|
|
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
|
|
self.sp_model.Load(vocab_file)
|
|
super().__init__(
|
|
bos_token=bos_token,
|
|
eos_token=eos_token,
|
|
unk_token=unk_token,
|
|
pad_token=pad_token,
|
|
add_bos_token=add_bos_token,
|
|
add_eos_token=add_eos_token,
|
|
sp_model_kwargs=self.sp_model_kwargs,
|
|
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
|
|
**kwargs,
|
|
)
|
|
def __getstate__(self):
|
|
state = self.__dict__.copy()
|
|
state["sp_model"] = None
|
|
return state
|
|
|
|
def __setstate__(self, d):
|
|
self.__dict__ = d
|
|
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
|
|
self.sp_model.Load(self.vocab_file)
|
|
|
|
@property
|
|
def vocab_size(self):
|
|
"""Returns vocab size"""
|
|
return self.sp_model.get_piece_size()
|
|
|
|
def get_vocab(self):
|
|
"""Returns vocab as a dict"""
|
|
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
|
|
vocab.update(self.added_tokens_encoder)
|
|
return vocab
|
|
|
|
def _tokenize(self, text):
|
|
"""Returns a tokenized string."""
|
|
return self.sp_model.encode(text, out_type=str)
|
|
|
|
def _convert_token_to_id(self, token):
|
|
"""Converts a token (str) in an id using the vocab."""
|
|
return self.sp_model.piece_to_id(token)
|
|
|
|
def _convert_id_to_token(self, index):
|
|
"""Converts an index (integer) in a token (str) using the vocab."""
|
|
token = self.sp_model.IdToPiece(index)
|
|
return token
|
|
|
|
def convert_tokens_to_string(self, tokens):
|
|
"""Converts a sequence of tokens (string) in a single string."""
|
|
current_sub_tokens = []
|
|
out_string = ""
|
|
prev_is_special = False
|
|
for i, token in enumerate(tokens):
|
|
|
|
if token in self.all_special_tokens:
|
|
if not prev_is_special and i != 0:
|
|
out_string += " "
|
|
out_string += self.sp_model.decode(current_sub_tokens) + token
|
|
prev_is_special = True
|
|
current_sub_tokens = []
|
|
else:
|
|
current_sub_tokens.append(token)
|
|
prev_is_special = False
|
|
out_string += self.sp_model.decode(current_sub_tokens)
|
|
return out_string
|
|
|
|
def save_vocabulary(
|
|
self, save_directory, filename_prefix: Optional[str] = None
|
|
) -> Tuple[str]:
|
|
"""
|
|
Save the vocabulary and special tokens file to a directory.
|
|
|
|
Args:
|
|
save_directory (`str`):
|
|
The directory in which to save the vocabulary.
|
|
|
|
Returns:
|
|
`Tuple(str)`: Paths to the files saved.
|
|
"""
|
|
if not os.path.isdir(save_directory):
|
|
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
|
|
return
|
|
out_vocab_file = os.path.join(
|
|
save_directory,
|
|
(filename_prefix + "-" if filename_prefix else "")
|
|
+ VOCAB_FILES_NAMES["vocab_file"],
|
|
)
|
|
|
|
if os.path.abspath(self.vocab_file) != os.path.abspath(
|
|
out_vocab_file
|
|
) and os.path.isfile(self.vocab_file):
|
|
copyfile(self.vocab_file, out_vocab_file)
|
|
elif not os.path.isfile(self.vocab_file):
|
|
with open(out_vocab_file, "wb") as fi:
|
|
content_spiece_model = self.sp_model.serialized_model_proto()
|
|
fi.write(content_spiece_model)
|
|
|
|
return (out_vocab_file,)
|
|
|
|
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
|
|
bos_token_id = [self.bos_token_id] if self.add_bos_token else []
|
|
eos_token_id = [self.eos_token_id] if self.add_eos_token else []
|
|
|
|
output = bos_token_id + token_ids_0 + eos_token_id
|
|
|
|
if token_ids_1 is not None:
|
|
output = output + bos_token_id + token_ids_1 + eos_token_id
|
|
|
|
return output
|
|
|
|
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:
|
|
`List[int]`: 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,
|
|
)
|
|
|
|
bos_token_id = [1] if self.add_bos_token else []
|
|
eos_token_id = [1] if self.add_eos_token else []
|
|
|
|
if token_ids_1 is None:
|
|
return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id
|
|
return (
|
|
bos_token_id
|
|
+ ([0] * len(token_ids_0))
|
|
+ eos_token_id
|
|
+ bos_token_id
|
|
+ ([0] * len(token_ids_1))
|
|
+ eos_token_id
|
|
)
|
|
|
|
def create_token_type_ids_from_sequences(
|
|
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
|
|
) -> List[int]:
|
|
"""
|
|
Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
|
|
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, only returns the first portion of the mask (0s).
|
|
|
|
Args:
|
|
token_ids_0 (`List[int]`):
|
|
List of ids.
|
|
token_ids_1 (`List[int]`, *optional*):
|
|
Optional second list of IDs for sequence pairs.
|
|
|
|
Returns:
|
|
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
|
|
"""
|
|
bos_token_id = [self.bos_token_id] if self.add_bos_token else []
|
|
eos_token_id = [self.eos_token_id] if self.add_eos_token else []
|
|
|
|
output = [0] * len(bos_token_id + token_ids_0 + eos_token_id)
|
|
|
|
if token_ids_1 is not None:
|
|
output += [1] * len(bos_token_id + token_ids_1 + eos_token_id)
|
|
|
|
return output
|
|
|
