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# Copyright (c) OpenMMLab. All rights reserved.
import json
import os.path as osp
from typing import Optional, Sequence, Union
import torch
class SentencePieceTokenizer:
"""Tokenizer of sentencepiece.
Args:
model_file (str): the path of the tokenizer model
"""
def __init__(self, model_file: str):
from sentencepiece import SentencePieceProcessor
self.model = SentencePieceProcessor(model_file=model_file)
self._no_prefix_space_tokens = None
@property
def vocab_size(self):
"""vocabulary size."""
return self.model.vocab_size()
@property
def bos_token_id(self):
"""begine of the sentence token id."""
return self.model.bos_id()
@property
def eos_token_id(self):
"""end of the sentence token id."""
return self.model.eos_id()
@property
def no_prefix_space_tokens(self):
"""tokens without prefix space."""
if self._no_prefix_space_tokens is None:
vocab = self.model.IdToPiece(list(range(self.vocab_size)))
self._no_prefix_space_tokens = {
i
for i, tok in enumerate(vocab) if not tok.startswith('▁')
}
return self._no_prefix_space_tokens
def _maybe_add_prefix_space(self, tokens, decoded):
"""maybe add prefix space for incremental decoding."""
if len(tokens) and tokens[0] not in self.no_prefix_space_tokens:
return ' ' + decoded
else:
return decoded
def encode(self, s: str):
"""Tokenize a prompt.
Args:
s (str): a prompt
Returns:
list[int]: token ids
"""
add_bos = False
add_eos = False
if s.find('<BOS>') != -1:
s = s.replace('<BOS>', '')
add_bos = True
if s == '<EOS>':
s = ''
add_eos = True
return self.model.Encode(s, add_bos=add_bos, add_eos=add_eos)
def decode(self, t: Sequence[int], offset: Optional[int] = None):
"""De-tokenize.
Args:
t (List[int]): a list of token ids
offset (int): for incrementally decoding. Default to None, which
means not applied.
Returns:
str: text of decoding tokens
"""
if isinstance(t, torch.Tensor):
t = t.tolist()
t = t[offset:]
out_string = self.model.Decode(t)
if offset:
out_string = self._maybe_add_prefix_space(t, out_string)
return out_string
def __call__(self, s: Union[str, Sequence[str]]):
"""Tokenize prompts.
Args:
s (str): prompts
Returns:
list[int]: token ids
"""
import addict
add_bos = False
add_eos = False
input_ids = self.model.Encode(s, add_bos=add_bos, add_eos=add_eos)
return addict.Addict(input_ids=input_ids)
class HuggingFaceTokenizer:
"""Tokenizer of sentencepiece.
Args:
model_dir (str): the directory of the tokenizer model
"""
def __init__(self, model_dir: str):
from transformers import (AutoTokenizer, CodeLlamaTokenizerFast,
LlamaTokenizer, LlamaTokenizerFast)
model_file = osp.join(model_dir, 'tokenizer.model')
backend_tokenizer_file = osp.join(model_dir, 'tokenizer.json')
model_file_exists = osp.exists(model_file)
if not osp.exists(backend_tokenizer_file) and model_file_exists:
print('WARNING: Can not find tokenizer.json. '
'It may take long time to initialize the tokenizer.')
self.model = AutoTokenizer.from_pretrained(model_dir,
trust_remote_code=True)
self.need_padding = type(self.model) in [
LlamaTokenizer, LlamaTokenizerFast, CodeLlamaTokenizerFast
]
self._no_prefix_space_tokens = None
# save tokenizer.json to reuse
if not osp.exists(backend_tokenizer_file) and model_file_exists:
if hasattr(self.model, 'backend_tokenizer'):
self.model.backend_tokenizer.save(backend_tokenizer_file)
if self.model.eos_token_id is None:
generation_config_file = osp.join(model_dir,
'generation_config.json')
with open(generation_config_file, 'r') as f:
cfg = json.load(f)
self.model.eos_token_id = cfg['eos_token_id']
@property
def vocab_size(self):
"""vocabulary size."""
return self.model.vocab_size
@property
def bos_token_id(self):
"""begine of the sentence token id."""
return self.model.bos_token_id
@property
def eos_token_id(self):
"""end of the sentence token id."""
return self.model.eos_token_id
@property
def no_prefix_space_tokens(self):
"""tokens without prefix space."""
if self._no_prefix_space_tokens is None:
vocab = self.model.convert_ids_to_tokens(
list(range(self.vocab_size)))
self._no_prefix_space_tokens = {
i
for i, tok in enumerate(vocab) if not tok.startswith('▁')
}
return self._no_prefix_space_tokens
def _maybe_add_prefix_space(self, tokens, decoded):
"""maybe add prefix space for incremental decoding."""
if self.need_padding and len(
tokens) and tokens[0] not in self.no_prefix_space_tokens:
return ' ' + decoded
else:
return decoded
def encode(self, s: str):
"""Tokenize a prompt.
Args:
s (str): a prompt
Returns:
list[int]: token ids
"""
add_special_tokens = False
if s.find('<BOS>') != -1:
s = s.replace('<BOS>', '<s>')
if s == '<EOS>':
s = '</s>'
if len(s) == 0:
add_special_tokens = True
return self.model.encode(s, add_special_tokens=add_special_tokens)
def decode(self, t: Sequence[int], offset: Optional[int] = None):
"""De-tokenize.
Args:
t (List[int]): a list of token ids
offset (int): for incrementally decoding. Default to None, which
means not applied.
Returns:
str: text of decoding tokens
"""
skip_special_tokens = True
t = t[offset:]
out_string = self.model.decode(t,
skip_special_tokens=skip_special_tokens)
if offset:
out_string = self._maybe_add_prefix_space(t, out_string)
return out_string
def __call__(self, s: Union[str, Sequence[str]]):
"""Tokenize prompts.
Args:
s (str): prompts
Returns:
list[int]: token ids
"""
add_special_tokens = False
return self.model(s, add_special_tokens=add_special_tokens)
class Tokenizer:
"""Tokenize prompts or de-tokenize tokens into texts.
Args:
model_file (str): the path of the tokenizer model
"""
def __init__(self, model_file: str):
if model_file.endswith('.model'):
model_folder = osp.split(model_file)[0]
else:
model_folder = model_file
model_file = osp.join(model_folder, 'tokenizer.model')
tokenizer_config_file = osp.join(model_folder, 'tokenizer_config.json')
model_file_exists = osp.exists(model_file)
config_exists = osp.exists(tokenizer_config_file)
use_hf_model = config_exists or not model_file_exists
if not use_hf_model:
self.model = SentencePieceTokenizer(model_file)
else:
self.model = HuggingFaceTokenizer(model_folder)
@property
def vocab_size(self):
"""vocabulary size."""
return self.model.vocab_size
@property
def bos_token_id(self):
"""begine of the sentence token id."""
return self.model.bos_token_id
@property
def eos_token_id(self):
"""end of the sentence token id."""
return self.model.eos_token_id
def encode(self, s: str):
"""Tokenize a prompt.
Args:
s (str): a prompt
Returns:
list[int]: token ids
"""
return self.model.encode(s)
def decode(self, t: Sequence[int], offset: Optional[int] = None):
"""De-tokenize.
Args:
t (List[int]): a list of token ids
offset (int): for incrementally decoding. Default to None, which
means not applied.
Returns:
str: text of decoding tokens
"""
return self.model.decode(t, offset)
def __call__(self, s: Union[str, Sequence[str]]):
"""Tokenize prompts.
Args:
s (str): prompts
Returns:
list[int]: token ids
"""
return self.model(s)
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