vllm/vllm/transformers_utils/tokenizer_base.py
Keyun Tong 3ee696a63d
[RFC][vllm-API] Support tokenizer registry for customized tokenizer in vLLM (#12518)
Signed-off-by: Keyun Tong <tongkeyun@gmail.com>
2025-02-12 12:25:58 +08:00

147 lines
3.8 KiB
Python

# SPDX-License-Identifier: Apache-2.0
import importlib
from abc import ABC, abstractmethod
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
if TYPE_CHECKING:
from vllm.entrypoints.chat_utils import ChatCompletionMessageParam
class TokenizerBase(ABC):
@property
@abstractmethod
def all_special_tokens_extended(self) -> List[str]:
raise NotImplementedError()
@property
@abstractmethod
def all_special_tokens(self) -> List[str]:
raise NotImplementedError()
@property
@abstractmethod
def all_special_ids(self) -> List[int]:
raise NotImplementedError()
@property
@abstractmethod
def bos_token_id(self) -> int:
raise NotImplementedError()
@property
@abstractmethod
def eos_token_id(self) -> int:
raise NotImplementedError()
@property
@abstractmethod
def sep_token(self) -> str:
raise NotImplementedError()
@property
@abstractmethod
def pad_token(self) -> str:
raise NotImplementedError()
@property
@abstractmethod
def is_fast(self) -> bool:
raise NotImplementedError()
@property
@abstractmethod
def vocab_size(self) -> int:
raise NotImplementedError()
@property
@abstractmethod
def max_token_id(self) -> int:
raise NotImplementedError()
def __len__(self) -> int:
return self.vocab_size
@abstractmethod
def __call__(
self,
text: Union[str, List[str], List[int]],
text_pair: Optional[str] = None,
add_special_tokens: bool = False,
truncation: bool = False,
max_length: Optional[int] = None,
):
raise NotImplementedError()
@abstractmethod
def get_vocab(self) -> Dict[str, int]:
raise NotImplementedError()
@abstractmethod
def get_added_vocab(self) -> Dict[str, int]:
raise NotImplementedError()
@abstractmethod
def encode_one(
self,
text: str,
truncation: bool = False,
max_length: Optional[int] = None,
) -> List[int]:
raise NotImplementedError()
@abstractmethod
def encode(self,
text: str,
add_special_tokens: Optional[bool] = None) -> List[int]:
raise NotImplementedError()
@abstractmethod
def apply_chat_template(self,
messages: List["ChatCompletionMessageParam"],
tools: Optional[List[Dict[str, Any]]] = None,
**kwargs) -> List[int]:
raise NotImplementedError()
@abstractmethod
def convert_tokens_to_string(self, tokens: List[str]) -> str:
raise NotImplementedError()
@abstractmethod
def decode(self,
ids: Union[List[int], int],
skip_special_tokens: bool = True) -> str:
raise NotImplementedError()
@abstractmethod
def convert_ids_to_tokens(
self,
ids: List[int],
skip_special_tokens: bool = True,
) -> List[str]:
raise NotImplementedError()
class TokenizerRegistry:
# Tokenizer name -> (tokenizer module, tokenizer class)
REGISTRY: Dict[str, Tuple[str, str]] = {}
@staticmethod
def register(name: str, module: str, class_name: str) -> None:
TokenizerRegistry.REGISTRY[name] = (module, class_name)
@staticmethod
def get_tokenizer(
tokenizer_name: str,
*args,
**kwargs,
) -> TokenizerBase:
tokenizer_cls = TokenizerRegistry.REGISTRY.get(tokenizer_name)
if tokenizer_cls is None:
raise ValueError(f"Tokenizer {tokenizer_name} not found.")
tokenizer_module = importlib.import_module(tokenizer_cls[0])
class_ = getattr(tokenizer_module, tokenizer_cls[1])
return class_.from_pretrained(*args, **kwargs)