v1.0

transformers_utils/tokenizer_base.py

@@ -0,0 +1,155 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import importlib
+from abc import ABC, abstractmethod
+from typing import TYPE_CHECKING, Any
+
+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()
+
+    @property
+    @abstractmethod
+    def truncation_side(self) -> str:
+        raise NotImplementedError()
+
+    def __len__(self) -> int:
+        return self.vocab_size
+
+    @abstractmethod
+    def __call__(
+        self,
+        text: str | list[str] | list[int],
+        text_pair: str | None = None,
+        add_special_tokens: bool = False,
+        truncation: bool = False,
+        max_length: int | None = 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: int | None = None,
+    ) -> list[int]:
+        raise NotImplementedError()
+
+    @abstractmethod
+    def encode(
+        self,
+        text: str,
+        truncation: bool | None = None,
+        max_length: int | None = None,
+        add_special_tokens: bool | None = None,
+    ) -> list[int]:
+        raise NotImplementedError()
+
+    @abstractmethod
+    def apply_chat_template(
+        self,
+        messages: list["ChatCompletionMessageParam"],
+        tools: list[dict[str, Any]] | None = None,
+        **kwargs,
+    ) -> list[int]:
+        raise NotImplementedError()
+
+    @abstractmethod
+    def convert_tokens_to_string(self, tokens: list[str]) -> str:
+        raise NotImplementedError()
+
+    @abstractmethod
+    def decode(self, ids: 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)