[RFC][vllm-API] Support tokenizer registry for customized tokenizer in vLLM

benchmarks/benchmark_serving.py

@@ -1224,11 +1224,12 @@ def main(args: argparse.Namespace):
         '--tokenizer-mode',
         type=str,
         default="auto",
-        choices=['auto', 'slow', 'mistral'],
+        choices=['auto', 'slow', 'mistral', 'custom'],
         help='The tokenizer mode.\n\n* "auto" will use the '
         'fast tokenizer if available.\n* "slow" will '
         'always use the slow tokenizer. \n* '
-        '"mistral" will always use the `mistral_common` tokenizer.')
+        '"mistral" will always use the `mistral_common` tokenizer. \n*'
+        '"custom" will use --tokenizer to select the preregistered tokenizer.')
 
     parser.add_argument("--served-model-name",
                         type=str,

tests/tokenization/test_tokenizer_registry.py

@@ -0,0 +1,123 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
+
+from vllm.transformers_utils.tokenizer import get_tokenizer
+from vllm.transformers_utils.tokenizer_base import (TokenizerBase,
+                                                    TokenizerRegistry)
+
+if TYPE_CHECKING:
+    from vllm.entrypoints.chat_utils import ChatCompletionMessageParam
+
+
+class TestTokenizer(TokenizerBase):
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs) -> "TestTokenizer":
+        return TestTokenizer()
+
+    @property
+    def all_special_tokens_extended(self) -> List[str]:
+        raise NotImplementedError()
+
+    @property
+    def all_special_tokens(self) -> List[str]:
+        raise NotImplementedError()
+
+    @property
+    def all_special_ids(self) -> List[int]:
+        raise NotImplementedError()
+
+    @property
+    def bos_token_id(self) -> int:
+        return 0
+
+    @property
+    def eos_token_id(self) -> int:
+        return 1
+
+    @property
+    def sep_token(self) -> str:
+        raise NotImplementedError()
+
+    @property
+    def pad_token(self) -> str:
+        raise NotImplementedError()
+
+    @property
+    def is_fast(self) -> bool:
+        raise NotImplementedError()
+
+    @property
+    def vocab_size(self) -> int:
+        raise NotImplementedError()
+
+    @property
+    def max_token_id(self) -> int:
+        raise NotImplementedError()
+
+    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()
+
+    def get_vocab(self) -> Dict[str, int]:
+        raise NotImplementedError()
+
+    def get_added_vocab(self) -> Dict[str, int]:
+        raise NotImplementedError()
+
+    def encode_one(
+        self,
+        text: str,
+        truncation: bool = False,
+        max_length: Optional[int] = None,
+    ) -> List[int]:
+        raise NotImplementedError()
+
+    def encode(self,
+               text: str,
+               add_special_tokens: Optional[bool] = None) -> List[int]:
+        raise NotImplementedError()
+
+    def apply_chat_template(self,
+                            messages: List["ChatCompletionMessageParam"],
+                            tools: Optional[Dict[str, Any]] = None,
+                            **kwargs) -> List[int]:
+        raise NotImplementedError()
+
+    def convert_tokens_to_string(self, tokens: List[str]) -> str:
+        raise NotImplementedError()
+
+    def decode(self,
+               ids: Union[List[int], int],
+               skip_special_tokens: bool = True) -> str:
+        raise NotImplementedError()
+
+    def convert_ids_to_tokens(
+        self,
+        ids: List[int],
+        skip_special_tokens: bool = True,
+    ) -> List[str]:
+        raise NotImplementedError()
+
+
+def test_customized_tokenizer():
+    TokenizerRegistry.register("test_tokenizer",
+                               "tests.tokenization.test_tokenizer_registry",
+                               "TestTokenizer")
+
+    tokenizer = TokenizerRegistry.get_tokenizer("test_tokenizer")
+    assert isinstance(tokenizer, TestTokenizer)
+    assert tokenizer.bos_token_id == 0
+    assert tokenizer.eos_token_id == 1
+
+    tokenizer = get_tokenizer("test_tokenizer", tokenizer_mode="custom")
+    assert isinstance(tokenizer, TestTokenizer)
+    assert tokenizer.bos_token_id == 0
+    assert tokenizer.eos_token_id == 1

vllm/config.py

@@ -96,8 +96,9 @@ class ModelConfig:
             it; otherwise, you must specify explicitly which task to use.
         tokenizer: Name or path of the huggingface tokenizer to use.
         tokenizer_mode: Tokenizer mode. "auto" will use the fast tokenizer if
-            available, "slow" will always use the slow tokenizer, and
-            "mistral" will always use the tokenizer from `mistral_common`.
+            available, "slow" will always use the slow tokenizer,
+            "mistral" will always use the tokenizer from `mistral_common`, and
+            "custom" will use --tokenizer to select the preregistered tokenizer.
         trust_remote_code: Trust remote code (e.g., from HuggingFace) when
             downloading the model and tokenizer.
         allowed_local_media_path: Allowing API requests to read local images or
@@ -453,10 +454,10 @@ def _init_has_inner_state(self) -> bool:
 
     def _verify_tokenizer_mode(self) -> None:
         tokenizer_mode = self.tokenizer_mode.lower()
-        if tokenizer_mode not in ["auto", "slow", "mistral"]:
+        if tokenizer_mode not in ["auto", "slow", "mistral", "custom"]:
             raise ValueError(
                 f"Unknown tokenizer mode: {self.tokenizer_mode}. Must be "
-                "either 'auto', 'slow' or 'mistral'.")
+                "either 'auto', 'slow', 'mistral' or 'custom'.")
         self.tokenizer_mode = tokenizer_mode
 
     def _get_preferred_task(

vllm/engine/arg_utils.py

@@ -280,11 +280,13 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
             '--tokenizer-mode',
             type=str,
             default=EngineArgs.tokenizer_mode,
-            choices=['auto', 'slow', 'mistral'],
+            choices=['auto', 'slow', 'mistral', 'custom'],
             help='The tokenizer mode.\n\n* "auto" will use the '
             'fast tokenizer if available.\n* "slow" will '
             'always use the slow tokenizer. \n* '
-            '"mistral" will always use the `mistral_common` tokenizer.')
+            '"mistral" will always use the `mistral_common` tokenizer. \n* '
+            '"custom" will use --tokenizer to select the '
+            'preregistered tokenizer.')
         parser.add_argument('--trust-remote-code',
                             action='store_true',
                             help='Trust remote code from huggingface.')

vllm/entrypoints/llm.py

@@ -1051,9 +1051,9 @@ def _embedding_score(
 
     def _cross_encoding_score(
         self,
-        tokenizer: Union[AnyTokenizer],
-        text_1: List[Union[str, TextPrompt, TokensPrompt]],
-        text_2: List[Union[str, TextPrompt, TokensPrompt]],
+        tokenizer: AnyTokenizer,
+        text_1: List[str],
+        text_2: List[str],
         truncate_prompt_tokens: Optional[int] = None,
         use_tqdm: bool = True,
         lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
@@ -1176,27 +1176,28 @@ def ensure_str(prompt: SingletonPrompt):
         if isinstance(text_1, (str, dict)):
             # Convert a single prompt to a list.
             text_1 = [text_1]
-        text_1 = [ensure_str(t) for t in text_1]
+        input_text_1: List[str] = [ensure_str(t) for t in text_1]
 
         if isinstance(text_2, (str, dict)):
             # Convert a single prompt to a list.
             text_2 = [text_2]
-        text_2 = [ensure_str(t) for t in text_2]
+        input_text_2: List[str] = [ensure_str(t) for t in text_2]
 
-        if len(text_1) > 1 and len(text_1) != len(text_2):
+        if len(input_text_1) > 1 and len(input_text_1) != len(input_text_2):
             raise ValueError("Input lengths must be either 1:1, 1:N or N:N")
-        if len(text_1) == 0:
+        if len(input_text_1) == 0:
             raise ValueError("At least one text element must be given")
-        if len(text_2) == 0:
+        if len(input_text_2) == 0:
             raise ValueError("At least one text_pair element must be given")
 
         if self.llm_engine.model_config.is_cross_encoder:
-            return self._cross_encoding_score(tokenizer, text_1, text_2,
+            return self._cross_encoding_score(tokenizer, input_text_1,
+                                              input_text_2,
                                               truncate_prompt_tokens, use_tqdm,
                                               lora_request,
                                               prompt_adapter_request)
         else:
-            return self._embedding_score(tokenizer, text_1, text_2,
+            return self._embedding_score(tokenizer, input_text_1, input_text_2,
                                          truncate_prompt_tokens, use_tqdm,
                                          lora_request, prompt_adapter_request)
 

vllm/entrypoints/openai/serving_engine.py

@@ -400,8 +400,7 @@ async def _preprocess_chat(
         _chat_template_kwargs.update(chat_template_kwargs or {})
 
         request_prompt: Union[str, List[int]]
-        is_mistral_tokenizer = isinstance(tokenizer, MistralTokenizer)
-        if is_mistral_tokenizer:
+        if isinstance(tokenizer, MistralTokenizer):
             request_prompt = apply_mistral_chat_template(
                 tokenizer,
                 messages=messages,

vllm/entrypoints/openai/serving_score.py

@@ -121,7 +121,7 @@ async def create_score(
 
                 tokenize_async = make_async(tokenizer.__call__,
                                             executor=self._tokenizer_executor)
-                prompt_inputs = await tokenize_async(text=q,
+                prompt_inputs = await tokenize_async(q,
                                                      text_pair=t,
                                                      **tokenization_kwargs)
 

vllm/logits_process.py

@@ -31,7 +31,7 @@ def get_bad_words_logits_processors(
 
             if isinstance(tokenizer, MistralTokenizer):
                 # Mistral tokenizers should not add special tokens
-                prompt_token_ids = tokenizer.encode(prompt=prompt)
+                prompt_token_ids = tokenizer.encode(text=prompt)
             else:
                 prompt_token_ids = tokenizer.encode(text=prompt,
                                                     add_special_tokens=False)

vllm/transformers_utils/tokenizer.py

@@ -14,14 +14,16 @@
 from vllm.envs import VLLM_USE_MODELSCOPE
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
+from vllm.transformers_utils.tokenizer_base import (TokenizerBase,
+                                                    TokenizerRegistry)
 from vllm.transformers_utils.tokenizers import MistralTokenizer
 from vllm.transformers_utils.utils import check_gguf_file
 from vllm.utils import make_async
 
 logger = init_logger(__name__)
 
 AnyTokenizer = Union[PreTrainedTokenizer, PreTrainedTokenizerFast,
-                     MistralTokenizer]
+                     TokenizerBase]
 
 
 def decode_tokens(
@@ -47,11 +49,7 @@ def encode_tokens(
     Backend-agnostic equivalent of HF's
     :code:`tokenizer.encode(text, add_special_tokens=...)`.
     """
-    if isinstance(tokenizer, MistralTokenizer):
-        return tokenizer.tokenizer.encode(text,
-                                          bos=add_special_tokens,
-                                          eos=add_special_tokens)
-    elif add_special_tokens is not None:
+    if add_special_tokens is not None:
         return tokenizer.encode(text, add_special_tokens=add_special_tokens)
     return tokenizer.encode(text)
 
@@ -183,9 +181,17 @@ def get_tokenizer(
             'encoding and decoding.',
             FutureWarning,
             stacklevel=2)
+
+    tokenizer: AnyTokenizer
     if tokenizer_mode == "mistral":
         tokenizer = MistralTokenizer.from_pretrained(str(tokenizer_name),
                                                      revision=revision)
+    elif tokenizer_mode == "custom":
+        tokenizer = TokenizerRegistry.get_tokenizer(str(tokenizer_name),
+                                                    *args,
+                                                    revision=revision,
+                                                    download_dir=download_dir,
+                                                    **kwargs)
     else:
         try:
             tokenizer = AutoTokenizer.from_pretrained(