Add ChatCompletionRequest-style support to /v1/tokenize (#23981)
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@@ -333,7 +333,7 @@ async def lifespan(fast_api_app: FastAPI):
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_global_state.tokenizer_manager, _global_state.template_manager
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)
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fast_api_app.state.openai_serving_tokenize = OpenAIServingTokenize(
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_global_state.tokenizer_manager
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_global_state.tokenizer_manager, _global_state.template_manager
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)
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fast_api_app.state.openai_serving_detokenize = OpenAIServingDetokenize(
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_global_state.tokenizer_manager
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@@ -31,6 +31,7 @@ from openai.types.responses.response import ToolChoice
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from openai.types.responses.tool import Tool
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from pydantic import (
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BaseModel,
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ConfigDict,
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Field,
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field_validator,
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model_serializer,
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@@ -1118,13 +1119,39 @@ class RerankResponse(BaseModel):
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class TokenizeRequest(BaseModel):
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"""Request schema for the /tokenize endpoint."""
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model_config = ConfigDict(extra="allow")
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model: str = DEFAULT_MODEL_NAME
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prompt: Union[str, List[str]]
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prompt: Optional[Union[str, List[str]]] = None
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messages: Optional[List[ChatCompletionMessageParam]] = None
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tools: Optional[List[Tool]] = Field(default=None, examples=[None])
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tool_choice: Optional[Union[ToolChoice, Literal["auto", "required", "none"]]] = (
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Field(default=None, examples=["auto"])
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)
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reasoning_effort: Optional[Literal["none", "low", "medium", "high"]] = None
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continue_final_message: bool = False
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chat_template_kwargs: Optional[Dict] = None
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add_special_tokens: bool = Field(
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default=True,
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description="whether to add model-specific special tokens (e.g. BOS/EOS) during encoding.",
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)
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@model_validator(mode="after")
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def validate_tokenize_input(self) -> "TokenizeRequest":
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if (self.prompt is None) == (self.messages is None):
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raise ValueError("Exactly one of 'prompt' or 'messages' must be provided.")
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return self
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def to_chat_completion_request(self) -> ChatCompletionRequest:
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data = self.model_dump(
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exclude={"prompt", "add_special_tokens"},
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exclude_none=True,
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)
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extra = getattr(self, "__pydantic_extra__", None)
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if extra:
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data.update(extra)
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return ChatCompletionRequest.model_validate(data)
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class TokenizeResponse(BaseModel):
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"""Response schema for the /tokenize endpoint."""
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@@ -1,6 +1,6 @@
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import logging
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from http import HTTPStatus
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from typing import List, Union
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from typing import List, Optional, Union
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from fastapi import Request
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@@ -12,6 +12,7 @@ from sglang.srt.entrypoints.openai.protocol import (
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TokenizeResponse,
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)
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from sglang.srt.entrypoints.openai.serving_base import OpenAIServingBase
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from sglang.srt.entrypoints.openai.serving_chat import OpenAIServingChat
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logger = logging.getLogger(__name__)
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@@ -19,6 +20,14 @@ logger = logging.getLogger(__name__)
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class OpenAIServingTokenize(OpenAIServingBase):
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"""Handler for /v1/tokenize requests"""
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def __init__(self, tokenizer_manager, template_manager=None):
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super().__init__(tokenizer_manager)
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self.chat_serving: Optional[OpenAIServingChat] = (
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OpenAIServingChat(tokenizer_manager, template_manager)
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if template_manager is not None
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else None
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)
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def _request_id_prefix(self) -> str:
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return "tok-"
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@@ -37,7 +46,11 @@ class OpenAIServingTokenize(OpenAIServingBase):
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tokenizer = self.tokenizer_manager.tokenizer
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max_model_len = getattr(tokenizer, "model_max_length", -1)
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if isinstance(request.prompt, str):
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if request.messages is not None:
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token_ids = self._tokenize_chat_request(request)
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tokens = token_ids
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count = len(token_ids)
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elif isinstance(request.prompt, str):
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token_ids = tokenizer.encode(
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request.prompt,
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add_special_tokens=request.add_special_tokens,
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@@ -61,6 +74,8 @@ class OpenAIServingTokenize(OpenAIServingBase):
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return TokenizeResponse(
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tokens=tokens, count=count, max_model_len=max_model_len
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)
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except ValueError as e:
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return self.create_error_response(str(e))
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except Exception as e:
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logger.error("Error during tokenization", exc_info=True)
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return self.create_error_response(
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@@ -69,6 +84,36 @@ class OpenAIServingTokenize(OpenAIServingBase):
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status_code=HTTPStatus.INTERNAL_SERVER_ERROR,
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)
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def _tokenize_chat_request(self, request: TokenizeRequest) -> List[int]:
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if self.chat_serving is None:
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raise ValueError("Chat template tokenization requires a template manager.")
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chat_request = request.to_chat_completion_request()
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validation_error = self.chat_serving._validate_request(chat_request)
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if validation_error:
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raise ValueError(validation_error)
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is_multimodal = self.tokenizer_manager.model_config.is_multimodal
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processed_messages = self.chat_serving._process_messages(
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chat_request, is_multimodal
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)
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prompt_ids = processed_messages.prompt_ids
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if isinstance(prompt_ids, list) and (
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prompt_ids or not processed_messages.prompt
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):
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return prompt_ids
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if isinstance(prompt_ids, str):
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return self.tokenizer_manager.tokenizer.encode(
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prompt_ids, add_special_tokens=False
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)
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if processed_messages.prompt:
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return self.tokenizer_manager.tokenizer.encode(
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processed_messages.prompt, add_special_tokens=False
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)
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raise ValueError("Failed to render chat messages into token ids.")
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class OpenAIServingDetokenize(OpenAIServingBase):
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"""Handler for /v1/detokenize requests"""
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