sgl-project--sglang/python/sglang/srt/entrypoints/openai/serving_tokenize.py

import logging
from http import HTTPStatus
from typing import List, Optional, Union

from fastapi import Request

from sglang.srt.entrypoints.openai.protocol import (
    DetokenizeRequest,
    DetokenizeResponse,
    ErrorResponse,
    TokenizeRequest,
    TokenizeResponse,
)
from sglang.srt.entrypoints.openai.serving_base import OpenAIServingBase
from sglang.srt.entrypoints.openai.serving_chat import OpenAIServingChat

logger = logging.getLogger(__name__)


class OpenAIServingTokenize(OpenAIServingBase):
    """Handler for /v1/tokenize requests"""

    def __init__(self, tokenizer_manager, template_manager=None):
        super().__init__(tokenizer_manager)
        self.chat_serving: Optional[OpenAIServingChat] = (
            OpenAIServingChat(tokenizer_manager, template_manager)
            if template_manager is not None
            else None
        )

    def _request_id_prefix(self) -> str:
        return "tok-"

    def _convert_to_internal_request(
        self, request: TokenizeRequest, raw_request: Request
    ) -> tuple[TokenizeRequest, TokenizeRequest]:
        return request, request

    async def _handle_non_streaming_request(
        self,
        adapted_request: TokenizeRequest,
        request: TokenizeRequest,
        raw_request: Request,
    ) -> Union[TokenizeResponse, ErrorResponse]:
        try:
            tokenizer = self.tokenizer_manager.tokenizer
            max_model_len = getattr(tokenizer, "model_max_length", -1)

            if request.messages is not None:
                token_ids = self._tokenize_chat_request(request)
                tokens = token_ids
                count = len(token_ids)
            elif isinstance(request.prompt, str):
                token_ids = tokenizer.encode(
                    request.prompt,
                    add_special_tokens=request.add_special_tokens,
                )
                tokens = token_ids
                count = len(token_ids)
            elif isinstance(request.prompt, list):
                token_ids_list = [
                    tokenizer.encode(
                        text, add_special_tokens=request.add_special_tokens
                    )
                    for text in request.prompt
                ]
                tokens = token_ids_list
                count = [len(ids) for ids in token_ids_list]
            else:
                return self.create_error_response(
                    f"Invalid prompt type: {type(request.prompt)}. Expected str or List[str]."
                )

            return TokenizeResponse(
                tokens=tokens, count=count, max_model_len=max_model_len
            )
        except ValueError as e:
            return self.create_error_response(str(e))
        except Exception as e:
            logger.error("Error during tokenization", exc_info=True)
            return self.create_error_response(
                f"Internal server error during tokenization: {e}",
                err_type="InternalServerError",
                status_code=HTTPStatus.INTERNAL_SERVER_ERROR,
            )

    def _tokenize_chat_request(self, request: TokenizeRequest) -> List[int]:
        if self.chat_serving is None:
            raise ValueError("Chat template tokenization requires a template manager.")

        chat_request = request.to_chat_completion_request()
        validation_error = self.chat_serving._validate_request(chat_request)
        if validation_error:
            raise ValueError(validation_error)

        is_multimodal = self.tokenizer_manager.model_config.is_multimodal
        processed_messages = self.chat_serving._process_messages(
            chat_request, is_multimodal
        )

        prompt_ids = processed_messages.prompt_ids
        if isinstance(prompt_ids, list) and (
            prompt_ids or not processed_messages.prompt
        ):
            return prompt_ids
        if isinstance(prompt_ids, str):
            return self.tokenizer_manager.tokenizer.encode(
                prompt_ids, add_special_tokens=False
            )
        if processed_messages.prompt:
            return self.tokenizer_manager.tokenizer.encode(
                processed_messages.prompt, add_special_tokens=False
            )

        raise ValueError("Failed to render chat messages into token ids.")


class OpenAIServingDetokenize(OpenAIServingBase):
    """Handler for /v1/detokenize requests"""

    def _request_id_prefix(self) -> str:
        return "detok-"

    def _convert_to_internal_request(
        self, request: DetokenizeRequest, raw_request: Request
    ) -> tuple[DetokenizeRequest, DetokenizeRequest]:
        return request, request

    async def _handle_non_streaming_request(
        self,
        adapted_request: DetokenizeRequest,
        request: DetokenizeRequest,
        raw_request: Request,
    ) -> Union[DetokenizeResponse, ErrorResponse]:
        try:
            tokenizer = self.tokenizer_manager.tokenizer

            if (
                isinstance(request.tokens, list)
                and request.tokens
                and isinstance(request.tokens[0], int)
            ):
                if not all(isinstance(t, int) for t in request.tokens):
                    return self.create_error_response(
                        "Invalid input: 'tokens' must be a list of integers."
                    )
                tokens_to_decode = [int(t) for t in request.tokens]
                text = tokenizer.decode(
                    tokens_to_decode, skip_special_tokens=request.skip_special_tokens
                )
                text_out: Union[str, List[str]] = text
            elif (
                isinstance(request.tokens, list)
                and request.tokens
                and isinstance(request.tokens[0], list)
            ):
                texts: List[str] = []
                for token_list in request.tokens:
                    if not all(isinstance(t, int) for t in token_list):
                        return self.create_error_response(
                            f"Invalid input: Sublist in 'tokens' must contain only integers. Found: {token_list}"
                        )
                    decoded_text = tokenizer.decode(
                        [int(t) for t in token_list],
                        skip_special_tokens=request.skip_special_tokens,
                    )
                    texts.append(decoded_text)
                text_out = texts
            elif isinstance(request.tokens, list) and not request.tokens:
                text_out = ""
            else:
                return self.create_error_response(
                    f"Invalid tokens type: {type(request.tokens)}. Expected List[int] or List[List[int]]."
                )

            return DetokenizeResponse(text=text_out)
        except Exception as e:
            logger.error("Error during detokenization", exc_info=True)
            if "decode" in str(e).lower():
                return self.create_error_response(
                    f"Error decoding tokens: {e}. Input tokens might be invalid for the model.",
                    err_type="DecodeError",
                    status_code=HTTPStatus.BAD_REQUEST,
                )
            return self.create_error_response(
                f"Internal server error during detokenization: {e}",
                err_type="InternalServerError",
                status_code=HTTPStatus.INTERNAL_SERVER_ERROR,
            )