148 lines
4.6 KiB
Python
148 lines
4.6 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from concurrent.futures import ThreadPoolExecutor
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from typing import Any
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from vllm.config import ModelConfig
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from vllm.entrypoints.chat_utils import (
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ChatCompletionMessageParam,
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ConversationMessage,
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parse_chat_messages,
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parse_chat_messages_async,
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)
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from vllm.inputs import TextPrompt, TokensPrompt
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from vllm.logger import init_logger
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from vllm.tokenizers import cached_get_tokenizer
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from vllm.tokenizers.mistral import MistralTokenizer
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from vllm.utils.async_utils import make_async
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from .protocol import RendererLike
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logger = init_logger(__name__)
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def safe_apply_chat_template(
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tokenizer: MistralTokenizer,
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messages: list[ChatCompletionMessageParam],
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**kwargs,
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) -> str | list[int]:
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from mistral_common.exceptions import MistralCommonException
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try:
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return tokenizer.apply_chat_template(messages, **kwargs)
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# mistral-common uses assert statements to stop processing of input
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# if input does not comply with the expected format.
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# We convert those assertion errors to ValueErrors so they can be
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# properly caught in the preprocessing_input step
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except (AssertionError, MistralCommonException) as e:
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raise ValueError(str(e)) from e
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# External library exceptions can sometimes occur despite the framework's
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# internal exception management capabilities.
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except Exception as e:
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# Log and report any library-related exceptions for further
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# investigation.
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logger.exception(
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"An error occurred in `mistral_common` while applying chat template"
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)
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raise ValueError(str(e)) from e
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class MistralRenderer(RendererLike):
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@classmethod
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def from_config(
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cls,
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config: ModelConfig,
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tokenizer_kwargs: dict[str, Any],
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) -> "RendererLike":
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return cls(config, tokenizer_kwargs)
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def __init__(
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self,
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config: ModelConfig,
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tokenizer_kwargs: dict[str, Any],
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) -> None:
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super().__init__()
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self.config = config
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if config.skip_tokenizer_init:
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tokenizer = None
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else:
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tokenizer = cached_get_tokenizer(
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tokenizer_cls=MistralTokenizer,
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**tokenizer_kwargs,
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)
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self._tokenizer = tokenizer
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self._apply_chat_template_executor = ThreadPoolExecutor(max_workers=1)
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self._apply_chat_template_async = make_async(
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safe_apply_chat_template, executor=self._apply_chat_template_executor
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)
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@property
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def tokenizer(self) -> MistralTokenizer | None:
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return self._tokenizer
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def get_tokenizer(self) -> MistralTokenizer:
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tokenizer = self.tokenizer
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if tokenizer is None:
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raise ValueError("Tokenizer not available when `skip_tokenizer_init=True`")
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return tokenizer
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def render_messages(
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self,
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messages: list[ChatCompletionMessageParam],
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**kwargs,
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) -> tuple[list[ConversationMessage], TextPrompt | TokensPrompt]:
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tokenizer = self.get_tokenizer()
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conversation, mm_data, mm_uuids = parse_chat_messages(
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messages,
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self.config,
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content_format="string",
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)
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prompt_raw = safe_apply_chat_template(tokenizer, messages, **kwargs)
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prompt = (
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TextPrompt(prompt=prompt_raw)
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if isinstance(prompt_raw, str)
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else TokensPrompt(prompt_token_ids=prompt_raw)
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)
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if mm_data is not None:
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prompt["multi_modal_data"] = mm_data
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if mm_uuids is not None:
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prompt["multi_modal_uuids"] = mm_uuids
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return conversation, prompt # type: ignore[return-value]
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async def render_messages_async(
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self,
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messages: list[ChatCompletionMessageParam],
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**kwargs,
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) -> tuple[list[ConversationMessage], TextPrompt | TokensPrompt]:
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tokenizer = self.get_tokenizer()
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conversation, mm_data, mm_uuids = await parse_chat_messages_async(
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messages,
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self.config,
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content_format="string",
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)
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prompt_raw = await self._apply_chat_template_async(
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tokenizer, messages, **kwargs
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)
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prompt = (
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TextPrompt(prompt=prompt_raw)
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if isinstance(prompt_raw, str)
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else TokensPrompt(prompt_token_ids=prompt_raw)
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)
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if mm_data is not None:
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prompt["multi_modal_data"] = mm_data
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if mm_uuids is not None:
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prompt["multi_modal_uuids"] = mm_uuids
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return conversation, prompt # type: ignore[return-value]
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