grok2.py 2.77 KB
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import Any

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from vllm.config import VllmConfig
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from vllm.entrypoints.chat_utils import (
    ChatCompletionMessageParam,
    ConversationMessage,
    parse_chat_messages,
    parse_chat_messages_async,
)
from vllm.logger import init_logger
from vllm.tokenizers import cached_get_tokenizer
from vllm.tokenizers.grok2 import Grok2Tokenizer

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from .base import BaseRenderer
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from .inputs import DictPrompt
from .inputs.preprocess import parse_dec_only_prompt
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from .params import ChatParams
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logger = init_logger(__name__)


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class Grok2Renderer(BaseRenderer[Grok2Tokenizer]):
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    @classmethod
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    def from_config(  # type: ignore[override]
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        cls,
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        config: VllmConfig,
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        tokenizer_kwargs: dict[str, Any],
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    ) -> "Grok2Renderer":
        model_config = config.model_config
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        if model_config.skip_tokenizer_init:
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            tokenizer = None
        else:
            tokenizer = cached_get_tokenizer(
                tokenizer_cls=Grok2Tokenizer,
                **tokenizer_kwargs,
            )

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        return cls(config, tokenizer)
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    def render_messages(
        self,
        messages: list[ChatCompletionMessageParam],
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        params: ChatParams,
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    ) -> tuple[list[ConversationMessage], DictPrompt]:
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        tokenizer = self.get_tokenizer()
        conversation, mm_data, mm_uuids = parse_chat_messages(
            messages,
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            self.model_config,
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            content_format="string",
        )

        prompt_raw = tokenizer.apply_chat_template(
            conversation=conversation,
            messages=messages,
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            **params.get_apply_chat_template_kwargs(),
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        )

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        prompt = parse_dec_only_prompt(prompt_raw)
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        if mm_data is not None:
            prompt["multi_modal_data"] = mm_data
        if mm_uuids is not None:
            prompt["multi_modal_uuids"] = mm_uuids

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        return conversation, prompt
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    async def render_messages_async(
        self,
        messages: list[ChatCompletionMessageParam],
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        params: ChatParams,
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    ) -> tuple[list[ConversationMessage], DictPrompt]:
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        tokenizer = self.get_tokenizer()
        conversation, mm_data, mm_uuids = await parse_chat_messages_async(
            messages,
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            self.model_config,
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            content_format="string",
        )

        prompt_raw = tokenizer.apply_chat_template(
            conversation=conversation,
            messages=messages,
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            **params.get_apply_chat_template_kwargs(),
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        )

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        prompt = parse_dec_only_prompt(prompt_raw)
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        if mm_data is not None:
            prompt["multi_modal_data"] = mm_data
        if mm_uuids is not None:
            prompt["multi_modal_uuids"] = mm_uuids

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        return conversation, prompt