112 lines
3.2 KiB
Python
112 lines
3.2 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 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.logger import init_logger
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from vllm.tokenizers import cached_get_tokenizer
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from vllm.tokenizers.grok2 import Grok2Tokenizer
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from .inputs import DictPrompt
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from .inputs.preprocess import parse_dec_only_prompt
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from .params import ChatParams
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from .protocol import BaseRenderer
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logger = init_logger(__name__)
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class Grok2Renderer(BaseRenderer):
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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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) -> "BaseRenderer":
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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__(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=Grok2Tokenizer,
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**tokenizer_kwargs,
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)
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self._tokenizer = tokenizer
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@property
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def tokenizer(self) -> Grok2Tokenizer | None:
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return self._tokenizer
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def get_tokenizer(self) -> Grok2Tokenizer:
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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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params: ChatParams,
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) -> tuple[list[ConversationMessage], DictPrompt]:
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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 = tokenizer.apply_chat_template(
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conversation=conversation,
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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:
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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
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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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params: ChatParams,
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) -> tuple[list[ConversationMessage], DictPrompt]:
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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 = tokenizer.apply_chat_template(
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conversation=conversation,
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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:
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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
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