wehub-resource-sync
91 lines
2.8 KiB
Python
91 lines
2.8 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 vllm.config import VllmConfig
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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.deepseek_v4 import DeepseekV4Tokenizer
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from vllm.utils.async_utils import make_async
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from .base import BaseRenderer
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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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logger = init_logger(__name__)
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class DeepseekV4Renderer(BaseRenderer[DeepseekV4Tokenizer]):
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def __init__(
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self,
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config: VllmConfig,
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tokenizer: DeepseekV4Tokenizer | None,
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) -> None:
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super().__init__(config, tokenizer)
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self._apply_chat_template_async = make_async(
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self._apply_chat_template, executor=self._executor
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)
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def _apply_chat_template(self, *args, **kwargs):
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return self.get_tokenizer().apply_chat_template(*args, **kwargs)
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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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conversation, mm_data, mm_uuids = parse_chat_messages(
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messages,
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self.model_config,
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content_format="string",
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media_io_kwargs=params.media_io_kwargs,
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mm_processor_kwargs=params.mm_processor_kwargs,
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)
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prompt_raw = self._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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conversation, mm_data, mm_uuids = await parse_chat_messages_async(
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messages,
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self.model_config,
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content_format="string",
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media_io_kwargs=params.media_io_kwargs,
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mm_processor_kwargs=params.mm_processor_kwargs,
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)
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prompt_raw = await self._apply_chat_template_async(
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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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