55 lines
1.9 KiB
Python
55 lines
1.9 KiB
Python
from dataclasses import dataclass
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from typing import Optional
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from vllm.v1.core.sched.output import NewRequestData
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from vllm.v1.request import Request
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from vllm_omni.engine import AdditionalInformationPayload, PromptEmbedsPayload
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@dataclass
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class OmniNewRequestData(NewRequestData):
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"""New request data for omni models with embeddings support.
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Extends NewRequestData to include prompt embeddings and additional
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information for direct transfer between pipeline stages.
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Args:
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prompt_embeds: Optional serialized prompt embeddings payload
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additional_information: Optional serialized additional information
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dictionary containing tensors or lists
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"""
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# Optional serialized prompt embeddings
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prompt_embeds: Optional[PromptEmbedsPayload] = None
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# Optional serialized additional information
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additional_information: Optional[AdditionalInformationPayload] = None
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@classmethod
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def from_request(
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cls,
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request: Request,
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block_ids: tuple[list[int], ...],
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) -> "OmniNewRequestData":
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"""Create OmniNewRequestData from a Request object.
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Args:
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request: Request object to convert
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block_ids: Tuple of block ID lists for KV cache allocation
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Returns:
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OmniNewRequestData instance with data from the request
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"""
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return cls(
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req_id=request.request_id,
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prompt_token_ids=request.prompt_token_ids,
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mm_features=request.mm_features,
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sampling_params=request.sampling_params,
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pooling_params=request.pooling_params,
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block_ids=block_ids,
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num_computed_tokens=request.num_computed_tokens,
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lora_request=request.lora_request,
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prompt_embeds=request.prompt_embeds,
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additional_information=request.additional_information,
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)
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