81 lines
2.9 KiB
Python
81 lines
2.9 KiB
Python
from dataclasses import dataclass, field
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from vllm.v1.core.sched.output import CachedRequestData, NewRequestData, SchedulerOutput
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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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(overrides parent's torch.Tensor type with PromptEmbedsPayload
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for cross-process serialization)
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external_req_id: Optional external request ID for tracking
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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 (override parent type for serialization)
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prompt_embeds: PromptEmbedsPayload | None = None # type: ignore[assignment]
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# Optional external request ID for tracking
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external_req_id: str | None = None
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# Optional serialized additional information
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additional_information: AdditionalInformationPayload | None = 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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prefill_token_ids: list[int] | None = None,
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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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prefill_token_ids: Optional prefill token IDs for v2 model runner
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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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external_req_id=getattr(request, "external_req_id", None),
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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=getattr(request, "prompt_embeds", None),
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prefill_token_ids=prefill_token_ids,
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additional_information=getattr(request, "additional_information", None),
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)
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@dataclass
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class OmniCachedRequestData(CachedRequestData):
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"""Cached request data for omni models with embeddings support.
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Args:
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prompt_token_ids: Mapping from request ID to list of prompt token IDs
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"""
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prompt_token_ids: dict[str, list[int]]
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@dataclass
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class OmniSchedulerOutput(SchedulerOutput):
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"""Scheduler output with omni-specific transfer metadata."""
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finished_requests_needing_kv_transfer: dict[str, dict] = field(default_factory=dict)
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