from typing import TYPE_CHECKING, Callable, Optional from vllm.v1.request import Request from vllm.v1.structured_output.request import StructuredOutputRequest if TYPE_CHECKING: from vllm.v1.core.kv_cache_utils import BlockHash from vllm_omni.engine import AdditionalInformationPayload, OmniEngineCoreRequest, PromptEmbedsPayload class OmniRequest(Request): """Request class for omni models, extending the base Request. This class extends the base vLLM Request with support for prompt embeddings and additional information payloads, enabling direct transfer of pre-computed embeddings between stages. Args: prompt_embeds: Optional serialized prompt embeddings payload. Used for direct transfer of embeddings between stages. additional_information: Optional additional information payload containing tensors or lists to be passed along with the request. """ def __init__( self, prompt_embeds: Optional[PromptEmbedsPayload] = None, additional_information: Optional[AdditionalInformationPayload] = None, *args, **kwargs, ): super().__init__(*args, **kwargs) # Serialized prompt embeddings payload (optional) self.prompt_embeds: Optional[PromptEmbedsPayload] = prompt_embeds # Serialized additional information payload (optional) self.additional_information: Optional[AdditionalInformationPayload] = additional_information @classmethod def from_engine_core_request( cls, request: OmniEngineCoreRequest, block_hasher: Optional[Callable[["Request"], list["BlockHash"]]], ) -> "Request": """Create an OmniRequest from an OmniEngineCoreRequest. Args: request: The OmniEngineCoreRequest to convert block_hasher: Optional function to compute block hashes for prefix caching Returns: OmniRequest instance created from the engine core request """ return cls( request_id=request.request_id, client_index=request.client_index, prompt_token_ids=request.prompt_token_ids, mm_features=request.mm_features, sampling_params=request.sampling_params, pooling_params=request.pooling_params, eos_token_id=request.eos_token_id, arrival_time=request.arrival_time, lora_request=request.lora_request, structured_output_request=( StructuredOutputRequest(sampling_params=request.sampling_params) if request.sampling_params else None ), cache_salt=request.cache_salt, priority=request.priority, trace_headers=request.trace_headers, block_hasher=block_hasher, prompt_embeds=request.prompt_embeds, additional_information=request.additional_information, )