tzhouam
83e7f6ba12
- Introduced `OmniModelRunnerOutput` and `OmniRequestOutput` classes for handling multimodal outputs. - Implemented `OmniEngineCoreRequest` and `OmniInputPreprocessor` to manage prompt embeddings and additional information. - Created `OmniTokensPrompt`, `OmniTokenInputs`, and `OmniEmbedsPrompt` for enhanced token handling. - Added parsing and preprocessing functions to support new input structures. - Removed the obsolete `diffusion_engine.py` file as part of the refactor.
47 lines
2.2 KiB
Python
47 lines
2.2 KiB
Python
from vllm.v1.request import Request
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from vllm.v1.structured_output.request import StructuredOutputRequest
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from vllm.multimodal.inputs import MultiModalKwargs
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from vllm.utils import is_list_of
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from typing import Optional
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from vllm_omni.engine import PromptEmbedsPayload, AdditionalInformationPayload, OmniEngineCoreRequest
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class OmniRequest(Request):
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def __init__(
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self,
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prompt_embeds: Optional[PromptEmbedsPayload] = None,
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additional_information: Optional[AdditionalInformationPayload] = None,
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*args, **kwargs):
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super().__init__(*args, **kwargs)
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# Serialized prompt embeddings payload (optional)
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self.prompt_embeds: Optional[PromptEmbedsPayload] = prompt_embeds
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# Serialized additional information payload (optional)
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self.additional_information: Optional[AdditionalInformationPayload] = additional_information
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@classmethod
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def from_engine_core_request(cls, request: OmniEngineCoreRequest) -> "Request":
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if request.mm_inputs is not None:
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assert isinstance(request.mm_inputs, list)
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assert is_list_of(request.mm_inputs, MultiModalKwargs), (
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"mm_inputs was not updated in EngineCore.add_request")
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return cls(
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request_id=request.request_id,
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client_index=request.client_index,
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prompt_token_ids=request.prompt_token_ids,
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multi_modal_inputs=request.mm_inputs,
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multi_modal_hashes=request.mm_hashes,
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multi_modal_placeholders=request.mm_placeholders,
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sampling_params=request.sampling_params,
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pooling_params=request.pooling_params,
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eos_token_id=request.eos_token_id,
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arrival_time=request.arrival_time,
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lora_request=request.lora_request,
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structured_output_request=StructuredOutputRequest(
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sampling_params=request.sampling_params) \
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if request.sampling_params else None,
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cache_salt=request.cache_salt,
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priority=request.priority,
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prompt_embeds=request.prompt_embeds,
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additional_information=request.additional_information,
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) |