Zhou Taichangand汪志鹏
70ef548448
Signed-off-by: Taichang Zhou <tzhouam@connect.ust.hk> Signed-off-by: tzhouam <tzhouam@connect.ust.hk> Signed-off-by: princepride <wangzhipeng628@gmail.com> Co-authored-by: 汪志鹏 <wangzhipeng628@gmail.com>
95 lines
3.7 KiB
Python
95 lines
3.7 KiB
Python
from collections.abc import Callable
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from typing import TYPE_CHECKING
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import numpy as np
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import torch
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from vllm.v1.request import Request
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if TYPE_CHECKING:
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from vllm.v1.core.kv_cache_utils import BlockHash
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from vllm_omni.engine import AdditionalInformationPayload, OmniEngineCoreRequest, PromptEmbedsPayload
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class OmniRequest(Request):
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"""Request class for omni models, extending the base Request.
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This class extends the base vLLM Request with support for prompt
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embeddings and additional information payloads, enabling direct
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transfer of pre-computed embeddings between stages.
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Args:
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prompt_embeds: Optional serialized prompt embeddings payload.
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Used for direct transfer of embeddings between stages.
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additional_information: Optional additional information payload
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containing tensors or lists to be passed along with the request.
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"""
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def __init__(
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self,
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prompt_embeds: PromptEmbedsPayload | torch.Tensor | None = None,
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# Optional external request ID for tracking
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external_req_id: str | None = None,
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additional_information: AdditionalInformationPayload | None = None,
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*args,
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**kwargs,
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):
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prompt_embeds_tensor = self._maybe_decode_prompt_embeds(prompt_embeds)
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super().__init__(prompt_embeds=prompt_embeds_tensor, *args, **kwargs)
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# Preserve serialized prompt embeddings payload (optional)
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self.prompt_embeds_payload: PromptEmbedsPayload | None = (
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prompt_embeds if isinstance(prompt_embeds, PromptEmbedsPayload) else None
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)
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# Optional external request ID for tracking
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self.external_req_id: str | None = external_req_id
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# Serialized additional information payload (optional)
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self.additional_information: AdditionalInformationPayload | None = additional_information
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@staticmethod
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def _maybe_decode_prompt_embeds(
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prompt_embeds: PromptEmbedsPayload | torch.Tensor | None,
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) -> torch.Tensor | None:
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if isinstance(prompt_embeds, PromptEmbedsPayload):
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dtype = getattr(np, prompt_embeds.dtype)
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arr = np.frombuffer(prompt_embeds.data, dtype=dtype)
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arr = arr.reshape(prompt_embeds.shape)
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return torch.from_numpy(arr)
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return prompt_embeds
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@classmethod
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def from_engine_core_request(
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cls,
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request: OmniEngineCoreRequest,
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block_hasher: Callable[["Request"], list["BlockHash"]] | None,
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) -> "Request":
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"""Create an OmniRequest from an OmniEngineCoreRequest.
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Args:
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request: The OmniEngineCoreRequest to convert
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block_hasher: Optional function to compute block hashes for
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prefix caching
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Returns:
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OmniRequest instance created from the engine core request
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"""
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return cls(
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request_id=request.request_id,
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# Optional external request ID for tracking
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external_req_id=request.external_req_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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prompt_embeds=request.prompt_embeds,
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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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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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cache_salt=request.cache_salt,
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priority=request.priority,
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trace_headers=request.trace_headers,
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block_hasher=block_hasher,
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additional_information=request.additional_information,
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resumable=request.resumable,
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)
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