152 lines
6.2 KiB
Python
152 lines
6.2 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
|
|
|
|
from collections.abc import Callable
|
|
from dataclasses import dataclass
|
|
from typing import TYPE_CHECKING, Any
|
|
|
|
import numpy as np
|
|
import torch
|
|
from vllm.multimodal.inputs import MultiModalFeatureSpec
|
|
from vllm.sampling_params import SamplingParams
|
|
from vllm.v1.request import Request
|
|
|
|
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,
|
|
*args,
|
|
prompt_embeds: PromptEmbedsPayload | torch.Tensor | None = None,
|
|
# Optional external request ID for tracking
|
|
external_req_id: str | None = None,
|
|
additional_information: AdditionalInformationPayload | None = None,
|
|
model_intermediate_buffer: dict | None = None,
|
|
payload_sender_info: dict[str, Any] | None = None,
|
|
**kwargs,
|
|
):
|
|
if prompt_embeds is not None:
|
|
kwargs["prompt_embeds"] = self._maybe_decode_prompt_embeds(prompt_embeds)
|
|
super().__init__(*args, **kwargs)
|
|
# vLLM 0.27 owns this counter; accelerator images still based on 0.26
|
|
# do not. Keep the Omni scheduler's stale-output drain compatible with
|
|
# both request layouts until those images move to 0.27.
|
|
self.num_stale_output_tokens = int(getattr(self, "num_stale_output_tokens", 0) or 0)
|
|
# Preserve serialized prompt embeddings payload (optional)
|
|
self.prompt_embeds_payload: PromptEmbedsPayload | None = (
|
|
prompt_embeds if isinstance(prompt_embeds, PromptEmbedsPayload) else None
|
|
)
|
|
# Optional external request ID for tracking
|
|
self.external_req_id: str | None = external_req_id
|
|
# Serialized additional information payload (optional)
|
|
self.additional_information: AdditionalInformationPayload | None = additional_information
|
|
# Runner-owned runtime payload.
|
|
self.model_intermediate_buffer: dict | None = model_intermediate_buffer
|
|
# Sender's connector address for this request's stage payload.
|
|
self.payload_sender_info: dict[str, Any] | None = payload_sender_info
|
|
|
|
@staticmethod
|
|
def _maybe_decode_prompt_embeds(
|
|
prompt_embeds: PromptEmbedsPayload | torch.Tensor | None,
|
|
) -> torch.Tensor | None:
|
|
if isinstance(prompt_embeds, PromptEmbedsPayload):
|
|
dtype = getattr(np, prompt_embeds.dtype)
|
|
arr = np.frombuffer(prompt_embeds.data, dtype=dtype)
|
|
arr = arr.reshape(prompt_embeds.shape)
|
|
return torch.from_numpy(arr)
|
|
return prompt_embeds
|
|
|
|
@classmethod
|
|
def from_engine_core_request(
|
|
cls,
|
|
request: OmniEngineCoreRequest,
|
|
block_hasher: Callable[["Request"], list["BlockHash"]] | None,
|
|
) -> "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,
|
|
# Optional external request ID for tracking
|
|
external_req_id=request.external_req_id,
|
|
client_index=request.client_index,
|
|
prompt_token_ids=request.prompt_token_ids,
|
|
prompt_embeds=request.prompt_embeds,
|
|
prompt_is_token_ids=request.prompt_is_token_ids,
|
|
mm_features=request.mm_features,
|
|
sampling_params=request.sampling_params,
|
|
pooling_params=request.pooling_params,
|
|
arrival_time=request.arrival_time,
|
|
lora_request=request.lora_request,
|
|
cache_salt=request.cache_salt,
|
|
priority=request.priority,
|
|
trace_headers=request.trace_headers,
|
|
block_hasher=block_hasher,
|
|
additional_information=request.additional_information,
|
|
model_intermediate_buffer=getattr(request, "model_intermediate_buffer", None),
|
|
payload_sender_info=request.payload_sender_info,
|
|
resumable=request.resumable,
|
|
session_id=request.session_id,
|
|
kv_hints=request.kv_hints,
|
|
reasoning_ended=request.reasoning_ended,
|
|
reasoning_parser_kwargs=request.reasoning_parser_kwargs,
|
|
abort_immediately=request.abort_immediately,
|
|
)
|
|
|
|
|
|
@dataclass
|
|
class OmniStreamingUpdate:
|
|
"""
|
|
Override: add additional information
|
|
Lightweight data for streaming session continuation.
|
|
|
|
Contains only the fields needed to update an existing streaming session
|
|
with new input data.
|
|
"""
|
|
|
|
mm_features: list[MultiModalFeatureSpec] | None
|
|
prompt_token_ids: list[int] | None
|
|
max_tokens: int
|
|
arrival_time: float
|
|
sampling_params: SamplingParams | None
|
|
additional_information: AdditionalInformationPayload | None = None
|
|
model_intermediate_buffer: dict | None = None
|
|
|
|
@classmethod
|
|
def from_request(cls, request: "Request") -> "OmniStreamingUpdate | None":
|
|
if not request.resumable:
|
|
return None
|
|
return cls(
|
|
mm_features=request.mm_features,
|
|
prompt_token_ids=request.prompt_token_ids,
|
|
max_tokens=request.max_tokens,
|
|
arrival_time=request.arrival_time,
|
|
sampling_params=request.sampling_params,
|
|
additional_information=request.additional_information,
|
|
model_intermediate_buffer=getattr(request, "model_intermediate_buffer", None),
|
|
)
|