diff --git a/vllm_omni/engine/processor.py b/vllm_omni/engine/processor.py index 1c5891265..850da0b51 100644 --- a/vllm_omni/engine/processor.py +++ b/vllm_omni/engine/processor.py @@ -14,10 +14,11 @@ from vllm.config import VllmConfig from vllm.transformers_utils.tokenizer_group import TokenizerGroup from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry from vllm_omni.inputs.preprocess import OmniInputPreprocessor +from vllm.platforms import current_platform from vllm.v1.engine.processor import Processor from vllm_omni.engine import PromptEmbedsPayload, AdditionalInformationPayload, AdditionalInformationEntry, OmniEngineCoreRequest - +import torch class OmniProcessor(Processor): def __init__(self, @@ -71,7 +72,6 @@ class OmniProcessor(Processor): lora_request=lora_request, return_mm_hashes=return_mm_hashes, ) - from vllm.platforms import current_platform current_platform.validate_request( prompt=prompt, params=params, @@ -157,8 +157,7 @@ class OmniProcessor(Processor): prompt_embeds_payload: Optional[PromptEmbedsPayload] = None additional_information_payload: Optional[AdditionalInformationPayload] = None if "prompt_embeds" in decoder_inputs: # type: ignore[operator] - import numpy as np - import torch + pe: torch.Tensor = decoder_inputs["prompt_embeds"] # type: ignore[index] if pe.ndim != 2: raise ValueError( @@ -174,8 +173,6 @@ class OmniProcessor(Processor): dtype=dtype_str, ) if "additional_information" in decoder_inputs: # type: ignore[operator] - import numpy as np - import torch raw_info: dict[str, Any] = decoder_inputs["additional_information"] # type: ignore[index] entries: dict[str, AdditionalInformationEntry] = {} for key, value in raw_info.items(): diff --git a/vllm_omni/inputs/parse.py b/vllm_omni/inputs/parse.py index a7c72b9a9..892740f95 100644 --- a/vllm_omni/inputs/parse.py +++ b/vllm_omni/inputs/parse.py @@ -8,7 +8,7 @@ def parse_singleton_prompt_omni(prompt: SingletonPrompt) -> ParsedSingletonPromp elif isinstance(prompt, dict): # Type ignores are because mypy does not correctly infer the TypedDicts # Pyright does succeed. - # 优先 tokens:当 tokens 与 embeds 同在时,保留两者并走 tokens 路径 + # Priority tokens: When both tokens and embeds exist, keep both and follow the tokens path if "prompt_token_ids" in prompt: return ParsedTokensPrompt( type="tokens", content=prompt) # type: ignore[typeddict-item]