from dataclasses import dataclass from typing import Optional from vllm.engine.arg_utils import EngineArgs from vllm.utils import FlexibleArgumentParser from vllm_omni.config import OmniModelConfig @dataclass class OmniEngineArgs(EngineArgs): stage_id: int = 0 model_stage: str = "thinker" model_arch: str = "Qwen2_5OmniForConditionalGeneration" engine_output_type: Optional[str] = None @staticmethod def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser: """Shared CLI arguments for vLLM engine.""" parser.add_argument( "--engine-output-type", type=str, default=OmniEngineArgs.engine_output_type, help=( "Declare EngineCoreOutput.output_type (e.g., 'text', 'image', " "'text+image', 'latent'). This will be written into " "model_config.engine_output_type for schedulers to use." ), ) parser.add_argument( "--model-stage", type=str, default=OmniEngineArgs.model_stage, help=( "Declare model stage (e.g., 'thinker', 'talker', 'token2wav'). " "This will be written into model_config.model_stage for " "schedulers to use." ), ) return parser def create_model_config(self) -> OmniModelConfig: # First, get the base ModelConfig from the parent class base_config = super().create_model_config() # Create OmniModelConfig by copying all base config attributes # and adding the new omni-specific fields config_dict = base_config.__dict__.copy() # Add the new omni-specific fields config_dict["stage_id"] = self.stage_id config_dict["model_stage"] = self.model_stage config_dict["model_arch"] = self.model_arch config_dict["engine_output_type"] = self.engine_output_type # Create and return the OmniModelConfig instance omni_config = OmniModelConfig(**config_dict) omni_config.hf_config.architectures = omni_config.architectures return omni_config