configuration_symphony.py

from transformers import PretrainedConfig, AutoConfig

class SymphonySpeechEncoderConfig(PretrainedConfig):
    model_type = "symphony_speech_encoder"
    def __init__(
        self,
        n_mels=128,
        n_ctx=1500,
        n_state=1280,
        n_head=20,
        n_layer=32,
        stage_tokens=[80, 80, 80],
        compression_size=50,
        **kwargs
    ):
        super().__init__(**kwargs)
        self.n_mels = n_mels
        self.n_ctx = n_ctx
        self.n_state = n_state
        self.n_head = n_head
        self.n_layer = n_layer
        self.stage_tokens = stage_tokens
        self.compression_size = compression_size

class SymphonyConfig(PretrainedConfig):
    model_type = "symphony"
    def __init__(
        self,
        encoder_config=None,
        llm_config=None,
        lora_r=16,
        lora_a=64,
        llm_modules=None,
        low_resource=False,
        **kwargs
    ):
        # llm_modules 기본값
        if llm_modules is None:
            llm_modules = ["q_proj","k_proj","v_proj","o_proj","gate_proj","up_proj","down_proj"]
        
        # LLM config 처리: dict면 AutoConfig로 변환
        if llm_config is None:
            llm_config = AutoConfig.from_pretrained("Qwen/Qwen3-4B")
        elif isinstance(llm_config, dict):
            if "_name_or_path" in llm_config:
                llm_config = AutoConfig.from_pretrained(llm_config["_name_or_path"], **llm_config)
            else:
                llm_config = AutoConfig.from_dict(llm_config)
        
        # Encoder config 처리
        if encoder_config is None:
            encoder_config = SymphonySpeechEncoderConfig()
        elif isinstance(encoder_config, dict):
            encoder_config = SymphonySpeechEncoderConfig(**encoder_config)

        self.llm_config = llm_config
        self.encoder_config = encoder_config
        self.lora_r = lora_r
        self.lora_a = lora_a
        self.llm_modules = llm_modules
        self.low_resource = low_resource

        super().__init__(**kwargs)