class EAGLEConfig(PretrainedConfig):
model_type = "eagle"
def __init__(self,
model: Union[PretrainedConfig, dict, None] = None,
truncated_vocab_size: Optional[int] = None,
method: Optional[str] = 'eagle',
**kwargs):
model_config: Union[PretrainedConfig, DeepseekV2Config, None]
if isinstance(model, dict):
archs = model.get("architectures", [])
target_archs = ["DeepseekV2ForCausalLM", "DeepseekV3ForCausalLM"]
if any(target_arch in archs for target_arch in target_archs):
# AutoConfig does not support DeepSeek MoE models yet
model_config = DeepseekV2Config(**model)
else:
model_config = AutoConfig.for_model(**model)
else:
model_config = model
for k, v in kwargs.items():
if k != "architectures" and k != "model_type" and hasattr(
model_config, k):
setattr(model_config, k, v)
self.model = model_config
if self.model is None:
self.truncated_vocab_size = None
else:
self.truncated_vocab_size = self.model.vocab_size if \
truncated_vocab_size is None else truncated_vocab_size
if not envs.VLLM_USE_V1:
kwargs["architectures"] = ["EAGLEModel"]
else:
# Eagle model name should follow naming convention of
# LlamaForCausalLM -> EagleLlamaForCausalLM
if method == "eagle":
assert self.model is not None, \
"model should not be None when method is eagle"
kwargs["architectures"] = [
f"Eagle{arch}" if not arch.startswith("Eagle") \
else arch for arch in self.model.architectures
]
elif method == "eagle3":
assert self.model is not None, \
"model should not be None when method is eagle3"
kwargs["architectures"] = [
f"Eagle3{arch}" if not arch.startswith("Eagle3") \
else arch for arch in self.model.architectures
]
else:
raise ValueError(f"Invalid method {method}. \
Supported methods are eagle and eagle3.")
super().__init__(**kwargs)
if self.model is not None:
for k, v in self.model.to_dict().items():
if k not in kwargs:
setattr(self, k, v)
@classmethod
def from_pretrained(
cls,
pretrained_model_name_or_path: Union[str, os.PathLike],
**kwargs,
) -> "EAGLEConfig":
config_dict, kwargs = cls.get_config_dict(
pretrained_model_name_or_path, **kwargs)
return cls.from_dict(config_dict, **kwargs)