50 lines
1.5 KiB
Python
50 lines
1.5 KiB
Python
import os
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from typing import Optional, Union
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from transformers import AutoConfig, PretrainedConfig
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class EAGLEConfig(PretrainedConfig):
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model_type = "eagle"
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def __init__(self,
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model: Union[PretrainedConfig, dict, None] = None,
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truncated_vocab_size: Optional[int] = None,
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**kwargs):
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model_config = None if model is None else (AutoConfig.for_model(
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**model) if isinstance(model, dict) else model)
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for k, v in kwargs.items():
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if k != "architectures" and k != "model_type" and hasattr(
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model_config, k):
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setattr(model_config, k, v)
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self.model = model_config
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if self.model is None:
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self.truncated_vocab_size = None
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else:
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self.truncated_vocab_size = self.model.vocab_size if \
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truncated_vocab_size is None else truncated_vocab_size
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if "architectures" not in kwargs:
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kwargs["architectures"] = ["EAGLEModel"]
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super().__init__(**kwargs)
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if self.model is not None:
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for k, v in self.model.to_dict().items():
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if not hasattr(self, k):
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setattr(self, k, v)
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@classmethod
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def from_pretrained(
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cls,
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pretrained_model_name_or_path: Union[str, os.PathLike],
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**kwargs,
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) -> "EAGLEConfig":
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config_dict, kwargs = cls.get_config_dict(
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pretrained_model_name_or_path, **kwargs)
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return cls.from_dict(config_dict, **kwargs)
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