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import os |
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from typing import Union |
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from transformers.configuration_utils import PretrainedConfig |
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from transformers.utils import logging |
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logger = logging.get_logger(__name__) |
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class MixinConfig(PretrainedConfig): |
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def __init__( |
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self, |
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mixin_every_n_layers=4, |
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language_dim=4096, |
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vision_dim=4096, |
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head_dim=128, |
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num_heads=16, |
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intermediate_size=16384, |
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**kwargs, |
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): |
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super().__init__(**kwargs) |
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self.mixin_every_n_layers = mixin_every_n_layers |
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self.language_dim=language_dim |
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self.vision_dim=vision_dim |
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self.head_dim=head_dim |
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self.num_heads=num_heads |
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self.intermediate_size=intermediate_size |
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@classmethod |
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def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> 'PretrainedConfig': |
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config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs) |
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if 'mixin_config' in config_dict: |
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config_dict = config_dict['mixin_config'] |
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if 'model_type' in config_dict and hasattr(cls, 'model_type') and config_dict['model_type'] != cls.model_type: |
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logger.warning( |
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f"You are using a model of type {config_dict['model_type']} to instantiate a model of type " |
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f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' |
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) |
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return cls.from_dict(config_dict, **kwargs) |
