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|
| import os |
| from typing import Union |
|
|
| from transformers.configuration_utils import PretrainedConfig |
| from transformers.utils import logging |
|
|
| logger = logging.get_logger(__name__) |
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|
|
| class InternVisionConfig(PretrainedConfig): |
| r""" |
| Configuration class for InternVL's vision encoder (`InternVisionModel`). |
| """ |
|
|
| model_type = "intern_vit_6b" |
|
|
| def __init__( |
| self, |
| num_channels=3, |
| patch_size=14, |
| image_size=224, |
| qkv_bias=False, |
| hidden_size=3200, |
| num_attention_heads=25, |
| intermediate_size=12800, |
| qk_normalization=True, |
| num_hidden_layers=48, |
| use_flash_attn=True, |
| hidden_act="gelu", |
| norm_type="rms_norm", |
| layer_norm_eps=1e-6, |
| dropout=0.0, |
| drop_path_rate=0.0, |
| attention_dropout=0.0, |
| initializer_range=0.02, |
| initializer_factor=0.1, |
| **kwargs, |
| ): |
| super().__init__(**kwargs) |
|
|
| self.hidden_size = hidden_size |
| self.intermediate_size = intermediate_size |
| self.dropout = dropout |
| self.drop_path_rate = drop_path_rate |
| self.num_hidden_layers = num_hidden_layers |
| self.num_attention_heads = num_attention_heads |
| self.num_channels = num_channels |
| self.patch_size = patch_size |
| self.image_size = image_size |
| self.initializer_range = initializer_range |
| self.initializer_factor = initializer_factor |
| self.attention_dropout = attention_dropout |
| self.layer_norm_eps = layer_norm_eps |
| self.hidden_act = hidden_act |
| self.norm_type = norm_type |
| self.qkv_bias = qkv_bias |
| self.qk_normalization = qk_normalization |
| self.use_flash_attn = use_flash_attn |
|
|
| @classmethod |
| def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig": |
| config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs) |
|
|
| |
| if "vision_config" in config_dict: |
| config_dict = config_dict["vision_config"] |
|
|
| if ( |
| "model_type" in config_dict |
| and hasattr(cls, "model_type") |
| and config_dict["model_type"] != cls.model_type |
| ): |
| logger.warning( |
| f"You are using a model of type {config_dict['model_type']} to instantiate a model of type " |
| f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." |
| ) |
|
|
| return cls.from_dict(config_dict, **kwargs) |
|
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|
