|
import os |
|
from typing import Union |
|
|
|
from transformers.configuration_utils import PretrainedConfig |
|
from transformers.utils import logging |
|
|
|
logger = logging.get_logger(__name__) |
|
|
|
|
|
class InternVisionConfig(PretrainedConfig): |
|
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) |
|
|
