|
|
|
|
|
|
|
|
|
|
|
|
| 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):
|
| r"""
|
| This is the configuration class to store the configuration of a [`InternVisionModel`]. It is used to
|
| instantiate a vision encoder according to the specified arguments, defining the model architecture.
|
|
|
| Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
| documentation from [`PretrainedConfig`] for more information.
|
|
|
| Args:
|
| num_channels (`int`, *optional*, defaults to 3):
|
| Number of color channels in the input images (e.g., 3 for RGB).
|
| patch_size (`int`, *optional*, defaults to 14):
|
| The size (resolution) of each patch.
|
| image_size (`int`, *optional*, defaults to 224):
|
| The size (resolution) of each image.
|
| qkv_bias (`bool`, *optional*, defaults to `False`):
|
| Whether to add a bias to the queries and values in the self-attention layers.
|
| hidden_size (`int`, *optional*, defaults to 3200):
|
| Dimensionality of the encoder layers and the pooler layer.
|
| num_attention_heads (`int`, *optional*, defaults to 25):
|
| Number of attention heads for each attention layer in the Transformer encoder.
|
| intermediate_size (`int`, *optional*, defaults to 12800):
|
| Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
|
| qk_normalization (`bool`, *optional*, defaults to `True`):
|
| Whether to normalize the queries and keys in the self-attention layers.
|
| num_hidden_layers (`int`, *optional*, defaults to 48):
|
| Number of hidden layers in the Transformer encoder.
|
| use_flash_attn (`bool`, *optional*, defaults to `True`):
|
| Whether to use flash attention mechanism.
|
| hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
|
| The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
|
| `"relu"`, `"selu"` and `"gelu_new"` ``"gelu"` are supported.
|
| layer_norm_eps (`float`, *optional*, defaults to 1e-6):
|
| The epsilon used by the layer normalization layers.
|
| dropout (`float`, *optional*, defaults to 0.0):
|
| The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
|
| drop_path_rate (`float`, *optional*, defaults to 0.0):
|
| Dropout rate for stochastic depth.
|
| attention_dropout (`float`, *optional*, defaults to 0.0):
|
| The dropout ratio for the attention probabilities.
|
| initializer_range (`float`, *optional*, defaults to 0.02):
|
| The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
| initializer_factor (`float`, *optional*, defaults to 0.1):
|
| A factor for layer scale.
|
| """
|
|
|
| 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)
|
|
|
