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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 InternVisionConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`InternVisionModel`]. It is used to
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instantiate a vision encoder according to the specified arguments, defining the model architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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num_channels (`int`, *optional*, defaults to 3):
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Number of color channels in the input images (e.g., 3 for RGB).
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patch_size (`int`, *optional*, defaults to 14):
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The size (resolution) of each patch.
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image_size (`int`, *optional*, defaults to 224):
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The size (resolution) of each image.
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qkv_bias (`bool`, *optional*, defaults to `False`):
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Whether to add a bias to the queries and values in the self-attention layers.
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hidden_size (`int`, *optional*, defaults to 3200):
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Dimensionality of the encoder layers and the pooler layer.
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num_attention_heads (`int`, *optional*, defaults to 25):
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Number of attention heads for each attention layer in the Transformer encoder.
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intermediate_size (`int`, *optional*, defaults to 12800):
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Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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qk_normalization (`bool`, *optional*, defaults to `True`):
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Whether to normalize the queries and keys in the self-attention layers.
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num_hidden_layers (`int`, *optional*, defaults to 48):
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Number of hidden layers in the Transformer encoder.
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use_flash_attn (`bool`, *optional*, defaults to `True`):
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Whether to use flash attention mechanism.
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hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"selu"` and `"gelu_new"` ``"gelu"` are supported.
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layer_norm_eps (`float`, *optional*, defaults to 1e-6):
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The epsilon used by the layer normalization layers.
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dropout (`float`, *optional*, defaults to 0.0):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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drop_path_rate (`float`, *optional*, defaults to 0.0):
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Dropout rate for stochastic depth.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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initializer_factor (`float`, *optional*, defaults to 0.1):
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A factor for layer scale.
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"""
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model_type = 'intern_vit_6b'
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def __init__(
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self,
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num_channels=3,
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patch_size=14,
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image_size=224,
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qkv_bias=False,
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hidden_size=3200,
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num_attention_heads=25,
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intermediate_size=12800,
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qk_normalization=True,
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num_hidden_layers=48,
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use_flash_attn=True,
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hidden_act='gelu',
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norm_type='rms_norm',
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layer_norm_eps=1e-6,
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dropout=0.0,
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drop_path_rate=0.0,
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attention_dropout=0.0,
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initializer_range=0.02,
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initializer_factor=0.1,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.dropout = dropout
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self.drop_path_rate = drop_path_rate
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.num_channels = num_channels
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self.patch_size = patch_size
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self.image_size = image_size
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self.initializer_range = initializer_range
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self.initializer_factor = initializer_factor
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self.attention_dropout = attention_dropout
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self.layer_norm_eps = layer_norm_eps
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self.hidden_act = hidden_act
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self.norm_type = norm_type
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self.qkv_bias = qkv_bias
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self.qk_normalization = qk_normalization
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self.use_flash_attn = use_flash_attn
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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 'vision_config' in config_dict:
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config_dict = config_dict['vision_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)
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