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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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