| | |
| | |
| | |
| | |
| | |
| |
|
| | 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) |
| |
|
