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| # -------------------------------------------------------- | |
| # InternVL | |
| # Copyright (c) 2023 OpenGVLab | |
| # Licensed under The MIT License [see LICENSE for details] | |
| # -------------------------------------------------------- | |
| 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 | |
| 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) |