Update configuration_intern_vit.py

configuration_intern_vit.py

@@ -1,117 +1,119 @@
-# --------------------------------------------------------
-# 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',
- 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.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)
+# --------------------------------------------------------
+# 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
+
+ @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)