# coding=utf-8 # Copyright 2025 HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from ...configuration_utils import PretrainedConfig from ..auto import CONFIG_MAPPING, AutoConfig class InternVLVisionConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`InternVLVisionModel`]. It is used to instantiate an InternVLVisionModel model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the InternVL3-1B. e.g. [OpenGVLab/InternVL3-1B-hf](https://huggingface.co/OpenGVLab/InternVL3-1B-hf) Args: hidden_size (`int`, *optional*, defaults to 1024): Dimensionality of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 24): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. attention_bias (`bool`, *optional*, defaults to `False`): Whether to add a bias to the queries, keys and values. use_qk_norm (`bool`, *optional*, defaults to `False`): Whether to apply normalization to the queries and keys before the attention operation. intermediate_size (`int`, *optional*, defaults to 4096): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. 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"` are supported. hidden_dropout_prob (`float`, *optional*, defaults to 0.0): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (`float`, *optional*, defaults to 0.0): Dropout probability for attention weights. projection_dropout (`float`, *optional*, defaults to 0.0): Dropout probability for the projection layer. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. norm_type (`str`, *optional*, defaults to `"layer_norm"`): The type of normalization to use in the encoder. Can be `"layer_norm"` or `"rms_norm"`. layer_norm_eps (`float`, *optional*, defaults to 1e-06): The epsilon used by the layer normalization layers. image_size (`int` or `list[int]`, *optional*, defaults to `[448, 448]`): The size (resolution) of each image. patch_size (`int` or `list[int]`, *optional*, defaults to `[14, 14]`): The size (resolution) of each patch. num_channels (`int`, *optional*, defaults to 3): The number of input channels. use_mask_token (`bool`, *optional*, defaults to `False`): Whether to use a mask token for masked image modeling. use_absolute_position_embeddings (`bool`, *optional*, defaults to `True`): Whether to use BERT-style absolute position embeddings. layer_scale_init_value (`float`, *optional*, defaults to 0.1): Scale to use in the self-attention layers. 0.1 for base, 1e-5 for large. Set 0 to disable layer scale. use_mean_pooling (`bool`, *optional*, defaults to `True`): Whether to mean pool the final hidden states of the patches instead of using the final hidden state of the CLS token, before applying the classification head. Example: ```python >>> from transformers import InternVLVisionConfig, InternVLVisionModel >>> # Initializing a InternVLVisionModel OpenGVLab/InternVL3-1B-hf style configuration >>> configuration = InternVLVisionConfig() >>> # Initializing a model (with random weights) from the OpenGVLab/InternVL3-1B-hf configuration >>> model = InternVLVisionModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "internvl_vision" base_config_key = "vision_config" def __init__( self, hidden_size=1024, num_hidden_layers=24, num_attention_heads=16, attention_bias=False, use_qk_norm=False, intermediate_size=4096, hidden_act="gelu", hidden_dropout_prob=0.0, attention_dropout=0.0, projection_dropout=0.0, initializer_range=0.02, norm_type="layer_norm", layer_norm_eps=1e-06, image_size=[448, 448], patch_size=[14, 14], num_channels=3, use_mask_token=False, use_absolute_position_embeddings=True, layer_scale_init_value=0.1, use_mean_pooling=True, **kwargs, ): super().__init__(**kwargs) self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.attention_bias = attention_bias self.use_qk_norm = use_qk_norm self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_dropout = attention_dropout self.projection_dropout = projection_dropout self.initializer_range = initializer_range self.norm_type = norm_type self.layer_norm_eps = layer_norm_eps image_size = image_size if isinstance(image_size, (list, tuple)) else (image_size, image_size) patch_size = patch_size if isinstance(patch_size, (list, tuple)) else (patch_size, patch_size) self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.use_mask_token = use_mask_token self.use_absolute_position_embeddings = use_absolute_position_embeddings self.layer_scale_init_value = layer_scale_init_value self.use_mean_pooling = use_mean_pooling class InternVLConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`InternVLForConditionalGeneration`]. It is used to instantiate a InternVL model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of InternVL3-1B. e.g. [OpenGVLab/InternVL3-1B-hf](https://huggingface.co/OpenGVLab/InternVL3-1B-hf) Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vision_config (`Union[AutoConfig, dict]`, *optional*, defaults to `InternVisonConfig`): The config object or dictionary of the vision backbone. text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `Qwen2Config`): The config object or dictionary of the text backbone. image_token_id (`int`, *optional*, defaults to 151667): The image token index to encode the image prompt. image_seq_length (`int`, *optional*, defaults to 256): Number of image tokens to use per image patch. downsample_ratio (`float`, *optional*, defaults to 0.5): Factor by which to downsample the image. projector_hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the projector. vision_feature_layer (`int`, *optional*, defaults to -1): The index of the layer to use as the image features. vision_feature_select_strategy (`str`, *optional*, defaults to `"default"`): The feature selection strategy used to select the vision feature from the vision backbone. Can be one of `"default"` or `"full"`. ```python >>> from transformers import InternVLForConditionalGeneration, InternVLConfig >>> # Initializing a InternVL style configuration >>> configuration = InternVLConfig() >>> # Initializing a model (with random weights) from the OpenGVLab/InternVL3-1B-hf configuration >>> model = InternVLForConditionalGeneration(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "internvl" sub_configs = {"text_config": AutoConfig, "vision_config": InternVLVisionConfig} def __init__( self, vision_config=None, text_config=None, image_token_id=151667, image_seq_length=256, downsample_ratio=0.5, projector_hidden_act="gelu", vision_feature_layer=-1, vision_feature_select_strategy="default", **kwargs, ): self.image_token_id = image_token_id self.image_seq_length = image_seq_length self.downsample_ratio = downsample_ratio self.projector_hidden_act = projector_hidden_act self.vision_feature_layer = vision_feature_layer self.vision_feature_select_strategy = vision_feature_select_strategy if isinstance(vision_config, dict): self.vision_config = InternVLVisionConfig(**vision_config) elif isinstance(vision_config, InternVLVisionConfig): self.vision_config = vision_config elif vision_config is None: self.vision_config = InternVLVisionConfig() if isinstance(text_config, dict): text_config["model_type"] = text_config["model_type"] if "model_type" in text_config else "qwen2" text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config) elif text_config is None: text_config = CONFIG_MAPPING["qwen2"]() self.text_config = text_config super().__init__(**kwargs) __all__ = ["InternVLVisionConfig", "InternVLConfig"]