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