# coding=utf-8 # Copyright 2024 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 ...utils import ( logging, ) from ..auto import CONFIG_MAPPING, AutoConfig logger = logging.get_logger(__name__) class LlavaOnevisionConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`LlavaOnevisionForConditionalGeneration`]. It is used to instantiate an Llava-NeXT 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 [llava-hf/llava-onevision-qwen2-7b-ov-hf](https://huggingface.co/llava-hf/llava-onevision-qwen2-7b-ov-hf) model. 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 `SiglipVisionConfig`): 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_index (`int`, *optional*, defaults to 151646): The image token index to encode the image prompt. video_token_index (`int`, *optional*, defaults to 151647): The video token index to encode the video prompt. projector_hidden_act (`str`, *optional*, defaults to `"gelu"`): The activation function used by the multimodal projector. vision_feature_select_strategy (`str`, *optional*, defaults to `"full"`): The feature selection strategy used to select the vision feature from the vision backbone. Can be one of `"default"` or `"full"`. If `"default"`, the CLS token is removed from the vision features. If `"full"`, the full vision features are used. vision_feature_layer (`Union[int, List[int]]`, *optional*, defaults to -1): The index of the layer to select the vision feature. If multiple indices are provided, the vision feature of the corresponding indices will be concatenated to form the vision features. vision_aspect_ratio (`str`, *optional*, defaults to `"anyres_max_9"`): Aspect ratio used when processong image features. The default value is "anyres_max_9". image_grid_pinpoints (`List`, *optional*): A list of possible resolutions to use for processing high resolution images. Each item in the list should be a tuple or list of the form `(height, width)`. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether the model's input and output word embeddings should be tied. multimodal_projector_bias (`bool`, *optional*, defaults to `True`): Whether to use bias in the multimodal projector. Example: ```python >>> from transformers import LlavaOnevisionForConditionalGeneration, LlavaOnevisionConfig, SiglipVisionConfig, Qwen2Config >>> # Initializing a CLIP-vision config >>> vision_config = SiglipVisionConfig() >>> # Initializing a Llama config >>> text_config = Qwen2Config() >>> # Initializing a Llava-Next llava-hf/llava-onevision-qwen2-7b-ov-hf style configuration >>> configuration = LlavaOnevisionConfig(vision_config, text_config) >>> # Initializing a model from the llava-hf/llava-onevision-qwen2-7b-ov-hf style configuration >>> model = LlavaOnevisionForConditionalGeneration(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "llava_onevision" sub_configs = {"text_config": AutoConfig, "vision_config": AutoConfig} def __init__( self, vision_config=None, text_config=None, image_token_index=151646, video_token_index=151647, projector_hidden_act="gelu", vision_feature_select_strategy="full", vision_feature_layer=-1, vision_aspect_ratio="anyres_max_9", image_grid_pinpoints=None, tie_word_embeddings=False, multimodal_projector_bias=True, **kwargs, ): self.image_token_index = image_token_index self.video_token_index = video_token_index self.projector_hidden_act = projector_hidden_act self.multimodal_projector_bias = multimodal_projector_bias if vision_feature_select_strategy not in ["default", "full"]: raise ValueError( "vision_feature_select_strategy should be one of 'default', 'full'." f"Got: {vision_feature_select_strategy}" ) self.vision_feature_select_strategy = vision_feature_select_strategy self.vision_feature_layer = vision_feature_layer self.vision_aspect_ratio = vision_aspect_ratio image_grid_pinpoints = ( image_grid_pinpoints if image_grid_pinpoints is not None else [ [384, 384], [384, 768], [384, 1152], [384, 1536], [384, 1920], [384, 2304], [768, 384], [768, 768], [768, 1152], [768, 1536], [768, 1920], [768, 2304], [1152, 384], [1152, 768], [1152, 1152], [1152, 1536], [1152, 1920], [1152, 2304], [1536, 384], [1536, 768], [1536, 1152], [1536, 1536], [1536, 1920], [1536, 2304], [1920, 384], [1920, 768], [1920, 1152], [1920, 1536], [1920, 1920], [1920, 2304], [2304, 384], [2304, 768], [2304, 1152], [2304, 1536], [2304, 1920], [2304, 2304], ] ) self.image_grid_pinpoints = image_grid_pinpoints if isinstance(vision_config, dict): vision_config["model_type"] = ( vision_config["model_type"] if "model_type" in vision_config else "siglip_vision_model" ) vision_config = CONFIG_MAPPING[vision_config["model_type"]](**vision_config) elif vision_config is None: vision_config = CONFIG_MAPPING["siglip_vision_model"]( hidden_size=1152, intermediate_size=4304, patch_size=14, image_size=384, num_hidden_layers=26, num_attention_heads=14, vision_use_head=False, ) self.vision_config = vision_config 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__(tie_word_embeddings=tie_word_embeddings, **kwargs) __all__ = ["LlavaOnevisionConfig"]