# coding=utf-8 # Copyright 2024 The 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. """Idefics3 model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging from ..auto import CONFIG_MAPPING, AutoConfig logger = logging.get_logger(__name__) class Idefics3VisionConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Idefics3VisionModel`]. It is used to instantiate a Idefics3 vision encoder according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the SigLIP checkpoint [google/siglip-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224) used in the Idefics3 model [HuggingFaceM4/Idefics3-8B-Llama3](https://huggingface.co/HuggingFaceM4/Idefics3-8B-Llama3). Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: hidden_size (`int`, *optional*, defaults to 1152): Dimensionality of the encoder layers and the pooler layer. intermediate_size (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. num_hidden_layers (`int`, *optional*, defaults to 12): 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. num_channels (`int`, *optional*, defaults to 3): Number of channels in the input images. image_size (`int`, *optional*, defaults to 224): The size (resolution) of each image. patch_size (`int`, *optional*, defaults to 32): The size (resolution) of each patch. hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported. layer_norm_eps (`float`, *optional*, defaults to 1e-06): The epsilon used by the layer normalization layers. 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. Example: ```python >>> from transformers.models.idefics3.modeling_idefics3 import Idefics3VisionTransformer >>> from transformers.models.idefics3.configuration_idefics3 import Idefics3VisionConfig >>> # Initializing a Idefics3VisionConfig with google/siglip-base-patch16-224 style configuration >>> configuration = Idefics3VisionConfig() >>> # Initializing a Idefics3VisionTransformer (with random weights) from the google/siglip-base-patch16-224 style configuration >>> model = Idefics3VisionTransformer(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "idefics3_vision" base_config_key = "vision_config" def __init__( self, hidden_size=1152, intermediate_size=3072, num_hidden_layers=12, num_attention_heads=16, num_channels=3, image_size=224, patch_size=32, hidden_act="gelu_pytorch_tanh", layer_norm_eps=1e-6, attention_dropout=0.0, initializer_range=0.02, **kwargs, ): super().__init__(**kwargs) self.hidden_size = hidden_size self.intermediate_size = intermediate_size 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.attention_dropout = attention_dropout self.layer_norm_eps = layer_norm_eps self.hidden_act = hidden_act self.initializer_range = initializer_range class Idefics3Config(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Idefics3Model`]. It is used to instantiate a Idefics3 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 model of the Idefics3 [HuggingFaceM4/Idefics3-8B-Llama3](https://huggingface.co/HuggingFaceM4/Idefics3-8B-Llama3) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should cache the key/value pairs of the attention mechanism. Only relevant if `config.is_decoder=True`. image_token_id (`int`, *optional*, defaults to 128257): The id of the "image" token. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether or not to tie the word embeddings with the token embeddings. vision_config (`IdeficsVisionConfig` or `dict`, *optional*, defaults to `IdeficsVisionConfig`): Custom vision config or dict for the vision tower text_config (`PretrainedConfig` or `dict`, *optional*, defaults to `LlamaConfig`): Custom text config or dict for the text model scale_factor (`int`, *optional*, defaults to 2): The scale factor for the image encoder. pad_token_id (`int`, *optional*, defaults to 128002): The id of the padding token. Example: ```python >>> from transformers import Idefics3Model, Idefics3Config >>> # Initializing configuration >>> configuration = Idefics3Config() >>> # Initializing a model from the configuration >>> model = Idefics3Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "idefics3" sub_configs = {"text_config": AutoConfig, "vision_config": Idefics3VisionConfig} def __init__( self, use_cache=True, image_token_id=128257, tie_word_embeddings=False, vision_config=None, text_config=None, scale_factor=2, pad_token_id=128_002, **kwargs, ): self.image_token_id = image_token_id self.use_cache = use_cache self.tie_word_embeddings = tie_word_embeddings if vision_config is None: self.vision_config = Idefics3VisionConfig() logger.info("vision_config is None, using default vision config") elif isinstance(vision_config, dict): self.vision_config = Idefics3VisionConfig(**vision_config) elif isinstance(vision_config, Idefics3VisionConfig): 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 "llama" text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config) elif text_config is None: logger.info("text_config is None, using default text config") text_config = CONFIG_MAPPING["llama"]( rms_norm_eps=1e-5, pad_token_id=pad_token_id, tie_word_embeddings=False, ) self.text_config = text_config self.scale_factor = scale_factor super().__init__(**kwargs, pad_token_id=pad_token_id, tie_word_embeddings=tie_word_embeddings) __all__ = ["Idefics3Config", "Idefics3VisionConfig"]