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# coding=utf-8
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# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
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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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"""Idefics2 model configuration"""
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from ...configuration_utils import PretrainedConfig
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from ...utils import logging
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from ..auto import CONFIG_MAPPING, AutoConfig
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logger = logging.get_logger(__name__)
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class Idefics2VisionConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`Idefics2VisionModel`]. It is used to instantiate a
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Idefics2 vision encoder according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the SigLIP checkpoint
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[google/siglip-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224) used in the Idefics2 model
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[HuggingFaceM4/idefics2-8b](https://huggingface.co/HuggingFaceM4/idefics2-8b).
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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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hidden_size (`int`, *optional*, defaults to 768):
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Dimensionality of the encoder layers and the pooler layer.
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intermediate_size (`int`, *optional*, defaults to 3072):
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Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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num_hidden_layers (`int`, *optional*, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 12):
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Number of attention heads for each attention layer in the Transformer encoder.
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num_channels (`int`, *optional*, defaults to 3):
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Number of channels in the input images.
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image_size (`int`, *optional*, defaults to 224):
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The size (resolution) of each image.
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patch_size (`int`, *optional*, defaults to 32):
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The size (resolution) of each patch.
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hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
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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"` `"quick_gelu"` are supported.
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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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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation for initializing all weight matrices in the model.
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Example:
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```python
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>>> from transformers.models.idefics2.modeling_idefics2 import Idefics2VisionTransformer
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>>> from transformers.models.idefics2.configuration_idefics2 import Idefics2VisionConfig
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>>> # Initializing a Idefics2VisionConfig with google/siglip-base-patch16-224 style configuration
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>>> configuration = Idefics2VisionConfig()
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>>> # Initializing a Idefics2VisionTransformer (with random weights) from the google/siglip-base-patch16-224 style configuration
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>>> model = Idefics2VisionTransformer(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 = "idefics2_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=768,
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intermediate_size=3072,
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num_hidden_layers=12,
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num_attention_heads=12,
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num_channels=3,
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image_size=224,
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patch_size=32,
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hidden_act="gelu_pytorch_tanh",
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layer_norm_eps=1e-6,
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attention_dropout=0.0,
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initializer_range=0.02,
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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.intermediate_size = intermediate_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.num_channels = num_channels
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self.patch_size = patch_size
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self.image_size = image_size
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self.attention_dropout = attention_dropout
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self.layer_norm_eps = layer_norm_eps
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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class Idefics2PerceiverConfig(PretrainedConfig):
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r"""
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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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hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
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The non-linear activation function (function or string) in the perceiver block.
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hidden_size (`int`, *optional*, defaults to 4096):
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Dimension of the hidden representations.
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rms_norm_eps (`float`, *optional*, defaults to 1e-06):
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The epsilon used by the rms normalization layers.
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resampler_n_latents (`int`, *optional*, defaults to 64):
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Number of latent embeddings to resample ("compress") the input sequence to (usually < 128).
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resampler_depth (`int`, *optional*, defaults to 3):
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Depth of the Perceiver Resampler (Transformer w/ cross attention). Should be shallow (<= 3).
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resampler_n_heads (`int`, *optional*, defaults to 16):
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Number of heads in each Transformer block (for multi-headed self-attention).
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resampler_head_dim (`int`, *optional*, defaults to 96):
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Dimensionality of each head projection in the Transformer block.
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num_key_value_heads (`int`, *optional*, defaults to 4):
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Number of key-value heads in the perceiver attention block.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation for initializing all weight matrices in the model.
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"""
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model_type = "idefics2_perceiver"
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def __init__(
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self,
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hidden_act="silu",
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hidden_size=4096,
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rms_norm_eps=1e-06,
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resampler_n_latents=64,
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resampler_depth=3,
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resampler_n_heads=16,
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resampler_head_dim=96,
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num_key_value_heads=4,
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attention_dropout=0.0,
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initializer_range=0.02,
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**kwargs,
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):
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self.hidden_act = hidden_act
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self.hidden_size = hidden_size
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self.rms_norm_eps = rms_norm_eps
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self.resampler_n_latents = resampler_n_latents
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self.resampler_depth = resampler_depth
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self.resampler_n_heads = resampler_n_heads
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self.num_key_value_heads = num_key_value_heads
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self.resampler_head_dim = resampler_head_dim
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self.attention_dropout = attention_dropout
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self.initializer_range = initializer_range
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if self.num_key_value_heads > self.resampler_n_heads:
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raise ValueError(
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f"num_key_value_heads={self.num_key_value_heads} must be less than or equal to"
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f" resampler_n_heads={self.resampler_n_heads}"
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)
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super().__init__(**kwargs)
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class Idefics2Config(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`Idefics2Model`]. It is used to instantiate a
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Idefics2 model according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the model of the Idefics2
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[HuggingFaceM4/idefics2-8b](https://huggingface.co/HuggingFaceM4/idefics2-8b) architecture.
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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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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should cache the key/value pairs of the attention mechanism.
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image_token_id (`int`, *optional*, defaults to 32001):
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The id of the "image" token.
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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Whether or not to tie the word embeddings with the token embeddings.
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vision_config (`IdeficsVisionConfig` or `dict`, *optional*):
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Custom vision config or dict
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perceiver_config (`IdeficsPerceiverConfig` or `dict`, *optional*):
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Custom perceiver config or dict
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text_config (`MistralConfig` or `dict`, *optional*):
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Custom text config or dict for the text model
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Example:
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```python
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>>> from transformers import Idefics2Model, Idefics2Config
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>>> # Initializing configuration
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>>> configuration = Idefics2Config()
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>>> # Initializing a model from the configuration
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>>> model = Idefics2Model(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 = "idefics2"
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sub_configs = {
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"text_config": AutoConfig,
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"perceiver_config": Idefics2PerceiverConfig,
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"vision_config": Idefics2VisionConfig,
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}
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def __init__(
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self,
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use_cache=True,
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image_token_id=32_001,
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tie_word_embeddings=False,
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vision_config=None,
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perceiver_config=None,
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text_config=None,
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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.use_cache = use_cache
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self.tie_word_embeddings = tie_word_embeddings
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if perceiver_config is None:
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self.perceiver_config = Idefics2PerceiverConfig()
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logger.info("perciver_config is None, using default perceiver config")
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elif isinstance(perceiver_config, dict):
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self.perceiver_config = Idefics2PerceiverConfig(**perceiver_config)
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elif isinstance(perceiver_config, Idefics2PerceiverConfig):
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self.perceiver_config = perceiver_config
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if vision_config is None:
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self.vision_config = Idefics2VisionConfig()
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logger.info("vision_config is None, using default vision config")
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elif isinstance(vision_config, dict):
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self.vision_config = Idefics2VisionConfig(**vision_config)
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elif isinstance(vision_config, Idefics2VisionConfig):
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self.vision_config = vision_config
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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 "mistral"
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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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logger.info("text_config is None, using default text config")
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text_config = CONFIG_MAPPING["mistral"](
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max_position_embeddings=4096 * 8,
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rms_norm_eps=1e-5,
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# None in the original configuration_mistral, we set it to the unk_token_id
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pad_token_id=0,
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tie_word_embeddings=False,
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)
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self.text_config = text_config
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if self.text_config.hidden_size != self.perceiver_config.hidden_size:
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self.perceiver_config.hidden_size = self.text_config.hidden_size
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self.perceiver_config.rms_norm_eps = self.text_config.rms_norm_eps
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logger.warning_once(
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"Perceiver config has a different `hidden_size` than text config, which means default values were used. "
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"In your model's config on the hub, add `hidden_size` and `rms_norm_eps` keys under the `perceiver_config` dict. "
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)
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super().__init__(**kwargs, tie_word_embeddings=tie_word_embeddings)
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__all__ = ["Idefics2Config"]
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