# coding=utf-8 # Copyright 2024 Microsoft Research and 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. """KOSMOS-2.5 model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) class Kosmos2_5TextConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Kosmos2_5TextModel`]. It is used to instantiate a KOSMOS-2.5 text decoder according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the text decoder of the KOSMOS-2.5 [microsoft/kosmos-2.5](https://huggingface.co/microsoft/kosmos-2.5) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 108481): Vocabulary size of the Kosmos2_5 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Kosmos2_5Model`]. max_position_embeddings (`int`, *optional*, defaults to 4096): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). embed_dim (`int`, *optional*, defaults to 1536): Dimensionality of the layers and the pooler layer. layers (`int`, *optional*, defaults to 24): Number of hidden layers in the Transformer encoder. ffn_dim (`int`, *optional*, defaults to 6144): Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. activation_function (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. dropout (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. activation_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for activations inside the fully connected layer. layerdrop (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://huggingface.co/papers/1909.11556) for more details. layer_norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon used by the layer normalization layers. init_std (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. scale_embedding (`bool`, *optional*, defaults to `True`): Scale embeddings by diving by sqrt(embed_dim). use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). ```""" model_type = "kosmos_2_5_text_model" base_config_key = "text_config" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = { "num_attention_heads": "attention_heads", "hidden_size": "embed_dim", "num_hidden_layers": "layers", } def __init__( self, vocab_size=108481, max_position_embeddings=4096, embed_dim=1536, layers=24, ffn_dim=6144, attention_heads=16, activation_function="gelu", dropout=0.1, attention_dropout=0.0, activation_dropout=0.0, layerdrop=0.0, layer_norm_eps=1e-5, init_std=0.02, scale_embedding=True, use_cache=True, pad_token_id=1, bos_token_id=0, eos_token_id=2, **kwargs, ): super().__init__( pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs, ) self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.embed_dim = embed_dim self.layers = layers self.ffn_dim = ffn_dim self.attention_heads = attention_heads self.activation_function = activation_function self.dropout = dropout self.attention_dropout = attention_dropout self.activation_dropout = activation_dropout self.layerdrop = layerdrop self.layer_norm_eps = layer_norm_eps self.init_std = init_std self.scale_embedding = scale_embedding self.use_cache = use_cache class Kosmos2_5VisionConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Kosmos2_5VisionModel`]. It is used to instantiate a KOSMOS-2.5 vision encoder according to the specified arguments, defining the model architecture. Instantiating a configuration defaults will yield a similar configuration to that of the vision encoder of the KOSMOS-2.5 [microsoft/kosmos-2.5](https://huggingface.co/microsoft/kosmos-2.5) architecture. 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 1536): Dimensionality of the encoder layers and the pooler layer. patch_embed_hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the input patch_embedding layer in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 3968): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. head_dim (`int`, *optional*, defaults to 64): Dimensionality of the key, query, value projections per attention head. num_hidden_layers (`int`, *optional*, defaults to 18): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 24): Number of attention heads for each attention layer in the Transformer encoder. dense_act_fn (`str` or `function`, *optional*, defaults to `"gelu_new"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` ``"gelu"` are supported. layer_norm_eps (`float`, *optional*, defaults to 1e-06): The epsilon used by the layer normalization layers. dropout_rate (`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): The dropout ratio for the attention probabilities. max_num_patches (`int`, *optional*, defaults to 4096): Maximum sequence length (here number of patches) supported by the model. Example: ```python >>> from transformers import Kosmos2_5VisionConfig, Kosmos2_5VisionModel >>> # Initializing a Kosmos2_5VisionConfig with microsoft/kosmos-2.5 style configuration >>> configuration = Kosmos2_5VisionConfig() >>> # Initializing a Kosmos2_5VisionModel (with random weights) from the microsoft/kosmos-2.5 style configuration >>> model = Kosmos2_5VisionModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "kosmos_2_5_vision_model" base_config_key = "vision_config" def __init__( self, hidden_size=1536, patch_embed_hidden_size=768, intermediate_size=3968, head_dim=64, num_hidden_layers=18, num_attention_heads=24, dense_act_fn="gelu_new", layer_norm_eps=1e-6, dropout_rate=0.0, attention_dropout=0.0, max_num_patches=4096, initializer_factor=1.0, initializer_range=0.02, **kwargs, ): super().__init__(**kwargs) self.hidden_size = hidden_size self.patch_embed_hidden_size = patch_embed_hidden_size self.intermediate_size = intermediate_size self.dropout_rate = dropout_rate self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.attention_dropout = attention_dropout self.layer_norm_eps = layer_norm_eps self.dense_act_fn = dense_act_fn self.max_num_patches = max_num_patches self.head_dim = head_dim self.initializer_factor = initializer_factor self.initializer_range = initializer_range class Kosmos2_5Config(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Kosmos2_5Model`]. It is used to instantiate a KOSMOS-2.5 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 KOSMOS-2.5 [microsoft/kosmos-2.5](https://huggingface.co/microsoft/kosmos-2.5) architecture. Args: text_config (`dict`, *optional*): Dictionary of configuration options used to initialize [`Kosmos2_5TextConfig`]. vision_config (`dict`, *optional*): Dictionary of configuration options used to initialize [`Kosmos2_5VisionConfig`]. latent_query_num (`int`, *optional*, defaults to 2048): The number of latent query tokens that represent the image features used in the text decoder component. kwargs (*optional*): Dictionary of keyword arguments. """ model_type = "kosmos-2.5" sub_configs = {"text_config": Kosmos2_5TextConfig, "vision_config": Kosmos2_5VisionConfig} def __init__( self, text_config=None, vision_config=None, latent_query_num=2048, **kwargs, ): super().__init__(**kwargs) if text_config is None: text_config = {} logger.info("text_config is None. Initializing the Kosmos2_5TextConfig with default values.") if vision_config is None: vision_config = {} logger.info("vision_config is None. Initializing the Kosmos2_5VisionConfig with default values.") self.text_config = Kosmos2_5TextConfig(**text_config) self.vision_config = Kosmos2_5VisionConfig(**vision_config) self.latent_query_num = latent_query_num __all__ = ["Kosmos2_5Config"]