# coding=utf-8 # Copyright 2023 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 model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) class Kosmos2TextConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Kosmos2TextModel`]. It is used to instantiate a KOSMOS-2 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 [microsoft/kosmos-2-patch14-224](https://huggingface.co/microsoft/kosmos-2-patch14-224) 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 65037): Vocabulary size of the Kosmos2 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Kosmos2Model`]. max_position_embeddings (`int`, *optional*, defaults to 2048): 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 2048): 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 8192): Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. attention_heads (`int`, *optional*, defaults to 32): 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 0.1): 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://arxiv.org/abs/1909.11556) for more details. layer_norm_eps (`float`, *optional*, defaults to 1e-05): 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). pad_token_id (`int`, *optional*, defaults to 1): Token id used for padding. bos_token_id (`int`, *optional*, defaults to 0): Token id used for beginning of string. eos_token_id (`int`, *optional*, defaults to 2): Token id used for end of string. ```""" model_type = "kosmos_2_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=65037, max_position_embeddings=2048, embed_dim=2048, layers=24, ffn_dim=8192, attention_heads=32, activation_function="gelu", dropout=0.1, attention_dropout=0.1, 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 Kosmos2VisionConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Kosmos2VisionModel`]. It is used to instantiate a KOSMOS-2 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 vision encoder of the KOSMOS-2 [microsoft/kosmos-2-patch14-224](https://huggingface.co/microsoft/kosmos-2-patch14-224) 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 1024): Dimensionality of the encoder layers and the pooler layer. intermediate_size (`int`, *optional*, defaults to 4096): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. num_hidden_layers (`int`, *optional*, defaults to 24): 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): The number of input channels. image_size (`int`, *optional*, defaults to 224): The size (resolution) of each image. patch_size (`int`, *optional*, defaults to 14): The size (resolution) of each patch. hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`): 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-05): 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. initializer_factor (`float`, *optional*, defaults to 1.0): A factor for initializing all weight matrices (should be kept to 1, used internally for initialization testing). ```""" model_type = "kosmos_2_vision_model" base_config_key = "vision_config" def __init__( self, hidden_size=1024, intermediate_size=4096, num_hidden_layers=24, num_attention_heads=16, num_channels=3, image_size=224, patch_size=14, hidden_act="quick_gelu", layer_norm_eps=1e-5, attention_dropout=0.0, initializer_range=0.02, initializer_factor=1.0, **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.initializer_range = initializer_range self.initializer_factor = initializer_factor self.attention_dropout = attention_dropout self.layer_norm_eps = layer_norm_eps self.hidden_act = hidden_act class Kosmos2Config(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Kosmos2Model`]. It is used to instantiate a KOSMOS-2 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 [microsoft/kosmos-2-patch14-224](https://huggingface.co/microsoft/kosmos-2-patch14-224) architecture. Args: text_config (`dict`, *optional*): Dictionary of configuration options used to initialize [`Kosmos2TextConfig`]. vision_config (`dict`, *optional*): Dictionary of configuration options used to initialize [`Kosmos2VisionConfig`]. latent_query_num (`int`, *optional*, defaults to 64): The number of latent query tokens that represent the image features used in the text decoder component. kwargs (*optional*): Dictionary of keyword arguments. Example: ```python >>> from transformers import Kosmos2Config, Kosmos2Model >>> # Initializing a Kosmos-2 kosmos-2-patch14-224 style configuration >>> configuration = Kosmos2Config() >>> # Initializing a model (with random weights) from the kosmos-2-patch14-224 style configuration >>> model = Kosmos2Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "kosmos-2" sub_configs = {"text_config": Kosmos2TextConfig, "vision_config": Kosmos2VisionConfig} def __init__( self, text_config=None, vision_config=None, latent_query_num=64, **kwargs, ): super().__init__(**kwargs) if text_config is None: text_config = {} logger.info("`text_config` is `None`. Initializing the `Kosmos2TextConfig` with default values.") if vision_config is None: vision_config = {} logger.info("`vision_config` is `None`. Initializing the `Kosmos2VisionConfig` with default values.") self.text_config = Kosmos2TextConfig(**text_config) self.vision_config = Kosmos2VisionConfig(**vision_config) self.latent_query_num = latent_query_num __all__ = ["Kosmos2Config"]