# 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 huggingface_hub.dataclasses import strict from ...configuration_utils import PreTrainedConfig from ...utils import auto_docstring, logging logger = logging.get_logger(__name__) @auto_docstring(checkpoint="microsoft/kosmos-2-patch14-224") @strict class Kosmos2TextConfig(PreTrainedConfig): 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", } vocab_size: int = 65037 max_position_embeddings: int = 2048 embed_dim: int = 2048 layers: int = 24 ffn_dim: int = 8192 attention_heads: int = 32 activation_function: str = "gelu" dropout: float | int = 0.1 attention_dropout: float | int = 0.1 activation_dropout: float | int = 0.0 layerdrop: float | int = 0.0 layer_norm_eps: float = 1e-5 init_std: float = 0.02 scale_embedding: bool = True use_cache: bool = True pad_token_id: int | None = 1 bos_token_id: int | None = 0 eos_token_id: int | list[int] | None = 2 add_cross_attention: bool = False @auto_docstring(checkpoint="microsoft/kosmos-2-patch14-224") @strict class Kosmos2VisionConfig(PreTrainedConfig): model_type = "kosmos_2_vision_model" base_config_key = "vision_config" hidden_size: int = 1024 intermediate_size: int = 4096 num_hidden_layers: int = 24 num_attention_heads: int = 16 num_channels: int = 3 image_size: int | list[int] | tuple[int, int] = 224 patch_size: int | list[int] | tuple[int, int] = 14 hidden_act: str = "quick_gelu" layer_norm_eps: float = 1e-5 attention_dropout: float | int = 0.0 initializer_range: float = 0.02 initializer_factor: float = 1.0 @auto_docstring(checkpoint="microsoft/kosmos-2-patch14-224") @strict class Kosmos2Config(PreTrainedConfig): r""" 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. 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} text_config: dict | PreTrainedConfig | None = None vision_config: dict | PreTrainedConfig | None = None latent_query_num: int = 64 tie_word_embeddings: bool = True def __post_init__(self, **kwargs): if self.text_config is None: self.text_config = Kosmos2TextConfig() logger.info("`text_config` is `None`. initializing the `Kosmos2TextConfig` with default values.") elif isinstance(self.text_config, dict): self.text_config = Kosmos2TextConfig(**self.text_config) if self.vision_config is None: self.vision_config = Kosmos2VisionConfig() logger.info("`vision_config` is `None`. initializing the `Kosmos2VisionConfig` with default values.") elif isinstance(self.vision_config, dict): self.vision_config = Kosmos2VisionConfig(**self.vision_config) super().__post_init__(**kwargs) __all__ = ["Kosmos2Config"]