| from typing import Any, Optional, List, Union | |
| from transformers import Qwen3Config | |
| from transformers.configuration_utils import PretrainedConfig | |
| __all__ = ["Siglip2NavitConfig", "Ovis2_5_Config"] | |
| class Siglip2NavitConfig(PretrainedConfig): | |
| """This is the configuration class to store the configuration of an [`AIMv2Model`]. | |
| Instantiating a configuration with the defaults will yield a similar configuration | |
| to that of the [apple/aimv2-large-patch14-224](https://huggingface.co/apple/aimv2-large-patch14-224). | |
| Args: | |
| hidden_size: Dimension of the hidden representations. | |
| intermediate_size: Dimension of the SwiGLU representations. | |
| num_hidden_layers: Number of hidden layers in the Transformer. | |
| num_attention_heads: Number of attention heads for each attention layer | |
| in the Transformer. | |
| num_channels: Number of input channels. | |
| image_size: Image size. | |
| patch_size: Patch size. | |
| rms_norm_eps: Epsilon value used for the RMS normalization layer. | |
| attention_dropout: Dropout ratio for attention probabilities. | |
| projection_dropout: Dropout ratio for the projection layer after the attention. | |
| qkv_bias: Whether to add a bias to the queries, keys and values. | |
| use_bias: Whether to add a bias in the feed-forward and projection layers. | |
| kwargs: Keyword arguments for the [`PretrainedConfig`]. | |
| """ | |
| model_type: str = "siglip2_navit" | |
| def __init__( | |
| self, | |
| hidden_size: int = 1024, | |
| intermediate_size: int = 4096, | |
| num_hidden_layers: int = 24, | |
| num_attention_heads: int = 16, | |
| num_channels: int = 3, | |
| num_patches: int = -1, | |
| image_size: int = 512, | |
| patch_size: int = 16, | |
| hidden_act: str="gelu_pytorch_tanh", | |
| layer_norm_eps: float = 1e-6, | |
| attention_dropout: float = 0.0, | |
| hidden_stride: int = 2, | |
| window_size: int = 112, | |
| fullatt_block_indexes: Optional[list] = None, | |
| temporal_patch_size: int = 1, | |
| preserve_original_pe: bool = True, | |
| use_rope: bool = True, | |
| **kwargs: Any, | |
| ): | |
| 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.num_patches = num_patches | |
| self.patch_size = patch_size | |
| self.image_size = image_size | |
| self.hidden_act = hidden_act | |
| self.attention_dropout = attention_dropout | |
| self.layer_norm_eps = layer_norm_eps | |
| self.hidden_stride = hidden_stride | |
| self.window_size = window_size | |
| self.fullatt_block_indexes = fullatt_block_indexes | |
| self.temporal_patch_size = temporal_patch_size | |
| self.preserve_original_pe = preserve_original_pe | |
| self.use_rope = use_rope | |
| class Ovis2_5_Config(PretrainedConfig): | |
| model_type = "ovis2_5" | |
| sub_configs = dict(llm_config=Qwen3Config, vit_config=Siglip2NavitConfig) | |
| def __init__(self, | |
| llm_config: Optional[Union[Qwen3Config, dict]] = None, | |
| vit_config: Optional[Union[Siglip2NavitConfig, dict]] = None, | |
| visual_vocab_size=65536, | |
| hidden_size=None, | |
| **kwargs | |
| ): | |
| super().__init__(**kwargs) | |
| if isinstance(llm_config, dict): | |
| llm_config = Qwen3Config(**llm_config) | |
| self.llm_config = llm_config | |
| if isinstance(vit_config, dict): | |
| vit_config = Siglip2NavitConfig(**vit_config) | |
| self.vit_config = vit_config | |
| self.visual_vocab_size = visual_vocab_size | |
| self.hidden_size = hidden_size | |
| if kwargs.get('attn_implementation'): | |
| self.llm_config._attn_implementation = kwargs['attn_implementation'] | |
| self.vit_config._attn_implementation = kwargs['attn_implementation'] | |