| | |
| | from typing import Any |
| |
|
| | from transformers.configuration_utils import PretrainedConfig |
| |
|
| | __all__ = ["AIMv2Config"] |
| |
|
| |
|
| | class AIMv2Config(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 = "aimv2" |
| |
|
| | def __init__( |
| | self, |
| | hidden_size: int = 1024, |
| | intermediate_size: int = 2816, |
| | num_hidden_layers: int = 24, |
| | num_attention_heads: int = 8, |
| | num_channels: int = 3, |
| | image_size: int = 224, |
| | patch_size: int = 14, |
| | rms_norm_eps: float = 1e-5, |
| | attention_dropout: float = 0.0, |
| | projection_dropout: float = 0.0, |
| | qkv_bias: bool = False, |
| | use_bias: bool = False, |
| | **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.patch_size = patch_size |
| | self.image_size = image_size |
| | self.attention_dropout = attention_dropout |
| | self.rms_norm_eps = rms_norm_eps |
| |
|
| | self.projection_dropout = projection_dropout |
| | self.qkv_bias = qkv_bias |
| | self.use_bias = use_bias |
| |
|
