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