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from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
class OneVisionEncoderConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`OneVisionEncoderModel`]. It is used to instantiate a
OneVision Encoder 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 OneVision Encoder 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 16):
The size (resolution) of each patch.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler.
layer_norm_eps (`float`, *optional*, defaults to 1e-6):
The epsilon used by the layer normalization layers.
layer_norm_type (`str`, *optional*, defaults to `"layer_norm"`):
The type of layer normalization to use. Supported values: `"layer_norm"`, `"rms_norm"`.
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.
rope_theta (`float`, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings.
use_head (`bool`, *optional*, defaults to `True`):
Whether to use the pooling head.
Example:
```python
>>> from configuration_onevision_encoder import OneVisionEncoderConfig
>>> from modeling_onevision_encoder import OneVisionEncoderModel
>>> # Initializing a OneVisionEncoder configuration
>>> configuration = OneVisionEncoderConfig()
>>> # Initializing a model (with random weights) from the configuration
>>> model = OneVisionEncoderModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```
"""
model_type = "onevision_encoder"
def __init__(
self,
hidden_size=1024,
intermediate_size=4096,
num_hidden_layers=24,
num_attention_heads=16,
num_channels=3,
image_size=448,
patch_size=16,
hidden_act="gelu",
layer_norm_eps=1e-6,
layer_norm_type="layer_norm",
attention_dropout=0.0,
initializer_range=0.02,
rope_theta=10000.0,
rope_temporal_size=64,
use_head=True,
**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.image_size = image_size
self.patch_size = patch_size
self.hidden_act = hidden_act
self.layer_norm_eps = layer_norm_eps
self.layer_norm_type = layer_norm_type
self.attention_dropout = attention_dropout
self.initializer_range = initializer_range
self.rope_theta = rope_theta
self.rope_temporal_size = rope_temporal_size # None=use actual frames, int=fixed size (legacy: 64)
self.use_head = use_head
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