# Copyright 2025 The EasyDeL Author @erfanzar (Erfan Zare Chavoshi).
#
# 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
#
# https://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.
import typing
from eformer.common_types import ColumnWise, Replicated, RowWise
from eformer.loggings import get_logger
from easydel.infra.base_module import EasyDeLBaseConfig
from easydel.infra.factory import register_config
logger = get_logger(__name__)
def _get_partition_rules(self, *args, **kwargs):
"""
Get the partition rules for the model.
Returns:
`tp.Tuple[tp.Tuple[str, PartitionSpec]]`: The partition rules.
"""
pmag = self.partition_manager
return (
(r"embeddings/token_embedding/embedding", pmag.resolve(ColumnWise)),
(r"embeddings/position_embedding/embedding", pmag.resolve(Replicated)),
(r"self_attn/(q_proj|k_proj|v_proj)/kernel", pmag.resolve(ColumnWise)),
(r"self_attn/out_proj/kernel", pmag.resolve(RowWise)),
(r"self_attn/.*proj/bias", pmag.resolve(Replicated)),
(r"mlp/fc1/kernel", pmag.resolve(ColumnWise)),
(r"mlp/fc2/kernel", pmag.resolve(RowWise)),
(r"mlp/fc(1|2)/bias", pmag.resolve(Replicated)),
(r"(layer_norm1|layer_norm2)/scale", pmag.resolve(Replicated)),
(r"(layer_norm1|layer_norm2)/bias", pmag.resolve(Replicated)),
(r"final_layer_norm/scale", pmag.resolve(Replicated)),
(r"final_layer_norm/bias", pmag.resolve(Replicated)),
(r"head/kernel", pmag.resolve(ColumnWise)),
(r"head/bias", pmag.resolve(Replicated)),
(r"embeddings/patch_embedding/kernel", pmag.resolve(ColumnWise)),
(r"embeddings/patch_embedding/bias", pmag.resolve(Replicated)),
(r"embeddings/position_embedding/embedding", pmag.resolve(ColumnWise)),
(r"self_attn/(q_proj|k_proj|v_proj)/kernel", pmag.resolve(ColumnWise)),
(r"self_attn/out_proj/kernel", pmag.resolve(RowWise)),
(r"self_attn/.*proj/bias", pmag.resolve(Replicated)),
(r"mlp/fc1/kernel", pmag.resolve(ColumnWise)),
(r"mlp/fc2/kernel", pmag.resolve(RowWise)),
(r"mlp/fc(1|2)/bias", pmag.resolve(Replicated)),
(r"(layer_norm1|layer_norm2)/scale", pmag.resolve(Replicated)),
(r"(layer_norm1|layer_norm2)/bias", pmag.resolve(Replicated)),
(r"post_layernorm/scale", pmag.resolve(Replicated)),
(r"post_layernorm/bias", pmag.resolve(Replicated)),
(r"head/probe", pmag.resolve(Replicated)),
(r"head/attention/in_proj_weight", pmag.resolve(ColumnWise)),
(r"head/attention/in_proj_bias", pmag.resolve(Replicated)),
(r"head/attention/out_proj/kernel", pmag.resolve(RowWise)),
(r"head/attention/out_proj/bias", pmag.resolve(Replicated)),
(r"head/layernorm/scale", pmag.resolve(Replicated)),
(r"head/layernorm/bias", pmag.resolve(Replicated)),
(r"head/mlp/fc1/kernel", pmag.resolve(ColumnWise)),
(r"head/mlp/fc2/kernel", pmag.resolve(RowWise)),
(r"head/mlp/fc(1|2)/bias", pmag.resolve(Replicated)),
(r"logit_scale", pmag.resolve(Replicated)),
(r"logit_bias", pmag.resolve(Replicated)),
(r"classifier/kernel", pmag.resolve(RowWise)),
(r"classifier/bias", pmag.resolve(Replicated)),
(r".*bias", pmag.resolve(Replicated)),
(r".*", pmag.resolve(Replicated)),
)
[docs]@register_config("siglip_text_model")
class SiglipTextConfig(EasyDeLBaseConfig):
r"""
Configuration objects inherit from [`EasyDeLBaseConfig`] and can be used to control the model outputs. Read the
documentation from [`EasyDeLBaseConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 32000):
Vocabulary size of the Siglip text model. Defines the number of different tokens that can be represented by
the `inputs_ids` passed when calling [`SiglipModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
max_position_embeddings (`int`, *optional*, defaults to 64):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
layer_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the layer normalization layers.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
pad_token_id (`int`, *optional*, defaults to 1):
The id of the padding token in the vocabulary.
bos_token_id (`int`, *optional*, defaults to 49406):
The id of the beginning-of-sequence token in the vocabulary.
eos_token_id (`int`, *optional*, defaults to 49407):
The id of the end-of-sequence token in the vocabulary.
projection_size (`int`, *optional*, defaults to `hidden_size`):
The size of the projection head.
Example:
```python
>>> from transformers import SiglipTextConfig, SiglipTextModel
>>> # Initializing a SiglipTextConfig with google/siglip-base-patch16-224 style configuration
>>> configuration = SiglipTextConfig()
>>> # Initializing a SiglipTextModel (with random weights)
>>> # from the google/siglip-base-patch16-224 style configuration
>>> model = SiglipTextModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "siglip_text_model"
base_config_key = "text_config"
def __init__(
self,
vocab_size=32000,
hidden_size=768,
intermediate_size=3072,
num_hidden_layers=12,
num_attention_heads=12,
max_position_embeddings=64,
hidden_act="gelu_pytorch_tanh",
layer_norm_eps=1e-6,
attention_dropout=0.0,
pad_token_id=1,
bos_token_id=49406,
eos_token_id=49407,
projection_size=None,
**kwargs,
):
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
**kwargs,
)
self.vocab_size = vocab_size
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.max_position_embeddings = max_position_embeddings
self.layer_norm_eps = layer_norm_eps
self.hidden_act = hidden_act
self.attention_dropout = attention_dropout
self.projection_size = projection_size if projection_size is not None else hidden_size
get_partition_rules = _get_partition_rules
[docs]@register_config("siglip_vision_model")
class SiglipVisionConfig(EasyDeLBaseConfig):
r"""
Configuration objects inherit from [`EasyDeLBaseConfig`] and can be used to control the model outputs. Read the
documentation from [`EasyDeLBaseConfig`] for more information.
Args:
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
num_channels (`int`, *optional*, defaults to 3):
Number of channels in the input images.
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_pytorch_tanh"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
layer_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the layer normalization layers.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
"""
model_type = "siglip_vision_model"
base_config_key = "vision_config"
def __init__(
self,
hidden_size=768,
intermediate_size=3072,
num_hidden_layers=12,
num_attention_heads=12,
num_channels=3,
image_size=224,
patch_size=16,
hidden_act="gelu_pytorch_tanh",
layer_norm_eps=1e-6,
attention_dropout=0.0,
**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.patch_size = patch_size
self.image_size = image_size
self.attention_dropout = attention_dropout
self.layer_norm_eps = layer_norm_eps
self.hidden_act = hidden_act
get_partition_rules = _get_partition_rules
[docs]@register_config("siglip")
class SiglipConfig(EasyDeLBaseConfig):
r"""
[`SiglipConfig`] is the configuration class to store the configuration of a [`SiglipModel`]. It is used to
instantiate a Siglip model according to the specified arguments, defining the text model and vision model configs.
Instantiating a configuration with the defaults will yield a similar configuration to that of the Siglip
[google/siglip-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224) architecture.
Configuration objects inherit from [`EasyDeLBaseConfig`] and can be used to control the model outputs. Read the
documentation from [`EasyDeLBaseConfig`] for more information.
Args:
text_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`SiglipTextConfig`].
vision_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`SiglipVisionConfig`].
kwargs (*optional*):
Dictionary of keyword arguments.
"""
model_type = "siglip"
"""
The model type identifier used to determine which model configuration this represents.
This is set to "siglip" to identify this as the main configuration for the SigLIP model.
"""
sub_configs: typing.ClassVar = {"text_config": SiglipTextConfig, "vision_config": SiglipVisionConfig}
"""
A dictionary that maps configuration keys to their respective configuration classes.
This enables the SiglipConfig to manage both text and vision components through
separate configurations while maintaining them as part of a single unified model.
"""
def __init__(self, text_config=None, vision_config=None, **kwargs):
super().__init__(**kwargs)
if text_config is None:
text_config = {}
logger.info("`text_config` is `None`. Initializing the `SiglipTextConfig` with default values.")
if vision_config is None:
vision_config = {}
logger.info("`vision_config` is `None`. initializing the `SiglipVisionConfig` with default values.")
self.text_config = SiglipTextConfig(**text_config)
self.vision_config = SiglipVisionConfig(**vision_config)
self.text_config.read_basics_from_config(self)
self.vision_config.read_basics_from_config(self)
self.initializer_factor = 1.0
[docs] @classmethod
def from_text_vision_configs(
cls,
text_config: SiglipTextConfig,
vision_config: SiglipVisionConfig,
**kwargs,
):
r"""
Instantiate a [`SiglipConfig`] (or a derived class) from siglip text model configuration and siglip vision
model configuration.
Returns:
[`SiglipConfig`]: An instance of a configuration object
"""
return cls(
text_config=text_config.to_dict(),
vision_config=vision_config.to_dict(),
**kwargs,
)
get_partition_rules = _get_partition_rules
__all__ = ["SiglipConfig", "SiglipTextConfig", "SiglipVisionConfig"]