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yaswant19 2025-06-18 00:05:38 +05:30
parent 0331fb368b
commit 95506b5a74
5 changed files with 40 additions and 92 deletions
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66
src/transformers/models/aimv2/modeling_aimv2.py
View File

@ -34,15 +34,7 @@ from ...modeling_attn_mask_utils import AttentionMaskConverter
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...utils import (
ModelOutput,
add_start_docstrings,
add_start_docstrings_to_model_forward,
auto_docstring,
can_return_tuple,
logging,
replace_return_docstrings,
)
from ...utils import ModelOutput, auto_docstring, can_return_tuple, logging, replace_return_docstrings
from .configuration_aimv2 import Aimv2Config, Aimv2TextConfig, Aimv2VisionConfig
@ -449,6 +441,7 @@ class Aimv2AttentionPoolingHead(nn.Module):
return output
@auto_docstring
class Aimv2PreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
@ -458,7 +451,12 @@ class Aimv2PreTrainedModel(PreTrainedModel):
config_class = Aimv2Config
base_model_prefix = "aimv2"
supports_gradient_checkpointing = True
_no_split_modules = ["AIMv2SwiGLUFFN"]
_no_split_modules = [
"Aimv2EncoderLayer",
"Aimv2AttentionPoolingHead",
"Aimv2VisionEmbeddings",
"Aimv2TextEmbeddings",
]
_supports_sdpa = True
def _init_weights(self, module):
@ -482,42 +480,10 @@ class Aimv2PreTrainedModel(PreTrainedModel):
module.cls_token.data.normal_(mean=0.0, std=std)
AIMV2_VISION_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`Aimv2VisionConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
AIMV2_VISION_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
[`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
interpolate_pos_encoding (`bool`, *optional*, defaults `False`):
Whether to interpolate the pre-trained position encodings.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"""The vision model from AIMv2 without any head or projection on top.""",
AIMV2_VISION_START_DOCSTRING,
@auto_docstring(
custom_intro="""
The Vision model from AIMv2 without any head or projection on top.
"""
)
class Aimv2VisionModel(Aimv2PreTrainedModel):
main_input_name = "pixel_values"
@ -540,7 +506,7 @@ class Aimv2VisionModel(Aimv2PreTrainedModel):
return self.embeddings.patch_embed
@can_return_tuple
@add_start_docstrings_to_model_forward(AIMV2_VISION_INPUTS_DOCSTRING)
@auto_docstring
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=Aimv2VisionConfig)
def forward(
self,
@ -597,6 +563,11 @@ class Aimv2VisionModel(Aimv2PreTrainedModel):
)
@auto_docstring(
custom_intro="""
The text model from AIMv2 without any head or projection on top.
"""
)
class Aimv2TextModel(Aimv2PreTrainedModel):
main_input_name = "input_ids"
@ -618,6 +589,7 @@ class Aimv2TextModel(Aimv2PreTrainedModel):
self.embeddings.token_embedding = value
@can_return_tuple
@auto_docstring
def forward(
self,
input_ids,

62
src/transformers/models/aimv2/modular_aimv2.py
View File

@ -27,8 +27,7 @@ from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import BaseModelOutputWithPooling
from ...modeling_utils import PreTrainedModel
from ...utils import (
add_start_docstrings,
add_start_docstrings_to_model_forward,
auto_docstring,
can_return_tuple,
logging,
replace_return_docstrings,
@ -289,8 +288,6 @@ class Aimv2Config(SiglipConfig):
del self.initializer_factor
pass
class Aimv2Output(SiglipOutput):
pass
@ -434,39 +431,7 @@ class Aimv2AttentionPoolingHead(nn.Module):
return output
AIMV2_VISION_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`Aimv2VisionConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
AIMV2_VISION_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
[`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
interpolate_pos_encoding (`bool`, *optional*, defaults `False`):
Whether to interpolate the pre-trained position encodings.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@auto_docstring
class Aimv2PreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
@ -476,7 +441,12 @@ class Aimv2PreTrainedModel(PreTrainedModel):
config_class = Aimv2Config
base_model_prefix = "aimv2"
supports_gradient_checkpointing = True
_no_split_modules = ["AIMv2SwiGLUFFN"]
_no_split_modules = [
"Aimv2EncoderLayer",
"Aimv2AttentionPoolingHead",
"Aimv2VisionEmbeddings",
"Aimv2TextEmbeddings",
]
_supports_sdpa = True
def _init_weights(self, module):
@ -500,9 +470,10 @@ class Aimv2PreTrainedModel(PreTrainedModel):
module.cls_token.data.normal_(mean=0.0, std=std)
@add_start_docstrings(
"""The vision model from AIMv2 without any head or projection on top.""",
AIMV2_VISION_START_DOCSTRING,
@auto_docstring(
custom_intro="""
The Vision model from AIMv2 without any head or projection on top.
"""
)
class Aimv2VisionModel(Aimv2PreTrainedModel):
main_input_name = "pixel_values"
@ -525,7 +496,7 @@ class Aimv2VisionModel(Aimv2PreTrainedModel):
return self.embeddings.patch_embed
@can_return_tuple
@add_start_docstrings_to_model_forward(AIMV2_VISION_INPUTS_DOCSTRING)
@auto_docstring
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=Aimv2VisionConfig)
def forward(
self,
@ -582,6 +553,11 @@ class Aimv2VisionModel(Aimv2PreTrainedModel):
)
@auto_docstring(
custom_intro="""
The text model from AIMv2 without any head or projection on top.
"""
)
class Aimv2TextModel(Aimv2PreTrainedModel):
main_input_name = "input_ids"
@ -603,6 +579,7 @@ class Aimv2TextModel(Aimv2PreTrainedModel):
self.embeddings.token_embedding = value
@can_return_tuple
@auto_docstring
def forward(
self,
input_ids,
@ -648,6 +625,7 @@ class Aimv2TextModel(Aimv2PreTrainedModel):
)
@auto_docstring
class Aimv2Model(CLIPModel, nn.Module):
def __init__(self, config: Aimv2Config):
nn.Module().__init__(config)

1
src/transformers/models/siglip/modeling_siglip.py
View File

@ -510,7 +510,6 @@ class SiglipPreTrainedModel(PreTrainedModel):
_no_split_modules = [
"SiglipTextEmbeddings",
"SiglipEncoderLayer",
"SiglipVisionEmbeddings",
"SiglipEncoderLayer",
"SiglipMultiheadAttentionPoolingHead",

1
src/transformers/models/siglip2/modeling_siglip2.py
View File

@ -742,7 +742,6 @@ class Siglip2PreTrainedModel(PreTrainedModel):
_no_split_modules = [
"Siglip2TextEmbeddings",
"Siglip2EncoderLayer",
"Siglip2VisionEmbeddings",
"Siglip2EncoderLayer",
"Siglip2MultiheadAttentionPoolingHead",

2
tests/models/aimv2/test_modeling_aimv2.py
View File

@ -1,5 +1,5 @@
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.