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🚨🚨 Fix and simplify attention implementation dispatch and subconfigs handling (#39423)

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* first try

* Update modeling_utils.py

* Update modeling_utils.py

* big refactor

* Update modeling_utils.py

* style

* docstrings and simplify inner workings of configs

* remove all trace of _internal

* Update modeling_utils.py

* fix logic error

* Update modeling_utils.py

* recursive on config

* Update configuration_utils.py

* fix

* Update configuration_dpt.py

* Update configuration_utils.py

* Update configuration_utils.py

* Update modeling_idefics.py

* Update modeling_utils.py

* fix for old models

* more old models fixup

* Update modeling_utils.py

* Update configuration_utils.py

* Remove outdated test

* remove the deepcopy!! 🥵🥵

* Update test_modeling_gpt_bigcode.py

* fix qwen dispatch

* restrict to only models supporting it

* style

* switch name

* Update modeling_utils.py

* Update modeling_utils.py

* add tests!

* fix

* rypo

* remove bad copies

* fix

* Update modeling_utils.py

* additional check

* Update modeling_utils.py

* Update modeling_utils.py

* Update modeling_utils.py

* Update modeling_utils.py

* Update modeling_utils.py

* fix

* skip
This commit is contained in:
Cyril Vallez
2025-07-18 13:41:54 +02:00
committed by GitHub
parent 2b819ba4e3
commit 4ded9a4113
33 changed files with 472 additions and 323 deletions
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7
utils/test_module/custom_configuration.py
View File

@@ -7,10 +7,3 @@ class CustomConfig(PretrainedConfig):
def __init__(self, attribute=1, **kwargs):
self.attribute = attribute
super().__init__(**kwargs)
class NoSuperInitConfig(PretrainedConfig):
model_type = "custom"
def __init__(self, attribute=1, **kwargs):
self.attribute = attribute

16
utils/test_module/custom_modeling.py
View File

@@ -2,7 +2,7 @@ import torch
from transformers import PreTrainedModel
from .custom_configuration import CustomConfig, NoSuperInitConfig
from .custom_configuration import CustomConfig
class CustomModel(PreTrainedModel):
@@ -17,17 +17,3 @@ class CustomModel(PreTrainedModel):
def _init_weights(self, module):
pass
class NoSuperInitModel(PreTrainedModel):
config_class = NoSuperInitConfig
def __init__(self, config):
super().__init__(config)
self.linear = torch.nn.Linear(config.attribute, config.attribute)
def forward(self, x):
return self.linear(x)
def _init_weights(self, module):
pass