transformers/examples/modular-transformers
Arthur a3add29097
Add support for __all__ and potentilly deleting functions (#33859)
* Add support for __all__ and potentailly deleting functions

* updates

* update

* nits

* remove dummies

* fix warning

* fixup

* style

* update

* fixup

* skip copied from when # skip

* remove log

* bring dummies back

* fixup

* remove copied from

* fixup

* remove warnings from `make fix-copies`

* fix doc issues

* nits

* Better error message !

* add support for more flexible naming!

* style

* breaking style?

* fix super() renaming issues

* del not needed when you don't call super().__init__()

* style

* no more fmt on :)

* properly remove `self`

* fixup

* fix

* doc nits

* add some doc 🫡
2024-10-08 10:19:17 +02:00
..
configuration_dummy.py Modular transformers: modularity and inheritance for new model additions (#33248) 2024-09-24 15:54:07 +02:00
configuration_my_new_model.py [modular] fixes! (#33820) 2024-09-30 16:43:55 +02:00
configuration_my_new_model2.py [modular] fixes! (#33820) 2024-09-30 16:43:55 +02:00
configuration_new_model.py [modular] fixes! (#33820) 2024-09-30 16:43:55 +02:00
configuration_super.py Modular transformers: modularity and inheritance for new model additions (#33248) 2024-09-24 15:54:07 +02:00
convert_examples.sh [modular] fixes! (#33820) 2024-09-30 16:43:55 +02:00
modeling_dummy_bert.py [modular] fixes! (#33820) 2024-09-30 16:43:55 +02:00
modeling_dummy.py Add support for __all__ and potentilly deleting functions (#33859) 2024-10-08 10:19:17 +02:00
modeling_my_new_model2.py Paligemma: fix static cache test (#33941) 2024-10-05 09:47:37 +02:00
modeling_super.py Modular transformers: modularity and inheritance for new model additions (#33248) 2024-09-24 15:54:07 +02:00
modular_dummy_bert.py Modular transformers: modularity and inheritance for new model additions (#33248) 2024-09-24 15:54:07 +02:00
modular_dummy.py [modular] fixes! (#33820) 2024-09-30 16:43:55 +02:00
modular_my_new_model.py Modular transformers: modularity and inheritance for new model additions (#33248) 2024-09-24 15:54:07 +02:00
modular_my_new_model2.py Modular transformers: modularity and inheritance for new model additions (#33248) 2024-09-24 15:54:07 +02:00
modular_new_model.py Modular transformers: modularity and inheritance for new model additions (#33248) 2024-09-24 15:54:07 +02:00
modular_roberta.py Modular transformers: modularity and inheritance for new model additions (#33248) 2024-09-24 15:54:07 +02:00
modular_super.py Modular transformers: modularity and inheritance for new model additions (#33248) 2024-09-24 15:54:07 +02:00
README.md Modular transformers: modularity and inheritance for new model additions (#33248) 2024-09-24 15:54:07 +02:00

Using the modular_converter linter

pip install libcst is a must!

sh examples/modular-transformers/convert_examples.sh to get the converted outputs

The modular converter is a new linter specific to transformers. It allows us to unpack inheritance in python to convert a modular file like modular_gemma.py into a single model single file.

Examples of possible usage are available in the examples/modular-transformers, or modular_gemma for a full model usage.

python utils/modular_model_converter.py --files_to_parse "/Users/arthurzucker/Work/transformers/examples/modular-transformers/modular_my_new_model2.py"

How it works

We use the libcst parser to produce an AST representation of the modular_xxx.py file. For any imports that are made from transformers.models.modeling_xxxx we parse the source code of that module, and build a class dependency mapping, which allows us to unpack the modularerence dependencies.

The code from the modular file and the class dependency mapping are "merged" to produce the single model single file. We use ruff to automatically remove the potential duplicate imports.

Why we use libcst instead of the native AST?

AST is super powerful, but it does not keep the docstring, comment or code formatting. Thus we decided to go with libcst