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| .. | ||
| configuration_dummy.py | ||
| configuration_my_new_model.py | ||
| configuration_my_new_model2.py | ||
| configuration_new_model.py | ||
| configuration_super.py | ||
| convert_examples.sh | ||
| image_processing_new_imgproc_model.py | ||
| modeling_dummy_bert.py | ||
| modeling_dummy.py | ||
| modeling_my_new_model2.py | ||
| modeling_new_task_model.py | ||
| modeling_roberta.py | ||
| modeling_super.py | ||
| modular_dummy_bert.py | ||
| modular_dummy.py | ||
| modular_my_new_model.py | ||
| modular_my_new_model2.py | ||
| modular_new_imgproc_model.py | ||
| modular_new_model.py | ||
| modular_new_task_model.py | ||
| modular_roberta.py | ||
| modular_super.py | ||
| README.md | ||
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