Add FLOPS estimation functionality to ModelBase #191
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- Add estimate_flops method to count FLOPS using torch.utils.flop_counter - Add _resolve_method helper to resolve method paths for FLOPS counting - Add MODEL_INPUT_ARGS dictionary with test inputs for all models - Add FLOPS estimation tests in test_models_general.py Addresses issue #180: Model complexity/resource utilization
…dling - Update estimate_flops method signature to accept method as the first argument followed by *args and **kwargs. - Adjust test cases in test_models_general.py to align with the new method signature, ensuring proper FLOPS estimation functionality is maintained.
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@SamirMoustafa I think we can safely merge this after the changes have been addressed. Could you please also update the model card with an additional FLOPS entry in a different PR? |
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Add FLOPS estimation functionality
✨ Context
This PR adds FLOPS (Floating Point Operations) estimation capability to the ModelBase class, addressing issue #180 for model complexity/resource utilization comparison.
🧠 Rationale behind the change
FLOPS estimation is needed to compare model resource requirements beyond just parameter count, as parameter count doesn't linearly correlate with computational complexity.
Type of changes
🛠 What does this PR implement
estimate_flops()method toModelBaseusingtorch.utils.flop_counter.FlopCounterMode_resolve_method()helper to resolve method paths for FLOPS counting (supports nested methods likeconch.forward)MODEL_INPUT_ARGSdictionary with test inputs for all models in the registrytest_models_general.pythat verify FLOPS can be computed for each model🙈 Missing
🧪 How should this be tested?
uv run pytest tests/models/test_models_general.py::test_model_init- verifies FLOPS estimation works for all modelsAddresses #180