[quant] Input-Weight Equalization - selective equalization (#61916)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61916
Functions used to run selective equalization based on the SQNR obtained from running the Numeric Suite. After running the Numeric Suite between the equalized and float model, we will get the SQNR between the two models and construct an equalization_qconfig_dict that specifies to only equalize the layers with the highest quantization errors.
How to run:
```
layer_to_sqnr_dict = get_layer_sqnr_dict(float_model, equalized_model, input)
eq_qconfig_dict = get_equalization_qconfig_dict(layer_to_sqnr_dict, equalized_model, num_layers_to_equalize)
prepared = prepare_fx(float_model, qconfig_dict, eq_qconfig_dict)
...
```
Test Plan:
`python test/test_quantization.py TestEqualizeFx.test_selective_equalization`
Imported from OSS
Reviewed By: supriyar
Differential Revision: D29796950
fbshipit-source-id: 91f0f8427d751beaea32d8ffc2f3b8aa8ef7ea95
