[ao][sparsity] Data Sparsifier Benchmarking: Model quality evaluation of the sparsified DLRM model (#81779)
The objective is to perform evaluation of the model quality after sparsifying the embeddings of the dlrm model.
The ```evaluation_model_metrics.py``` makes use of the ```sparse_model_metadata.csv``` file dumped by the
```evaluate_disk_savings.py```. The model metrics such as accuracy, auc, f1 etc are calculated on the test-dataset
for various sparsity levels, block shapes and norms available on the metadata csv file.
**Results**: The model accuracy decreases slowly with sparsity levels. Even at 90% sparsity levels, the model accuracy decreases only by 2%.
After running `evaluate_memory_savings.py`, run: `python evaluate_model_metrics.py --raw_data_file=<path_to_raw_data_txt_file> --processed_data_file=<path_to_kaggleAdDisplayChallenge_processed.npz> --sparse_model_metadata=<path_to_sparse_model_metadata_csv>`
Dependencies: DLRM Repository (https://github.com/facebookresearch/dlrm)
Test Plan: None
Pull Request resolved: https://github.com/pytorch/pytorch/pull/81779
Approved by: https://github.com/z-a-f
