1. Split DPQuery.accumulate_record function into preprocess_record and accumulate_preprocessed_record.
2. Add merge_sample_state function.
3. Add default implementations for some functions in DPQuery, and add base class SumAggregationDPQuery that implements some more. Only get_noised_result is still abstract.
4. Enforce that all states and parameters used as inputs and outputs to DPQuery functions are nested structures of tensors by replacing numbers with constants and Nones with empty tuples.
PiperOrigin-RevId: 247975791
At present, the script has no heavy dependencies except for the rdp_accountant, which is by itself pretty light-weight. However, importing rdp_accountant triggers __init__.py in third_party/py/tensorflow_privacy/privacy, which loads TF and all of tf.privacy. The CL adds a check to the __init__.py, which controls this behavior.
PiperOrigin-RevId: 243172355
Prior to this change the PrivacyLedger is running to keep a log of private queries, but the ledger is not actually used to compute the (epsilon, delta) guarantees. This CL adds a function to compute the RDP directly from the ledger.
Note I did verify that the tutorial builds and runs with the changes and for the first few iterations prints the same epsilon values as before the change.
PiperOrigin-RevId: 241063532
Moved query classes from dir optimizers into new dir dp_query. Added NormalizedQuery class for queries that divide the output of another query by a constant like GaussianAverageQuery.
PiperOrigin-RevId: 240167115
