def get_noised_result(self, sample_state, global_state):
"""Ensures sample is recorded to the ledger and returns noised result."""
result, new_global_state = self._query.get_noised_result(
sample_state, global_state)
self._ledger.finalise_sample()
op = lambda tensor: tensor.clone().detach()
return nest.map_structure(op, result), new_global_state
def get_noised_result(self, sample_state, global_state):
def add_noise(p):
return p + (torch.randn_like(p) * global_state.noise_stddev)
if self._ledger:
self._ledger.record_sum_query(global_state.l2_norm_clip, global_state.noise_stddev)
return nest.map_structure(add_noise, sample_state), global_state
def get_noised_result(self, sample_state, global_state, selected_indices):
"""Ensures sample is recorded to the ledger and returns noised result."""
result, new_global_state = self._query.get_noised_result(
sample_state, global_state)
# record sum queries for each client who was selected
for i in range(self.M):
if i in selected_indices:
self.ledgers[i].record_sum_query(global_state.l2_norm_clip,
global_state.noise_stddev)
self.ledgers[i].finalise_sample()
if isinstance(result, torch.Tensor):
op = lambda tensor: tensor.clone().detach()
return nest.map_structure(op, result), new_global_state
else:
return result, new_global_state
def preprocess_record(self, params, record):
"""
Return the scaled record and also the l2 norm (to deduce whether clipping occured or not)
:param params:
:param record:
:return:
"""
l2_norm_clip = params
# logger.debug(f"clipping bound {l2_norm_clip}")
l2_norm = torch.sqrt(nest.reduce_structure(lambda p: torch.norm(torch.flatten(p), p=2) ** 2,
torch.add,
record))
self._record_l2_norm = l2_norm
if l2_norm < l2_norm_clip:
return record
else:
return nest.map_structure(lambda p: torch.div(p, torch.abs(l2_norm / l2_norm_clip)), record)
def initial_sample_state(self, param_groups):
return nest.map_structure(torch.zeros_like, param_groups)
def merge_sample_states(self, sample_state_1, sample_state_2):
return nest.map_structure(torch.add, sample_state_1, sample_state_2)
def accumulate_preprocessed_record(self, sample_state, record):
return nest.map_structure(torch.add, sample_state, record)
def initial_sample_state(self, param_groups):
""" Return state of zeros the same shape as the parameter groups."""
return nest.map_structure(torch.zeros_like, param_groups)