def encoded_sum_fn(state, values, weight=None):
"""Encoded sum federated_computation."""
del weight # Unused.
encode_params, decode_before_sum_params, decode_after_sum_params = (
tff.federated_map(nest_encoder.get_params_fn, state))
encode_params = tff.federated_broadcast(encode_params)
decode_before_sum_params = tff.federated_broadcast(
decode_before_sum_params)
encoded_values = tff.federated_map(
nest_encoder.encode_fn,
[values, encode_params, decode_before_sum_params])
aggregated_values = tff.federated_aggregate(encoded_values,
nest_encoder.zero_fn(),
nest_encoder.accumulate_fn,
nest_encoder.merge_fn,
nest_encoder.report_fn)
decoded_values = tff.federated_map(
nest_encoder.decode_after_sum_fn,
[aggregated_values.values, decode_after_sum_params])
updated_state = tff.federated_map(
nest_encoder.update_state_fn,
[state, aggregated_values.state_update_tensors])
return updated_state, decoded_values
def foo(temperatures, threshold):
return tff.federated_sum(
tff.federated_map(
tff.tf_computation(
lambda x, y: tf.cast(tf.greater(x, y), tf.int32),
[tf.float32, tf.float32]),
[temperatures, tff.federated_broadcast(threshold)]))
def encoded_broadcast_fn(state, value):
"""Encoded broadcast federated_computation."""
new_state, encoded_value = tff.federated_apply(encode_fn,
(state, value))
client_encoded_value = tff.federated_broadcast(encoded_value)
client_value = tff.federated_map(decode_fn, client_encoded_value)
return new_state, client_value
def broadcast_next_fn(state, value):
@tff.tf_computation(tf.int32)
def add_one(value):
return value + 1
return {
'call_count': tff.federated_apply(add_one, state.call_count),
}, tff.federated_broadcast(value)
def next_fn(server_state, client_data):
broadcast_state = tff.federated_broadcast(server_state)
@tff.tf_computation(tf.int32, tff.SequenceType(tf.float32))
@tf.function
def some_transform(x, y):
del y # Unused
return x + 1
client_update = tff.federated_map(some_transform,
(broadcast_state, client_data))
aggregate_update = tff.federated_sum(client_update)
server_output = tff.federated_value(1234, tff.SERVER)
return aggregate_update, server_output
def encoded_broadcast_fn(state, value):
"""Broadcast function, to be wrapped as federated_computation."""
state_type = state.type_signature.member
value_type = value.type_signature.member
encode_fn, decode_fn = _build_encode_decode_tf_computations_for_broadcast(
state_type, value_type, encoders)
new_state, encoded_value = tff.federated_apply(encode_fn,
(state, value))
client_encoded_value = tff.federated_broadcast(encoded_value)
client_value = tff.federated_map(decode_fn, client_encoded_value)
return new_state, client_value
def next_computation(arg):
"""The logic of a single MapReduce sprocessing round."""
s1 = arg[0]
c1 = arg[1]
s2 = tff.federated_apply(cf.prepare, s1)
c2 = tff.federated_broadcast(s2)
c3 = tff.federated_zip([c1, c2])
c4 = tff.federated_map(cf.work, c3)
c5 = c4[0]
c6 = c4[1]
s3 = tff.federated_aggregate(c5, cf.zero(), cf.accumulate, cf.merge,
cf.report)
s4 = tff.federated_zip([s1, s3])
s5 = tff.federated_apply(cf.update, s4)
s6 = s5[0]
s7 = s5[1]
return s6, s7, c6
def next_fn(global_state, value, weight=None):
"""Defines next_fn for StatefulAggregateFn."""
# Weighted aggregation is not supported.
# TODO(b/140236959): Add an assertion that weight is None here, so the
# contract of this method is better established. Will likely cause some
# downstream breaks.
del weight
#######################################
# Define local tf_computations
# TODO(b/129567727): Make most of these tf_computations polymorphic
# so type manipulation isn't needed.
global_state_type = initialize_fn.type_signature.result
@tff.tf_computation(global_state_type)
def derive_sample_params(global_state):
return query.derive_sample_params(global_state)
@tff.tf_computation(derive_sample_params.type_signature.result,
value.type_signature.member)
def preprocess_record(params, record):
# TODO(b/123092620): Once TFF passes the expected container type (instead
# of AnonymousTuple), we shouldn't need this.
record = from_anon_tuple_fn(record)
return query.preprocess_record(params, record)
# TODO(b/123092620): We should have the expected container type here.
value_type = value_type_fn(value)
tensor_specs = tff_framework.type_to_tf_tensor_specs(value_type)
@tff.tf_computation
def zero():
return query.initial_sample_state(tensor_specs)
sample_state_type = zero.type_signature.result
@tff.tf_computation(sample_state_type,
preprocess_record.type_signature.result)
def accumulate(sample_state, preprocessed_record):
return query.accumulate_preprocessed_record(
sample_state, preprocessed_record)
@tff.tf_computation(sample_state_type, sample_state_type)
def merge(sample_state_1, sample_state_2):
return query.merge_sample_states(sample_state_1, sample_state_2)
@tff.tf_computation(merge.type_signature.result)
def report(sample_state):
return sample_state
@tff.tf_computation(sample_state_type, global_state_type)
def post_process(sample_state, global_state):
result, new_global_state = query.get_noised_result(
sample_state, global_state)
return new_global_state, result
#######################################
# Orchestration logic
sample_params = tff.federated_apply(derive_sample_params, global_state)
client_sample_params = tff.federated_broadcast(sample_params)
preprocessed_record = tff.federated_map(preprocess_record,
(client_sample_params, value))
agg_result = tff.federated_aggregate(preprocessed_record, zero(),
accumulate, merge, report)
return tff.federated_apply(post_process, (agg_result, global_state))
def _(x): return tff.federated_broadcast(x)
