def federated_aggregate(self, value, zero, accumulate, merge, report):
"""Implements `federated_aggregate` as defined in `api/intrinsics.py`.
Args:
value: As in `api/intrinsics.py`.
zero: As in `api/intrinsics.py`.
accumulate: As in `api/intrinsics.py`.
merge: As in `api/intrinsics.py`.
report: As in `api/intrinsics.py`.
Returns:
As in `api/intrinsics.py`.
Raises:
TypeError: As in `api/intrinsics.py`.
"""
value = value_impl.to_value(value, None, self._context_stack)
value_utils.check_federated_value_placement(value, placements.CLIENTS,
'value to be aggregated')
zero = value_impl.to_value(zero, None, self._context_stack)
py_typecheck.check_type(zero, value_base.Value)
# TODO(b/113112108): We need a check here that zero does not have federated
# constituents.
accumulate = value_impl.to_value(accumulate, None, self._context_stack)
merge = value_impl.to_value(merge, None, self._context_stack)
report = value_impl.to_value(report, None, self._context_stack)
for op in [accumulate, merge, report]:
py_typecheck.check_type(op, value_base.Value)
py_typecheck.check_type(op.type_signature,
computation_types.FunctionType)
accumulate_type_expected = type_factory.reduction_op(
zero.type_signature, value.type_signature.member)
merge_type_expected = type_factory.reduction_op(
zero.type_signature, zero.type_signature)
report_type_expected = computation_types.FunctionType(
zero.type_signature, report.type_signature.result)
for op_name, op, type_expected in [
('accumulate', accumulate, accumulate_type_expected),
('merge', merge, merge_type_expected),
('report', report, report_type_expected)
]:
if not type_utils.is_assignable_from(type_expected,
op.type_signature):
raise TypeError(
'Expected parameter `{}` to be of type {}, but received {} instead.'
.format(op_name, type_expected, op.type_signature))
value = value_impl.ValueImpl.get_comp(value)
zero = value_impl.ValueImpl.get_comp(zero)
accumulate = value_impl.ValueImpl.get_comp(accumulate)
merge = value_impl.ValueImpl.get_comp(merge)
report = value_impl.ValueImpl.get_comp(report)
comp = building_block_factory.create_federated_aggregate(
value, zero, accumulate, merge, report)
return value_impl.ValueImpl(comp, self._context_stack)
def federated_mean(self, value, weight):
"""Implements `federated_mean` as defined in `api/intrinsics.py`.
Args:
value: As in `api/intrinsics.py`.
weight: As in `api/intrinsics.py`.
Returns:
As in `api/intrinsics.py`.
Raises:
TypeError: As in `api/intrinsics.py`.
"""
# TODO(b/113112108): Possibly relax the constraints on numeric types, and
# inject implicit casts where appropriate. For instance, we might want to
# allow `tf.int32` values as the input, and automatically cast them to
# `tf.float321 before invoking the average, thus producing a floating-point
# result.
# TODO(b/120439632): Possibly allow the weight to be either structured or
# non-scalar, e.g., for the case of averaging a convolutional layer, when
# we would want to use a different weight for every filter, and where it
# might be cumbersome for users to have to manually slice and assemble a
# variable.
value = value_impl.to_value(value, None, self._context_stack)
value_utils.check_federated_value_placement(value, placements.CLIENTS,
'value to be averaged')
if not type_utils.is_average_compatible(value.type_signature):
raise TypeError(
'The value type {} is not compatible with the average operator.'
.format(value.type_signature))
if weight is not None:
weight = value_impl.to_value(weight, None, self._context_stack)
value_utils.check_federated_value_placement(
weight, placements.CLIENTS, 'weight to use in averaging')
py_typecheck.check_type(weight.type_signature.member,
computation_types.TensorType)
if weight.type_signature.member.shape.ndims != 0:
raise TypeError(
'The weight type {} is not a federated scalar.'.format(
weight.type_signature))
if not (weight.type_signature.member.dtype.is_integer
or weight.type_signature.member.dtype.is_floating):
raise TypeError(
'The weight type {} is not a federated integer or floating-point '
'tensor.'.format(weight.type_signature))
value = value_impl.ValueImpl.get_comp(value)
if weight is not None:
weight = value_impl.ValueImpl.get_comp(weight)
comp = building_block_factory.create_federated_mean(value, weight)
return value_impl.ValueImpl(comp, self._context_stack)
def federated_map_all_equal(self, fn, arg):
"""Implements `federated_map` as defined in `api/intrinsic.py`.
Implements `federated_map` as defined in `api/intrinsic.py` with an argument
with the `all_equal` bit set.
Args:
fn: As in `api/intrinsics.py`.
arg: As in `api/intrinsics.py`, with the `all_equal` bit set.
Returns:
As in `api/intrinsics.py`.
Raises:
TypeError: As in `api/intrinsics.py`.
"""
# TODO(b/113112108): Possibly lift the restriction that the mapped value
# must be placed at the clients after adding support for placement labels
# in the federated types, and expanding the type specification of the
# intrinsic this is based on to work with federated values of arbitrary
# placement.
arg = value_impl.to_value(arg, None, self._context_stack)
if isinstance(arg.type_signature, computation_types.NamedTupleType):
if len(anonymous_tuple.to_elements(arg.type_signature)) >= 2:
# We've been passed a value which the user expects to be zipped.
arg = self.federated_zip(arg)
value_utils.check_federated_value_placement(arg, placements.CLIENTS,
'value to be mapped')
# TODO(b/113112108): Add support for polymorphic templates auto-instantiated
# here based on the actual type of the argument.
fn = value_impl.to_value(fn, None, self._context_stack)
py_typecheck.check_type(fn, value_base.Value)
py_typecheck.check_type(fn.type_signature,
computation_types.FunctionType)
if not type_utils.is_assignable_from(fn.type_signature.parameter,
arg.type_signature.member):
raise TypeError(
'The mapping function expects a parameter of type {}, but member '
'constituents of the mapped value are of incompatible type {}.'
.format(fn.type_signature.parameter,
arg.type_signature.member))
fn = value_impl.ValueImpl.get_comp(fn)
arg = value_impl.ValueImpl.get_comp(arg)
comp = building_block_factory.create_federated_map_all_equal(fn, arg)
return value_impl.ValueImpl(comp, self._context_stack)
def federated_reduce(self, value, zero, op):
"""Implements `federated_reduce` as defined in `api/intrinsics.py`.
Args:
value: As in `api/intrinsics.py`.
zero: As in `api/intrinsics.py`.
op: As in `api/intrinsics.py`.
Returns:
As in `api/intrinsics.py`.
Raises:
TypeError: As in `api/intrinsics.py`.
"""
# TODO(b/113112108): Since in most cases, it can be assumed that CLIENTS is
# a non-empty collective (or else, the computation fails), specifying zero
# at this level of the API should probably be optional. TBD.
value = value_impl.to_value(value, None, self._context_stack)
value_utils.check_federated_value_placement(value, placements.CLIENTS,
'value to be reduced')
zero = value_impl.to_value(zero, None, self._context_stack)
py_typecheck.check_type(zero, value_base.Value)
# TODO(b/113112108): We need a check here that zero does not have federated
# constituents.
op = value_impl.to_value(op, None, self._context_stack)
py_typecheck.check_type(op, value_base.Value)
py_typecheck.check_type(op.type_signature,
computation_types.FunctionType)
op_type_expected = type_factory.reduction_op(
zero.type_signature, value.type_signature.member)
if not type_utils.is_assignable_from(op_type_expected,
op.type_signature):
raise TypeError('Expected an operator of type {}, got {}.'.format(
op_type_expected, op.type_signature))
value = value_impl.ValueImpl.get_comp(value)
zero = value_impl.ValueImpl.get_comp(zero)
op = value_impl.ValueImpl.get_comp(op)
comp = building_block_factory.create_federated_reduce(value, zero, op)
return value_impl.ValueImpl(comp, self._context_stack)
def federated_collect(self, value):
"""Implements `federated_collect` as defined in `api/intrinsics.py`.
Args:
value: As in `api/intrinsics.py`.
Returns:
As in `api/intrinsics.py`.
Raises:
TypeError: As in `api/intrinsics.py`.
"""
value = value_impl.to_value(value, None, self._context_stack)
value_utils.check_federated_value_placement(value, placements.CLIENTS,
'value to be collected')
value = value_impl.ValueImpl.get_comp(value)
comp = building_block_factory.create_federated_collect(value)
return value_impl.ValueImpl(comp, self._context_stack)
def federated_apply(self, fn, arg):
"""Implements `federated_apply` as defined in `api/intrinsics.py`.
Args:
fn: As in `api/intrinsics.py`.
arg: As in `api/intrinsics.py`.
Returns:
As in `api/intrinsics.py`.
Raises:
TypeError: As in `api/intrinsics.py`.
"""
fn = value_impl.to_value(fn, None, self._context_stack)
py_typecheck.check_type(fn, value_base.Value)
py_typecheck.check_type(fn.type_signature,
computation_types.FunctionType)
arg = value_impl.to_value(arg, None, self._context_stack)
if isinstance(arg.type_signature, computation_types.NamedTupleType):
if len(anonymous_tuple.to_elements(arg.type_signature)) >= 2:
# We've been passed a value which the user expects to be zipped.
arg = self.federated_zip(arg)
value_utils.check_federated_value_placement(arg, placements.SERVER,
'the argument')
if not arg.type_signature.all_equal:
raise TypeError('The argument should be equal at all locations.')
if not type_utils.is_assignable_from(fn.type_signature.parameter,
arg.type_signature.member):
raise TypeError(
'The function to apply expects a parameter of type {}, but member '
'constituents of the argument are of an incompatible type {}.'.
format(fn.type_signature.parameter, arg.type_signature.member))
fn = value_impl.ValueImpl.get_comp(fn)
arg = value_impl.ValueImpl.get_comp(arg)
comp = building_block_factory.create_federated_apply(fn, arg)
return value_impl.ValueImpl(comp, self._context_stack)
def federated_broadcast(self, value):
"""Implements `federated_broadcast` as defined in `api/intrinsics.py`.
Args:
value: As in `api/intrinsics.py`.
Returns:
As in `api/intrinsics.py`.
Raises:
TypeError: As in `api/intrinsics.py`.
"""
value = value_impl.to_value(value, None, self._context_stack)
value_utils.check_federated_value_placement(value, placements.SERVER,
'value to be broadcasted')
if not value.type_signature.all_equal:
raise TypeError('The broadcasted value should be equal at all locations.')
value = value_impl.ValueImpl.get_comp(value)
comp = computation_constructing_utils.create_federated_broadcast(value)
return value_impl.ValueImpl(comp, self._context_stack)
def federated_sum(self, value):
"""Implements `federated_sum` as defined in `api/intrinsics.py`.
Args:
value: As in `api/intrinsics.py`.
Returns:
As in `api/intrinsics.py`.
Raises:
TypeError: As in `api/intrinsics.py`.
"""
value = value_impl.to_value(value, None, self._context_stack)
value_utils.check_federated_value_placement(value, placements.CLIENTS,
'value to be summed')
if not type_utils.is_sum_compatible(value.type_signature):
raise TypeError(
'The value type {} is not compatible with the sum operator.'.
format(value.type_signature))
value = value_impl.ValueImpl.get_comp(value)
comp = building_block_factory.create_federated_sum(value)
return value_impl.ValueImpl(comp, self._context_stack)
def _(x):
value_utils.check_federated_value_placement(x, placements.CLIENTS)
with self.assertRaises(TypeError):
value_utils.check_federated_value_placement(
x, placements.SERVER)
return x
