def construct_map_or_apply(func, arg):
"""Injects intrinsic to allow application of `func` to federated `arg`.
Args:
func: `value_base.Value` instance of non-federated type to be wrapped with
intrinsic in order to call on `arg`.
arg: `computation_building_blocks.ComputationBuildingBlock` instance of
federated type for which to construct intrinsic in order to call `func` on
`value`.
Returns:
Returns `value_base.Value` instance wrapping
`computation_building_blocks.Intrinsic` which can call `func` on `arg`.
"""
py_typecheck.check_type(func,
computation_building_blocks.ComputationBuildingBlock)
py_typecheck.check_type(arg,
computation_building_blocks.ComputationBuildingBlock)
py_typecheck.check_type(arg.type_signature, computation_types.FederatedType)
result_type = computation_types.FederatedType(func.type_signature.result,
arg.type_signature.placement,
arg.type_signature.all_equal)
if arg.type_signature.placement == placement_literals.SERVER:
intrinsic = computation_building_blocks.Intrinsic(
intrinsic_defs.FEDERATED_APPLY.uri,
computation_types.FunctionType(
[func.type_signature, arg.type_signature], result_type))
elif arg.type_signature.placement == placement_literals.CLIENTS:
intrinsic = computation_building_blocks.Intrinsic(
intrinsic_defs.FEDERATED_MAP.uri,
computation_types.FunctionType(
[func.type_signature, arg.type_signature], result_type))
return intrinsic
def construct_map_or_apply(fn, arg):
"""Injects intrinsic to allow application of `fn` to federated `arg`.
Args:
fn: An instance of `computation_building_blocks.ComputationBuildingBlock` of
functional type to be wrapped with intrinsic in order to call on `arg`.
arg: `computation_building_blocks.ComputationBuildingBlock` instance of
federated type for which to construct intrinsic in order to call `fn` on
`arg`. `member` of `type_signature` of `arg` must be assignable to
`parameter` of `type_signature` of `fn`.
Returns:
Returns a `computation_building_blocks.Intrinsic` which can call
`fn` on `arg`.
"""
py_typecheck.check_type(fn,
computation_building_blocks.ComputationBuildingBlock)
py_typecheck.check_type(arg,
computation_building_blocks.ComputationBuildingBlock)
py_typecheck.check_type(fn.type_signature, computation_types.FunctionType)
py_typecheck.check_type(arg.type_signature, computation_types.FederatedType)
type_utils.check_assignable_from(fn.type_signature.parameter,
arg.type_signature.member)
if arg.type_signature.placement == placement_literals.SERVER:
result_type = computation_types.FederatedType(fn.type_signature.result,
arg.type_signature.placement,
arg.type_signature.all_equal)
intrinsic_type = computation_types.FunctionType(
[fn.type_signature, arg.type_signature], result_type)
intrinsic = computation_building_blocks.Intrinsic(
intrinsic_defs.FEDERATED_APPLY.uri, intrinsic_type)
tup = computation_building_blocks.Tuple((fn, arg))
return computation_building_blocks.Call(intrinsic, tup)
elif arg.type_signature.placement == placement_literals.CLIENTS:
return create_federated_map(fn, arg)
def test_propogates_dependence_up_through_lambda(self):
dummy_intrinsic = computation_building_blocks.Intrinsic(
'dummy_intrinsic', tf.int32)
lam = computation_building_blocks.Lambda('x', tf.int32, dummy_intrinsic)
dependent_nodes = tree_analysis.extract_nodes_consuming(
lam, dummy_intrinsic_predicate)
self.assertIn(lam, dependent_nodes)
def test_propogates_dependence_up_through_selection(self):
dummy_intrinsic = computation_building_blocks.Intrinsic(
'dummy_intrinsic', [tf.int32])
selection = computation_building_blocks.Selection(dummy_intrinsic, index=0)
dependent_nodes = tree_analysis.extract_nodes_consuming(
selection, dummy_intrinsic_predicate)
self.assertIn(selection, dependent_nodes)
def test_intrinsic_class_succeeds_simple_federated_map(self):
simple_function = computation_types.FunctionType(tf.int32, tf.float32)
federated_arg = computation_types.FederatedType(
simple_function.parameter, placements.CLIENTS)
federated_result = computation_types.FederatedType(
simple_function.result, placements.CLIENTS)
federated_map_concrete_type = computation_types.FunctionType(
[simple_function, federated_arg], federated_result)
concrete_federated_map = computation_building_blocks.Intrinsic(
intrinsic_defs.FEDERATED_MAP.uri, federated_map_concrete_type)
self.assertIsInstance(concrete_federated_map,
computation_building_blocks.Intrinsic)
self.assertEqual(
str(concrete_federated_map.type_signature),
'(<(int32 -> float32),{int32}@CLIENTS> -> {float32}@CLIENTS)')
self.assertEqual(concrete_federated_map.uri, 'federated_map')
self.assertEqual(concrete_federated_map.compact_representation(),
'federated_map')
concrete_federated_map_proto = concrete_federated_map.proto
self.assertEqual(
type_serialization.deserialize_type(
concrete_federated_map_proto.type),
concrete_federated_map.type_signature)
self.assertEqual(
concrete_federated_map_proto.WhichOneof('computation'),
'intrinsic')
self.assertEqual(concrete_federated_map_proto.intrinsic.uri,
concrete_federated_map.uri)
self._serialize_deserialize_roundtrip_test(concrete_federated_map)
def _create_call_to_federated_map(fn, arg):
r"""Creates a computation to call a federated map.
Call
/ \
Intrinsic Tuple
/ \
Computation Computation
Args:
fn: An instance of a functional
`computation_building_blocks.ComputationBuildingBlock` to use as the map
function.
arg: An instance of `computation_building_blocks.ComputationBuildingBlock`
to use as the map argument.
Returns:
An instance of `computation_building_blocks.Call` wrapping the federated map
computation.
"""
py_typecheck.check_type(
fn, computation_building_blocks.ComputationBuildingBlock)
py_typecheck.check_type(
arg, computation_building_blocks.ComputationBuildingBlock)
federated_type = computation_types.FederatedType(fn.type_signature.result,
placements.CLIENTS)
function_type = computation_types.FunctionType(
[fn.type_signature, arg.type_signature], federated_type)
intrinsic = computation_building_blocks.Intrinsic(
intrinsic_defs.FEDERATED_MAP.uri, function_type)
tup = computation_building_blocks.Tuple((fn, arg))
return computation_building_blocks.Call(intrinsic, tup)
def create_sequence_map(fn, arg):
r"""Creates a called sequence map.
Call
/ \
Intrinsic Tuple
|
[Comp, Comp]
Args:
fn: A `computation_building_blocks.ComputationBuildingBlock` to use as the
function.
arg: A `computation_building_blocks.ComputationBuildingBlock` to use as the
argument.
Returns:
A `computation_building_blocks.Call`.
Raises:
TypeError: If any of the types do not match.
"""
py_typecheck.check_type(
fn, computation_building_blocks.ComputationBuildingBlock)
py_typecheck.check_type(
arg, computation_building_blocks.ComputationBuildingBlock)
result_type = computation_types.SequenceType(fn.type_signature.result)
intrinsic_type = computation_types.FunctionType(
(fn.type_signature, arg.type_signature), result_type)
intrinsic = computation_building_blocks.Intrinsic(
intrinsic_defs.SEQUENCE_MAP.uri, intrinsic_type)
values = computation_building_blocks.Tuple((fn, arg))
return computation_building_blocks.Call(intrinsic, values)
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)
type_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(
str(value.type_signature)))
# TODO(b/113112108): Replace this as noted above.
result_type = computation_types.FederatedType(value.type_signature.member,
placements.SERVER, True)
intrinsic = value_impl.ValueImpl(
computation_building_blocks.Intrinsic(
intrinsic_defs.FEDERATED_SUM.uri,
computation_types.FunctionType(value.type_signature, result_type)),
self._context_stack)
return intrinsic(value)
def sequence_reduce(self, value, zero, op):
"""Implements `sequence_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`.
"""
value = value_impl.to_value(value, None, self._context_stack)
zero = value_impl.to_value(zero, None, self._context_stack)
op = value_impl.to_value(op, None, self._context_stack)
if isinstance(value.type_signature, computation_types.SequenceType):
element_type = value.type_signature.element
else:
py_typecheck.check_type(value.type_signature,
computation_types.FederatedType)
py_typecheck.check_type(value.type_signature.member,
computation_types.SequenceType)
element_type = value.type_signature.member.element
op_type_expected = type_constructors.reduction_op(
zero.type_signature, element_type)
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)
if isinstance(value.type_signature, computation_types.SequenceType):
return computation_constructing_utils.create_sequence_reduce(
value, zero, op)
else:
value_type = computation_types.SequenceType(element_type)
intrinsic_type = computation_types.FunctionType((
value_type,
zero.type_signature,
op.type_signature,
), op.type_signature.result)
intrinsic = computation_building_blocks.Intrinsic(
intrinsic_defs.SEQUENCE_REDUCE.uri, intrinsic_type)
ref = computation_building_blocks.Reference('arg', value_type)
tup = computation_building_blocks.Tuple((ref, zero, op))
call = computation_building_blocks.Call(intrinsic, tup)
fn = computation_building_blocks.Lambda(ref.name,
ref.type_signature, call)
fn_impl = value_impl.ValueImpl(fn, self._context_stack)
if value.type_signature.placement is placements.SERVER:
return self.federated_apply(fn_impl, value)
elif value.type_signature.placement is placements.CLIENTS:
return self.federated_map(fn_impl, value)
else:
raise TypeError('Unsupported placement {}.'.format(
value.type_signature.placement))
def sequence_reduce(self, value, zero, op):
"""Implements `sequence_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`.
"""
value = value_impl.to_value(value, None, self._context_stack)
zero = value_impl.to_value(zero, None, self._context_stack)
op = value_impl.to_value(op, None, self._context_stack)
if isinstance(value.type_signature, computation_types.SequenceType):
element_type = value.type_signature.element
else:
py_typecheck.check_type(value.type_signature,
computation_types.FederatedType)
py_typecheck.check_type(value.type_signature.member,
computation_types.SequenceType)
element_type = value.type_signature.member.element
op_type_expected = type_constructors.reduction_op(zero.type_signature,
element_type)
if not type_utils.is_assignable_from(op_type_expected, op.type_signature):
raise TypeError('Expected an operator of type {}, got {}.'.format(
str(op_type_expected), str(op.type_signature)))
sequence_reduce_building_block = computation_building_blocks.Intrinsic(
intrinsic_defs.SEQUENCE_REDUCE.uri,
computation_types.FunctionType([
computation_types.SequenceType(element_type), zero.type_signature,
op.type_signature
], zero.type_signature))
if isinstance(value.type_signature, computation_types.SequenceType):
sequence_reduce_intrinsic = value_impl.ValueImpl(
sequence_reduce_building_block, self._context_stack)
return sequence_reduce_intrinsic(value, zero, op)
else:
federated_mapping_fn_building_block = computation_building_blocks.Lambda(
'arg', computation_types.SequenceType(element_type),
computation_building_blocks.Call(
sequence_reduce_building_block,
computation_building_blocks.Tuple([
computation_building_blocks.Reference(
'arg', computation_types.SequenceType(element_type)),
value_impl.ValueImpl.get_comp(zero),
value_impl.ValueImpl.get_comp(op)
])))
federated_mapping_fn = value_impl.ValueImpl(
federated_mapping_fn_building_block, self._context_stack)
if value.type_signature.placement is placements.SERVER:
return self.federated_apply(federated_mapping_fn, value)
elif value.type_signature.placement is placements.CLIENTS:
return self.federated_map(federated_mapping_fn, value)
else:
raise TypeError('Unsupported placement {}.'.format(
str(value.type_signature.placement)))
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)
type_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.')
# TODO(b/113112108): Replace this hand-crafted logic here and below with
# a call to a helper function that handles it in a uniform manner after
# implementing support for correctly typechecking federated template types
# and instantiating template types on concrete arguments.
result_type = computation_types.FederatedType(value.type_signature.member,
placements.CLIENTS, True)
intrinsic = value_impl.ValueImpl(
computation_building_blocks.Intrinsic(
intrinsic_defs.FEDERATED_BROADCAST.uri,
computation_types.FunctionType(value.type_signature, result_type)),
self._context_stack)
return intrinsic(value)
def _create_lambda_to_add_one(dtype):
r"""Creates a computation to add `1` to an argument.
Lambda
\
Call
/ \
Intrinsic Tuple
/ \
Reference Computation
Args:
dtype: The type of the argument.
Returns:
An instance of `computation_building_blocks.Lambda` wrapping a function that
adds 1 to an argument.
"""
if isinstance(dtype, computation_types.TensorType):
dtype = dtype.dtype
py_typecheck.check_type(dtype, tf.dtypes.DType)
function_type = computation_types.FunctionType([dtype, dtype], dtype)
intrinsic = computation_building_blocks.Intrinsic(
intrinsic_defs.GENERIC_PLUS.uri, function_type)
arg = computation_building_blocks.Reference('arg', dtype)
constant = _create_call_to_py_fn(lambda: tf.cast(tf.constant(1), dtype))
tup = computation_building_blocks.Tuple([arg, constant])
call = computation_building_blocks.Call(intrinsic, tup)
return computation_building_blocks.Lambda(arg.name, arg.type_signature,
call)
def create_federated_map(fn, arg):
r"""Creates a called federated map.
Call
/ \
Intrinsic Tuple
|
[Comp, Comp]
Args:
fn: A `computation_building_blocks.ComputationBuildingBlock` to use as the
function.
arg: A `computation_building_blocks.ComputationBuildingBlock` to use as the
argument.
Returns:
A `computation_building_blocks.Call`.
Raises:
TypeError: If any of the types do not match.
"""
py_typecheck.check_type(
fn, computation_building_blocks.ComputationBuildingBlock)
py_typecheck.check_type(
arg, computation_building_blocks.ComputationBuildingBlock)
result_type = computation_types.FederatedType(fn.type_signature.result,
placement_literals.CLIENTS,
False)
intrinsic_type = computation_types.FunctionType(
(fn.type_signature, arg.type_signature), result_type)
intrinsic = computation_building_blocks.Intrinsic(
intrinsic_defs.FEDERATED_MAP.uri, intrinsic_type)
values = computation_building_blocks.Tuple((fn, arg))
return computation_building_blocks.Call(intrinsic, values)
def create_federated_sum(value):
r"""Creates a called federated sum.
Call
/ \
Intrinsic Comp
Args:
value: A `computation_building_blocks.ComputationBuildingBlock` to use as
the value.
Returns:
A `computation_building_blocks.Call`.
Raises:
TypeError: If any of the types do not match.
"""
py_typecheck.check_type(
value, computation_building_blocks.ComputationBuildingBlock)
result_type = computation_types.FederatedType(value.type_signature.member,
placement_literals.SERVER,
True)
intrinsic_type = computation_types.FunctionType(value.type_signature,
result_type)
intrinsic = computation_building_blocks.Intrinsic(
intrinsic_defs.FEDERATED_SUM.uri, intrinsic_type)
return computation_building_blocks.Call(intrinsic, value)
def _create_lambda_to_dummy_intrinsic(type_spec, uri='dummy'):
r"""Creates a lambda to call a dummy intrinsic.
Lambda
\
Call
/ \
Intrinsic Ref(arg)
Args:
type_spec: The type of the argument.
uri: The URI of the intrinsic.
Returns:
A `computation_building_blocks.Lambda`.
Raises:
TypeError: If `type_spec` is not a `tf.dtypes.DType`.
"""
py_typecheck.check_type(type_spec, tf.dtypes.DType)
intrinsic_type = computation_types.FunctionType(type_spec, type_spec)
intrinsic = computation_building_blocks.Intrinsic(uri, intrinsic_type)
arg = computation_building_blocks.Reference('arg', type_spec)
call = computation_building_blocks.Call(intrinsic, arg)
return computation_building_blocks.Lambda(arg.name, arg.type_signature,
call)
def create_federated_value(value, placement):
r"""Creates a called federated value.
Call
/ \
Intrinsic Comp
Args:
value: A `computation_building_blocks.ComputationBuildingBlock` to use as
the value.
placement: A `placement_literals.PlacementLiteral` to use as the placement.
Returns:
A `computation_building_blocks.Call`.
Raises:
TypeError: If any of the types do not match.
"""
py_typecheck.check_type(
value, computation_building_blocks.ComputationBuildingBlock)
if placement is placement_literals.CLIENTS:
uri = intrinsic_defs.FEDERATED_VALUE_AT_CLIENTS.uri
elif placement is placement_literals.SERVER:
uri = intrinsic_defs.FEDERATED_VALUE_AT_SERVER.uri
else:
raise TypeError('Unsupported placement {}.'.format(placement))
result_type = computation_types.FederatedType(value.type_signature,
placement, True)
intrinsic_type = computation_types.FunctionType(value.type_signature,
result_type)
intrinsic = computation_building_blocks.Intrinsic(uri, intrinsic_type)
return computation_building_blocks.Call(intrinsic, value)
def federated_value(self, value, placement):
"""Implements `federated_value` as defined in `api/intrinsics.py`.
Args:
value: As in `api/intrinsics.py`.
placement: 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)
# TODO(b/113112108): Verify that neither the value, nor any of its parts
# are of a federated type.
if placement is placements.CLIENTS:
uri = intrinsic_defs.FEDERATED_VALUE_AT_CLIENTS.uri
elif placement is placements.SERVER:
uri = intrinsic_defs.FEDERATED_VALUE_AT_SERVER.uri
else:
raise TypeError('The placement must be either CLIENTS or SERVER.')
result_type = computation_types.FederatedType(value.type_signature,
placement, True)
intrinsic = value_impl.ValueImpl(
computation_building_blocks.Intrinsic(
uri,
computation_types.FunctionType(value.type_signature, result_type)),
self._context_stack)
return intrinsic(value)
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)
type_utils.check_federated_value_placement(value, placements.CLIENTS,
'value to be collected')
result_type = computation_types.FederatedType(
computation_types.SequenceType(value.type_signature.member),
placements.SERVER, True)
intrinsic = value_impl.ValueImpl(
computation_building_blocks.Intrinsic(
intrinsic_defs.FEDERATED_COLLECT.uri,
computation_types.FunctionType(value.type_signature, result_type)),
self._context_stack)
return intrinsic(value)
def _create_zip_two_values(value):
r"""Creates a called federated zip with two values.
Call
/ \
Intrinsic Tuple
|
[Comp1, Comp2]
Notice that this function will drop any names associated to the two-tuple it
is processing. This is necessary due to the type signature of the
underlying federated zip intrinsic, `<T@P,U@P>-><T,U>@P`. Keeping names
here would violate this type signature. The names are cached at a higher
level than this function, and appended to the resulting tuple in a single
call to `federated_map` or `federated_apply` before the resulting structure
is sent back to the caller.
Args:
value: A `computation_building_blocks.ComputationBuildingBlock` with a
`type_signature` of type `computation_types.NamedTupleType` containing
exactly two elements.
Returns:
A `computation_building_blocks.Call`.
Raises:
TypeError: If any of the types do not match.
ValueError: If `value` does not contain exactly two elements.
"""
py_typecheck.check_type(
value, computation_building_blocks.ComputationBuildingBlock)
named_type_signatures = anonymous_tuple.to_elements(value.type_signature)
length = len(named_type_signatures)
if length != 2:
raise ValueError(
'Expected a value with exactly two elements, received {} elements.'
.format(named_type_signatures))
placement = value[0].type_signature.placement
if placement is placement_literals.CLIENTS:
uri = intrinsic_defs.FEDERATED_ZIP_AT_CLIENTS.uri
all_equal = False
elif placement is placement_literals.SERVER:
uri = intrinsic_defs.FEDERATED_ZIP_AT_SERVER.uri
all_equal = True
else:
raise TypeError('Unsupported placement {}.'.format(placement))
elements = []
for _, type_signature in named_type_signatures:
federated_type = computation_types.FederatedType(
type_signature.member, placement, all_equal)
elements.append((None, federated_type))
parameter_type = computation_types.NamedTupleType(elements)
result_type = computation_types.FederatedType(
[(None, e.member) for _, e in named_type_signatures], placement,
all_equal)
intrinsic_type = computation_types.FunctionType(parameter_type,
result_type)
intrinsic = computation_building_blocks.Intrinsic(uri, intrinsic_type)
return computation_building_blocks.Call(intrinsic, value)
def zip_two_tuple(input_val, context_stack):
"""Helper function to perform 2-tuple at a time zipping.
Takes 2-tuple of federated values and returns federated 2-tuple of values.
Args:
input_val: 2-tuple TFF `Value` of `NamedTuple` type, whose elements must be
`FederatedTypes` with the same placement.
context_stack: The context stack to use, as in `impl.value_impl.to_value`.
Returns:
TFF `Value` of `FederatedType` with member of 2-tuple `NamedTuple` type.
"""
py_typecheck.check_type(input_val, value_base.Value)
py_typecheck.check_type(input_val.type_signature,
computation_types.NamedTupleType)
py_typecheck.check_type(input_val[0].type_signature,
computation_types.FederatedType)
zip_uris = {
placements.CLIENTS: intrinsic_defs.FEDERATED_ZIP_AT_CLIENTS.uri,
placements.SERVER: intrinsic_defs.FEDERATED_ZIP_AT_SERVER.uri,
}
zip_all_equal = {
placements.CLIENTS: False,
placements.SERVER: True,
}
output_placement = input_val[0].type_signature.placement
if output_placement not in zip_uris:
raise TypeError(
'The argument must have components placed at SERVER or '
'CLIENTS')
output_all_equal_bit = zip_all_equal[output_placement]
for elem in input_val:
type_utils.check_federated_value_placement(elem, output_placement)
num_elements = len(anonymous_tuple.to_elements(input_val.type_signature))
if num_elements != 2:
raise ValueError('The argument of zip_two_tuple must be a 2-tuple, '
'not an {}-tuple'.format(num_elements))
result_type = computation_types.FederatedType(
[(name, e.member)
for name, e in anonymous_tuple.to_elements(input_val.type_signature)],
output_placement, output_all_equal_bit)
def _adjust_all_equal_bit(x):
return computation_types.FederatedType(x.member, x.placement,
output_all_equal_bit)
adjusted_input_type = computation_types.NamedTupleType([
(k, _adjust_all_equal_bit(v)) if k else _adjust_all_equal_bit(v)
for k, v in anonymous_tuple.to_elements(input_val.type_signature)
])
intrinsic = value_impl.ValueImpl(
computation_building_blocks.Intrinsic(
zip_uris[output_placement],
computation_types.FunctionType(adjusted_input_type, result_type)),
context_stack)
return intrinsic(input_val)
def test_passes_with_federated_map(self):
intrinsic = computation_building_blocks.Intrinsic(
intrinsic_defs.FEDERATED_MAP.uri,
computation_types.FunctionType([
computation_types.FunctionType(tf.int32, tf.float32),
computation_types.FederatedType(tf.int32, placements.CLIENTS)
], computation_types.FederatedType(tf.float32, placements.CLIENTS)))
tree_analysis.check_intrinsics_whitelisted_for_reduction(intrinsic)
def test_returns_string_for_intrinsic(self):
comp = computation_building_blocks.Intrinsic('intrinsic', tf.int32)
compact_string = comp.compact_representation()
self.assertEqual(compact_string, 'intrinsic')
formatted_string = comp.formatted_representation()
self.assertEqual(formatted_string, 'intrinsic')
structural_string = comp.structural_representation()
self.assertEqual(structural_string, 'intrinsic')
def _transform(comp):
"""Returns a new transformed computation or `comp`."""
if not _should_transform(comp):
return comp, False
def _create_block_to_chained_calls(comps):
r"""Constructs a transformed block computation from `comps`.
Block
/ \
[fn=Tuple] Lambda(arg)
| \
[Comp(y), Comp(x)] Call
/ \
Sel(1) Call
/ / \
Ref(fn) Sel(0) Ref(arg)
/
Ref(fn)
(let fn=<y, x> in (arg -> fn[1](fn[0](arg)))
Args:
comps: a Python list of computations.
Returns:
A `computation_building_blocks.Block`.
"""
functions = computation_building_blocks.Tuple(comps)
fn_ref = computation_building_blocks.Reference(
'fn', functions.type_signature)
arg_type = comps[0].type_signature.parameter
arg_ref = computation_building_blocks.Reference('arg', arg_type)
arg = arg_ref
for index, _ in enumerate(comps):
fn_sel = computation_building_blocks.Selection(fn_ref,
index=index)
call = computation_building_blocks.Call(fn_sel, arg)
arg = call
lam = computation_building_blocks.Lambda(arg_ref.name,
arg_ref.type_signature,
call)
return computation_building_blocks.Block([('fn', functions)], lam)
block = _create_block_to_chained_calls((
comp.argument[1].argument[0],
comp.argument[0],
))
arg = computation_building_blocks.Tuple([
block,
comp.argument[1].argument[1],
])
intrinsic_type = computation_types.FunctionType(
arg.type_signature, comp.function.type_signature.result)
intrinsic = computation_building_blocks.Intrinsic(
comp.function.uri, intrinsic_type)
transformed_comp = computation_building_blocks.Call(intrinsic, arg)
return transformed_comp, True
def test_propogates_dependence_up_through_block_locals(self):
dummy_intrinsic = computation_building_blocks.Intrinsic(
'dummy_intrinsic', tf.int32)
integer_reference = computation_building_blocks.Reference('int', tf.int32)
block = computation_building_blocks.Block([('x', dummy_intrinsic)],
integer_reference)
dependent_nodes = tree_analysis.extract_nodes_consuming(
block, dummy_intrinsic_predicate)
self.assertIn(block, dependent_nodes)
def test_propogates_dependence_up_through_tuple(self):
dummy_intrinsic = computation_building_blocks.Intrinsic(
'dummy_intrinsic', tf.int32)
integer_reference = computation_building_blocks.Reference('int', tf.int32)
tup = computation_building_blocks.Tuple(
[integer_reference, dummy_intrinsic])
dependent_nodes = tree_analysis.extract_nodes_consuming(
tup, dummy_intrinsic_predicate)
self.assertIn(tup, dependent_nodes)
def test_raises_with_federated_mean(self):
intrinsic = computation_building_blocks.Intrinsic(
intrinsic_defs.FEDERATED_MEAN.uri,
computation_types.FunctionType(
computation_types.FederatedType(tf.int32, placements.CLIENTS),
computation_types.FederatedType(tf.int32, placements.SERVER)))
with self.assertRaisesRegex(ValueError,
intrinsic.compact_representation()):
tree_analysis.check_intrinsics_whitelisted_for_reduction(intrinsic)
def federated_map(self, mapping_fn, value):
"""Implements `federated_map` as defined in `api/intrinsics.py`.
Args:
mapping_fn: As in `api/intrinsics.py`.
value: As in `api/intrinsics.py`.
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.
value = value_impl.to_value(value, None, self._context_stack)
if isinstance(value.type_signature, computation_types.NamedTupleType):
if len(anonymous_tuple.to_elements(value.type_signature)) >= 2:
# We've been passed a value which the user expects to be zipped.
value = self.federated_zip(value)
type_utils.check_federated_value_placement(value, 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.
mapping_fn = value_impl.to_value(mapping_fn, None, self._context_stack)
py_typecheck.check_type(mapping_fn, value_base.Value)
py_typecheck.check_type(mapping_fn.type_signature,
computation_types.FunctionType)
if not type_utils.is_assignable_from(mapping_fn.type_signature.parameter,
value.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(
str(mapping_fn.type_signature.parameter),
str(value.type_signature.member)))
# TODO(b/113112108): Replace this as noted above.
result_type = computation_types.FederatedType(
mapping_fn.type_signature.result, placements.CLIENTS,
value.type_signature.all_equal)
intrinsic = value_impl.ValueImpl(
computation_building_blocks.Intrinsic(
intrinsic_defs.FEDERATED_MAP.uri,
computation_types.FunctionType(
[mapping_fn.type_signature, value.type_signature],
result_type)), self._context_stack)
return intrinsic(mapping_fn, value)
def create_federated_mean(value, weight):
r"""Creates a called federated mean.
Call
/ \
Intrinsic Tuple
|
[Comp, Comp]
Args:
value: A `computation_building_blocks.ComputationBuildingBlock` to use as
the value.
weight: A `computation_building_blocks.ComputationBuildingBlock` to use as
the weight or `None`.
Returns:
A `computation_building_blocks.Call`.
Raises:
TypeError: If any of the types do not match.
"""
py_typecheck.check_type(
value, computation_building_blocks.ComputationBuildingBlock)
if weight is not None:
py_typecheck.check_type(
weight, computation_building_blocks.ComputationBuildingBlock)
result_type = computation_types.FederatedType(value.type_signature.member,
placement_literals.SERVER,
True)
if weight is not None:
intrinsic_type = computation_types.FunctionType(
(value.type_signature, weight.type_signature), result_type)
intrinsic = computation_building_blocks.Intrinsic(
intrinsic_defs.FEDERATED_WEIGHTED_MEAN.uri, intrinsic_type)
values = computation_building_blocks.Tuple((value, weight))
return computation_building_blocks.Call(intrinsic, values)
else:
intrinsic_type = computation_types.FunctionType(
value.type_signature, result_type)
intrinsic = computation_building_blocks.Intrinsic(
intrinsic_defs.FEDERATED_MEAN.uri, intrinsic_type)
return computation_building_blocks.Call(intrinsic, value)
def test_propogates_dependence_up_through_call(self):
dummy_intrinsic = computation_building_blocks.Intrinsic(
'dummy_intrinsic', tf.int32)
ref_to_x = computation_building_blocks.Reference('x', tf.int32)
identity_lambda = computation_building_blocks.Lambda(
'x', tf.int32, ref_to_x)
called_lambda = computation_building_blocks.Call(
identity_lambda, dummy_intrinsic)
dependent_nodes = tree_analysis.extract_nodes_consuming(
called_lambda, dummy_intrinsic_predicate)
self.assertIn(called_lambda, dependent_nodes)
def _make_sequence_sum_for(type_spec):
py_typecheck.check_type(type_spec, computation_types.SequenceType)
if not type_utils.is_sum_compatible(type_spec.element):
raise TypeError(
'The value type {} is not compatible with the sum operator.'.format(
str(type_spec)))
return value_impl.ValueImpl(
computation_building_blocks.Intrinsic(
intrinsic_defs.SEQUENCE_SUM.uri,
computation_types.FunctionType(type_spec, type_spec.element)),
self._context_stack)
