def _construct_selection_from_federated_tuple(federated_tuple, selected_index,
name_generator):
"""Selects the index `selected_index` from `federated_tuple`.
Args:
federated_tuple: Instance of `tff_framework.ComputationBuildingBlock` of
federated named tuple type from which we wish to select one of the tuple's
elements.
selected_index: Integer index we wish to select from `federated_tuple`.
name_generator: `generator` to generate unique names in the construction.
Returns:
An instance of `tff_framework.ComputationBuildingBlock` representing index
`selected_index` from `federated_tuple`, still federated at the same
placement.
"""
py_typecheck.check_type(federated_tuple,
tff_framework.ComputationBuildingBlock)
py_typecheck.check_type(selected_index, int)
py_typecheck.check_type(federated_tuple.type_signature, tff.FederatedType)
py_typecheck.check_type(federated_tuple.type_signature.member,
tff.NamedTupleType)
unique_reference_name = six.next(name_generator)
selection_function_ref = tff_framework.Reference(
unique_reference_name, federated_tuple.type_signature.member)
selected_building_block = tff_framework.Selection(
selection_function_ref, index=selected_index)
constructed_selection_function = tff_framework.Lambda(
unique_reference_name, federated_tuple.type_signature.member,
selected_building_block)
return tff_framework.create_federated_map_or_apply(
constructed_selection_function, federated_tuple)
def select_output_from_lambda(comp, indices):
"""Constructs a new function with result of selecting `indices` from `comp`.
Args:
comp: Instance of `tff_framework.Lambda` of result type `tff.NamedTupleType`
from which we wish to select `indices`. Notice that this named tuple type
must have elements of federated type.
indices: Instance of `int`, `list`, or `tuple`, specifying the indices we
wish to select from the result of `comp`. If `indices` is an `int`, the
result of the returned `comp` will be of type at index `indices` in
`comp.type_signature.result`. If `indices` is a `list` or `tuple`, the
result type will be a `tff.NamedTupleType` wrapping the specified
selections.
Returns:
A transformed version of `comp` with result value the selection from the
result of `comp` specified by `indices`.
"""
py_typecheck.check_type(comp, tff_framework.Lambda)
py_typecheck.check_type(comp.type_signature.result, tff.NamedTupleType)
py_typecheck.check_type(indices, (int, tuple, list))
result_tuple = comp.result
name_generator = tff_framework.unique_name_generator(comp)
new_name = six.next(name_generator)
ref_to_result_tuple = tff_framework.Reference(new_name,
result_tuple.type_signature)
if isinstance(indices, (tuple, list)):
if not all(isinstance(x, int) for x in indices):
raise TypeError('Must select by index in `select_output_from_lambda`.')
selected_output = [
tff_framework.Selection(ref_to_result_tuple, index=x) for x in indices
]
tuple_of_selected_output = tff_framework.Tuple(selected_output)
result = tff_framework.Block([(new_name, result_tuple)],
tuple_of_selected_output)
else:
selected_output = tff_framework.Selection(
ref_to_result_tuple, index=indices)
result = tff_framework.Block([(new_name, result_tuple)], selected_output)
return tff_framework.Lambda(comp.parameter_name, comp.parameter_type, result)
def bind_single_selection_as_argument_to_lower_level_lambda(comp, index):
r"""Binds selection from the param of `comp` as param to lower-level lambda.
The returned pattern is quite important here; given an input lambda `comp`,
we will return an equivalent structure of the form:
Lambda(x)
|
Call
/ \
Lambda Selection from x
Args:
comp: Instance of `tff_framework.Lambda`, whose parameters we wish to rebind
to a different lambda. This lambda must have unique names.
index: `int` representing the index to bind as an argument to the
lower-level lambda.
Returns:
An instance of `tff_framework.Lambda`, equivalent to `comp`, satisfying the
pattern above.
"""
py_typecheck.check_type(comp, tff_framework.Lambda)
py_typecheck.check_type(index, int)
tff_framework.check_has_unique_names(comp)
comp = _prepare_for_rebinding(comp)
name_generator = tff_framework.unique_name_generator(comp)
parameter_name = comp.parameter_name
new_name = six.next(name_generator)
new_ref = tff_framework.Reference(new_name,
comp.type_signature.parameter[index])
def _remove_selection_from_ref(inner_comp):
if isinstance(inner_comp, tff_framework.Selection) and isinstance(
inner_comp.source, tff_framework.Reference
) and inner_comp.index == index and inner_comp.source.name == parameter_name:
return new_ref, True
return inner_comp, False
references_rebound_in_result, _ = tff_framework.transform_postorder(
comp.result, _remove_selection_from_ref)
newly_bound_lambda = tff_framework.Lambda(new_ref.name,
new_ref.type_signature,
references_rebound_in_result)
_check_for_missed_binding(comp, newly_bound_lambda)
original_ref = tff_framework.Reference(comp.parameter_name,
comp.parameter_type)
selection = tff_framework.Selection(original_ref, index=index)
called_rebound = tff_framework.Call(newly_bound_lambda, selection)
return tff_framework.Lambda(comp.parameter_name, comp.parameter_type,
called_rebound)
def zip_selection_as_argument_to_lower_level_lambda(comp, selected_index_lists):
r"""Binds selections from the param of `comp` as params to lower-level lambda.
Notice that `comp` must be a `tff_framework.Lambda`.
The returned pattern is quite important here; given an input lambda `Comp`,
we will return an equivalent structure of the form:
Lambda(x)
|
Call
/ \
Lambda <Selections from x>
Where <Selections from x> represents a tuple of selections from the parameter
`x`, as specified by `selected_index_lists`. This transform is necessary in
order to isolate spurious dependence on arguments that are not in fact used,
for example after we have separated processing on the server from that which
happens on the clients, but the server-processing still declares some
parameters placed at the clients.
`selected_index_lists` must be a list of lists. Each list represents
a sequence of selections to the parameter of `comp`. For example, if `var`
is the parameter of `comp`, the list `[0, 1, 0]` would represent the
selection `x[0][1][0]`. The elements of these inner lists must be integers;
that is, the selections must be positional. Notice we do not allow for tuples
due to automatic unwrapping.
Args:
comp: Instance of `tff_framework.Lambda`, whose parameters we wish to rebind
to a different lambda.
selected_index_lists: 2-d list of `int`s, specifying the parameters of
`comp` which we wish to rebind as the parameter to a lower-level lambda.
Returns:
An instance of `tff_framework.Lambda`, equivalent to `comp`, satisfying the
pattern above.
"""
py_typecheck.check_type(comp, tff_framework.Lambda)
py_typecheck.check_type(selected_index_lists, list)
for selection_list in selected_index_lists:
py_typecheck.check_type(selection_list, list)
for selected_element in selection_list:
py_typecheck.check_type(selected_element, int)
original_comp = comp
comp = _prepare_for_rebinding(comp)
top_level_parameter_type = comp.type_signature.parameter
name_generator = tff_framework.unique_name_generator(comp)
top_level_parameter_name = comp.parameter_name
top_level_parameter_reference = tff_framework.Reference(
top_level_parameter_name, comp.parameter_type)
type_list = []
for selection_list in selected_index_lists:
try:
selected_type = top_level_parameter_type
for selection in selection_list:
selected_type = selected_type[selection]
type_list.append(selected_type)
except TypeError:
six.reraise(
TypeError,
TypeError(
'You have tried to bind a variable to a nonexistent index in your '
'lambda parameter type; the selection defined by {} is '
'inadmissible for the lambda parameter type {}, in the comp {}.'
.format(selection_list, top_level_parameter_type, original_comp)),
sys.exc_info()[2])
if not all(isinstance(x, tff.FederatedType) for x in type_list):
raise TypeError(
'All selected arguments should be of federated type; your selections '
'have resulted in the list of types {}'.format(type_list))
placement = type_list[0].placement
if not all(x.placement is placement for x in type_list):
raise ValueError(
'In order to zip the argument to the lower-level lambda together, all '
'selected arguments should be at the same placement. Your selections '
'have resulted in the list of types {}'.format(type_list))
arg_to_lower_level_lambda_list = []
for selection_tuple in selected_index_lists:
selected_comp = top_level_parameter_reference
for selection in selection_tuple:
selected_comp = tff_framework.Selection(selected_comp, index=selection)
arg_to_lower_level_lambda_list.append(selected_comp)
zip_arg = tff_framework.create_federated_zip(
tff_framework.Tuple(arg_to_lower_level_lambda_list))
zip_type = tff.FederatedType([x.member for x in type_list],
placement=placement)
ref_to_zip = tff_framework.Reference(six.next(name_generator), zip_type)
selections_from_zip = [
_construct_selection_from_federated_tuple(ref_to_zip, x, name_generator)
for x in range(len(selected_index_lists))
]
def _replace_selections_with_new_bindings(inner_comp):
"""Identifies selection pattern and replaces with new binding.
Detecting this pattern is the most brittle part of this rebinding function.
It relies on pattern-matching, and right now we cannot guarantee that this
pattern is present in every situation we wish to replace with a new
binding.
Args:
inner_comp: Instance of `tff_framework.ComputationBuildingBlock` in which
we wish to replace the selections specified by `selected_index_lists`
with the parallel new bindings from `selections_from_zip`.
Returns:
A possibly transformed version of `inner_comp` with nodes matching the
selection patterns replaced by their new bindings.
"""
# TODO(b/135541729): Either come up with a preprocessing way to enforce
# this is sufficient, or rework the should_transform predicate.
for idx, tup in enumerate(selected_index_lists):
selection = inner_comp # Empty selection
tuple_pattern_matched = True
for selected_index in tup[::-1]:
if isinstance(
selection,
tff_framework.Selection) and selection.index == selected_index:
selection = selection.source
else:
tuple_pattern_matched = False
break
if tuple_pattern_matched:
if isinstance(selection, tff_framework.Reference
) and selection.name == top_level_parameter_name:
return selections_from_zip[idx], True
return inner_comp, False
variables_rebound_in_result, _ = tff_framework.transform_postorder(
comp.result, _replace_selections_with_new_bindings)
lambda_with_zipped_param = tff_framework.Lambda(ref_to_zip.name,
ref_to_zip.type_signature,
variables_rebound_in_result)
_check_for_missed_binding(comp, lambda_with_zipped_param)
zipped_lambda_called = tff_framework.Call(lambda_with_zipped_param, zip_arg)
constructed_lambda = tff_framework.Lambda(comp.parameter_name,
comp.parameter_type,
zipped_lambda_called)
names_uniquified, _ = tff_framework.uniquify_reference_names(
constructed_lambda)
return names_uniquified
def _split_by_intrinsic(comp, uri):
"""Splits `comp` into `before` and `after` the intrinsic for the given `uri`.
This function finds the intrinsic for the given `uri` in `comp`; splits `comp`
into two computations `before` and `after` the intrinsic; and returns a Python
tuple representing the pair of `before` and `after` computations.
NOTE: This function is generally safe to call on computations that do not fit
into canonical form. It is left to the caller to determine if the resulting
computations are expected.
Args:
comp: The `tff_framework.Lambda` to transform.
uri: A URI of an intrinsic.
Returns:
A pair of `tff_framework.ComputationBuildingBlock`s representing the
computations `before` and `after` the intrinsic.
Raises:
ValueError: If `comp` is not a `tff_framework.Lambda` referencing a
`tff_framework.Block` referencing a collections of variables containing an
intrinsic with the given `uri` or if there is more than one intrinsic with
the given `uri` in `comp`.
"""
py_typecheck.check_type(comp, tff_framework.Lambda)
py_typecheck.check_type(uri, six.string_types)
py_typecheck.check_type(comp.result, tff_framework.Block)
def _get_called_intrinsic_from_block_variables(variables, uri):
for index, (name, variable) in enumerate(variables):
if tff_framework.is_called_intrinsic(variable, uri):
return index, name, variable
raise ValueError(
'Expected a lambda referencing a block referencing a collection of '
'variables containing an intrinsic with the uri: {}, found None.'
.format(uri))
index, name, variable = _get_called_intrinsic_from_block_variables(
comp.result.locals, uri)
intrinsics = _get_called_intrinsics(comp, uri)
length = len(intrinsics)
if length != 1:
raise ValueError(
'Expected a computation with exactly one intrinsic with the uri: {}, '
'found: {}.'.format(uri, length))
name_generator = tff_framework.unique_name_generator(comp)
before = tff_framework.Lambda(comp.parameter_name, comp.parameter_type,
variable.argument)
parameter_type = tff.NamedTupleType(
(comp.parameter_type, variable.type_signature))
ref_name = six.next(name_generator)
ref = tff_framework.Reference(ref_name, parameter_type)
sel_0 = tff_framework.Selection(ref, index=0)
sel_1 = tff_framework.Selection(ref, index=1)
variables = comp.result.locals
variables[index] = (name, sel_1)
variables.insert(0, (comp.parameter_name, sel_0))
block = tff_framework.Block(variables, comp.result.result)
after = tff_framework.Lambda(ref.name, ref.type_signature, block)
return before, after
def _transform(comp):
if not _should_transform(comp):
return comp, False
index = intrinsics.index(comp)
comp = tff_framework.Selection(ref, index=index)
return comp, True
def get_canonical_form_for_iterative_process(iterative_process):
"""Constructs `tff.backends.mapreduce.CanonicalForm` given iterative process.
This function transforms computations from the input `iterative_process` into
an instance of `tff.backends.mapreduce.CanonicalForm`.
Args:
iterative_process: An instance of `tff.utils.IterativeProcess`.
Returns:
An instance of `tff.backends.mapreduce.CanonicalForm` equivalent to this
process.
Raises:
TypeError: If the arguments are of the wrong types.
transformations.CanonicalFormCompilationError: If the compilation
process fails.
"""
py_typecheck.check_type(iterative_process,
computation_utils.IterativeProcess)
initialize_comp = tff_framework.ComputationBuildingBlock.from_proto(
iterative_process.initialize._computation_proto) # pylint: disable=protected-access
next_comp = tff_framework.ComputationBuildingBlock.from_proto(
iterative_process.next._computation_proto) # pylint: disable=protected-access
if not (isinstance(next_comp.type_signature.parameter, tff.NamedTupleType)
and isinstance(next_comp.type_signature.result,
tff.NamedTupleType)):
raise TypeError(
'Any IterativeProcess compatible with CanonicalForm must '
'have a `next` function which takes and returns instances '
'of `tff.NamedTupleType`; your next function takes '
'parameters of type {} and returns results of type {}'.format(
next_comp.type_signature.parameter,
next_comp.type_signature.result))
if len(next_comp.type_signature.result) == 2:
next_result = next_comp.result
dummy_clients_metrics_appended = tff_framework.Tuple([
tff_framework.Selection(next_result, index=0),
tff_framework.Selection(next_result, index=1),
tff.federated_value([], tff.CLIENTS)._comp # pylint: disable=protected-access
])
next_comp = tff_framework.Lambda(next_comp.parameter_name,
next_comp.parameter_type,
dummy_clients_metrics_appended)
initialize_comp = tff_framework.replace_intrinsics_with_bodies(
initialize_comp)
next_comp = tff_framework.replace_intrinsics_with_bodies(next_comp)
tff_framework.check_intrinsics_whitelisted_for_reduction(initialize_comp)
tff_framework.check_intrinsics_whitelisted_for_reduction(next_comp)
tff_framework.check_broadcast_not_dependent_on_aggregate(next_comp)
before_broadcast, after_broadcast = (
transformations.force_align_and_split_by_intrinsic(
next_comp, tff_framework.FEDERATED_BROADCAST.uri))
before_aggregate, after_aggregate = (
transformations.force_align_and_split_by_intrinsic(
after_broadcast, tff_framework.FEDERATED_AGGREGATE.uri))
init_info_packed = pack_initialize_comp_type_signature(
initialize_comp.type_signature)
next_info_packed = pack_next_comp_type_signature(next_comp.type_signature,
init_info_packed)
before_broadcast_info_packed = (
check_and_pack_before_broadcast_type_signature(
before_broadcast.type_signature, next_info_packed))
before_aggregate_info_packed = (
check_and_pack_before_aggregate_type_signature(
before_aggregate.type_signature, before_broadcast_info_packed))
canonical_form_types = check_and_pack_after_aggregate_type_signature(
after_aggregate.type_signature, before_aggregate_info_packed)
initialize = transformations.consolidate_and_extract_local_processing(
initialize_comp)
if not (isinstance(initialize, tff_framework.CompiledComputation)
and initialize.type_signature.result
== canonical_form_types['initialize_type'].member):
raise transformations.CanonicalFormCompilationError(
'Compilation of initialize has failed. Expected to extract a '
'`tff_framework.CompiledComputation` of type {}, instead we extracted '
'a {} of type {}.'.format(next_comp.type_signature.parameter[0],
type(initialize),
initialize.type_signature.result))
prepare = extract_prepare(before_broadcast, canonical_form_types)
work = extract_work(before_aggregate, after_aggregate,
canonical_form_types)
zero_noarg_function, accumulate, merge, report = extract_aggregate_functions(
before_aggregate, canonical_form_types)
update = extract_update(after_aggregate, canonical_form_types)
cf = canonical_form.CanonicalForm(
tff_framework.building_block_to_computation(initialize),
tff_framework.building_block_to_computation(prepare),
tff_framework.building_block_to_computation(work),
tff_framework.building_block_to_computation(zero_noarg_function),
tff_framework.building_block_to_computation(accumulate),
tff_framework.building_block_to_computation(merge),
tff_framework.building_block_to_computation(report),
tff_framework.building_block_to_computation(update))
return cf