def concatenate_function_outputs(first_function, second_function):
"""Constructs a new function concatenating the outputs of its arguments.
Assumes that `first_function` and `second_function` already have unique
names, and have declared parameters of the same type. The constructed
function will bind its parameter to each of the parameters of
`first_function` and `second_function`, and return the result of executing
these functions in parallel and concatenating the outputs in a tuple.
Args:
first_function: Instance of `tff_framework.Lambda` whose result we wish to
concatenate with the result of `second_function`.
second_function: Instance of `tff_framework.Lambda` whose result we wish to
concatenate with the result of `first_function`.
Returns:
A new instance of `tff_framework.Lambda` with unique names representing the
computation described above.
Raises:
TypeError: If the arguments are not instances of `tff_framework.Lambda`,
or declare parameters of different types.
"""
py_typecheck.check_type(first_function, tff_framework.Lambda)
py_typecheck.check_type(second_function, tff_framework.Lambda)
tff_framework.check_has_unique_names(first_function)
tff_framework.check_has_unique_names(second_function)
if first_function.parameter_type != second_function.parameter_type:
raise TypeError('Must pass two functions which declare the same parameter '
'type to `concatenate_function_outputs`; you have passed '
'one function which declared a parameter of type {}, and '
'another which declares a parameter of type {}'.format(
first_function.type_signature,
second_function.type_signature))
def _rename_first_function_arg(comp):
if isinstance(
comp,
tff_framework.Reference) and comp.name == first_function.parameter_name:
if comp.type_signature != second_function.parameter_type:
raise AssertionError('{}, {}'.format(comp.type_signature,
second_function.parameter_type))
return tff_framework.Reference(second_function.parameter_name,
comp.type_signature), True
return comp, False
first_function, _ = tff_framework.transform_postorder(
first_function, _rename_first_function_arg)
concatenated_function = tff_framework.Lambda(
second_function.parameter_name, second_function.parameter_type,
tff_framework.Tuple([first_function.result, second_function.result]))
renamed, _ = tff_framework.uniquify_reference_names(concatenated_function)
return renamed
def _extract_multiple_intrinsic_as_tuple_to_top_level_lambda(comp, uri):
"""Extracts multiple intrinsics from `comp` as a tuple for the given `uri`.
Args:
comp: The `tff_framework.Lambda` to transform. The names of lambda
parameters and block variables in `comp` must be unique.
uri: A URI of an intrinsic.
Returns:
A new computation with the transformation applied or the original `comp`.
Raises:
ValueError: If all the intrinsics for the given `uri` in `comp` are not
exclusively bound by `comp`.
"""
py_typecheck.check_type(comp, tff_framework.Lambda)
tff_framework.check_has_unique_names(comp)
py_typecheck.check_type(uri, six.string_types)
intrinsics = _get_called_intrinsics(comp, uri)
if len(intrinsics) < 2:
return comp, False
if not _are_comps_bound_exclusively_by_top_level_lambda(comp, intrinsics):
raise ValueError(
'Expected a computation which binds all the references in all the '
'intrinsic with the uri: {}.'.format(uri))
name_generator = tff_framework.unique_name_generator(comp)
extracted_intrinsics = tff_framework.Tuple(intrinsics)
ref_name = six.next(name_generator)
ref_type = tff.to_type(extracted_intrinsics.type_signature)
ref = tff_framework.Reference(ref_name, ref_type)
def _should_transform(comp):
return tff_framework.is_called_intrinsic(comp, uri)
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
comp, _ = tff_framework.transform_postorder(comp, _transform)
comp = _insert_comp_in_top_level_lambda(
comp, name=ref.name, comp_to_insert=extracted_intrinsics)
return comp, True
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 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([
next_result[0],
next_result[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
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