def serialize_concrete_function(concrete_function, node_ids):
"""Build a SavedConcreteFunction."""
bound_inputs = []
try:
for capture in concrete_function.captured_inputs:
bound_inputs.append(node_ids[capture])
except KeyError:
raise KeyError(
f"Failed to add concrete function '{concrete_function.name}' to object-"
f"based SavedModel as it captures tensor {capture!r} which is unsupported"
" or not reachable from root. "
"One reason could be that a stateful object or a variable that the "
"function depends on is not assigned to an attribute of the serialized "
"trackable object (see SaveTest.test_captures_unreachable_variable)."
)
concrete_function_proto = saved_object_graph_pb2.SavedConcreteFunction()
structured_outputs = func_graph_module.convert_structure_to_signature(
concrete_function.structured_outputs)
concrete_function_proto.canonicalized_input_signature.CopyFrom(
nested_structure_coder.encode_structure(
concrete_function.structured_input_signature))
concrete_function_proto.output_signature.CopyFrom(
nested_structure_coder.encode_structure(structured_outputs))
concrete_function_proto.bound_inputs.extend(bound_inputs)
return concrete_function_proto
def serialize_function(function, node_ids):
"""Build a SavedFunction proto."""
coder = nested_structure_coder.StructureCoder()
proto = saved_object_graph_pb2.SavedFunction()
function_spec_proto = _serialize_function_spec(function.function_spec,
coder)
proto.function_spec.CopyFrom(function_spec_proto)
all_concrete_functions = \
function._list_all_concrete_functions_for_serialization() # pylint: disable=protected-access
for concrete_function in all_concrete_functions:
bound_inputs = []
try:
for capture in concrete_function.captured_inputs:
bound_inputs.append(node_ids[capture])
except KeyError:
# TODO(andresp): Would it better to throw an exception?
logging.warning(
"Concrete function %s not added to object based saved model as it "
"captures tensor %s which is unsupported or not reachable from root.",
concrete_function.name, capture)
continue
signature_args, signature_kwargs = \
concrete_function.structured_input_signature
del signature_kwargs
concrete_function_proto = proto.concrete_function.add()
concrete_function_proto.name = concrete_function.name
concrete_function_proto.canonicalized_input_signature.CopyFrom(
coder.encode_structure(signature_args))
structured_outputs = func_graph_module.convert_structure_to_signature(
concrete_function.structured_outputs)
concrete_function_proto.output_signature.CopyFrom(
coder.encode_structure(structured_outputs))
concrete_function_proto.bound_inputs.extend(bound_inputs)
return proto
def _prune_receiver_tensors(wrapped_function, receiver_tensors, outputs, name):
inputs = _canonicalize_receiver_tensors(receiver_tensors)
return wrapped_function.prune(
inputs,
outputs,
name=name,
input_signature=(None,
func_graph.convert_structure_to_signature(inputs)))
def __init__(self, closure, type_spec): self._closure = closure # The type spec for this `RemoteValue` which is used to trace functions that # take this `RemoteValue` as input. self._type_spec = func_graph.convert_structure_to_signature(type_spec) self._value = None self._error = None self._status_available_event = threading.Event() self._status = _RemoteValueStatus.NOT_READY
def serialize_concrete_function(concrete_function, node_ids, coder):
"""Build a SavedConcreteFunction."""
bound_inputs = []
try:
for capture in concrete_function.captured_inputs:
bound_inputs.append(node_ids[capture])
except KeyError:
raise KeyError(
"Concrete function %s not added to object based saved model as it "
"captures tensor %s which is unsupported or not reachable from root."
% (concrete_function.name, capture))
concrete_function_proto = saved_object_graph_pb2.SavedConcreteFunction()
structured_outputs = func_graph_module.convert_structure_to_signature(
concrete_function.structured_outputs)
concrete_function_proto.canonicalized_input_signature.CopyFrom(
coder.encode_structure(concrete_function.structured_input_signature))
concrete_function_proto.output_signature.CopyFrom(
coder.encode_structure(structured_outputs))
concrete_function_proto.bound_inputs.extend(bound_inputs)
return concrete_function_proto
def serialize_concrete_function(concrete_function, node_ids):
"""Build a SavedConcreteFunction."""
bound_inputs = []
try:
for capture in concrete_function.captured_inputs:
bound_inputs.append(node_ids[capture])
except KeyError:
# TODO(andresp): Would it better to throw an exception?
logging.warning(
"Concrete function %s not added to object based saved model as it "
"captures tensor %s which is unsupported or not reachable from root.",
concrete_function.name, capture)
return None
coder = nested_structure_coder.StructureCoder()
concrete_function_proto = saved_object_graph_pb2.SavedConcreteFunction()
concrete_function_proto.name = concrete_function.name
concrete_function_proto.canonicalized_input_signature.CopyFrom(
coder.encode_structure(concrete_function.structured_input_signature))
structured_outputs = func_graph_module.convert_structure_to_signature(
concrete_function.structured_outputs)
concrete_function_proto.output_signature.CopyFrom(
coder.encode_structure(structured_outputs))
concrete_function_proto.bound_inputs.extend(bound_inputs)
return concrete_function_proto
def serialize_concrete_function(concrete_function, node_ids, coder):
"""Build a SavedConcreteFunction."""
bound_inputs = []
try:
for capture in concrete_function.captured_inputs:
bound_inputs.append(node_ids[capture])
except KeyError:
# TODO(allenl): This warning shadows a real issue in test_table in
# save_test.py, where we don't handle captured constants. Fix that and
# then make this an exception.
logging.warning(
"Concrete function %s not added to object based saved model as it "
"captures tensor %s which is unsupported or not reachable from root.",
concrete_function.name, capture)
return None
concrete_function_proto = saved_object_graph_pb2.SavedConcreteFunction()
structured_outputs = func_graph_module.convert_structure_to_signature(
concrete_function.structured_outputs)
concrete_function_proto.canonicalized_input_signature.CopyFrom(
coder.encode_structure(concrete_function.structured_input_signature))
concrete_function_proto.output_signature.CopyFrom(
coder.encode_structure(structured_outputs))
concrete_function_proto.bound_inputs.extend(bound_inputs)
return concrete_function_proto
def serialize_concrete_function(concrete_function, node_ids):
"""Build a SavedConcreteFunction."""
bound_inputs = []
try:
for capture in concrete_function.captured_inputs:
bound_inputs.append(node_ids[capture])
except KeyError:
# TODO(andresp): Would it better to throw an exception?
logging.warning(
"Concrete function %s not added to object based saved model as it "
"captures tensor %s which is unsupported or not reachable from root.",
concrete_function.name, capture)
return None
coder = nested_structure_coder.StructureCoder()
concrete_function_proto = saved_object_graph_pb2.SavedConcreteFunction()
concrete_function_proto.name = concrete_function.name
concrete_function_proto.canonicalized_input_signature.CopyFrom(
coder.encode_structure(concrete_function.structured_input_signature))
structured_outputs = func_graph_module.convert_structure_to_signature(
concrete_function.structured_outputs)
concrete_function_proto.output_signature.CopyFrom(
coder.encode_structure(structured_outputs))
concrete_function_proto.bound_inputs.extend(bound_inputs)
return concrete_function_proto
def serialize_concrete_function(concrete_function, node_ids, coder):
"""Build a SavedConcreteFunction."""
bound_inputs = []
try:
for capture in concrete_function.captured_inputs:
bound_inputs.append(node_ids[capture])
except KeyError:
raise KeyError(
"Failed to add concrete function %s to object based saved model as it "
"captures tensor %s which is unsupported or not reachable from root. "
"One reason could be that a stateful object or a variable that the "
"function depends on is not assigned to an attribute of the serialized "
"checkpointable object "
"(see SaveTest.test_captures_unreachable_variable)."
% (concrete_function.name, capture))
concrete_function_proto = saved_object_graph_pb2.SavedConcreteFunction()
structured_outputs = func_graph_module.convert_structure_to_signature(
concrete_function.structured_outputs)
concrete_function_proto.canonicalized_input_signature.CopyFrom(
coder.encode_structure(concrete_function.structured_input_signature))
concrete_function_proto.output_signature.CopyFrom(
coder.encode_structure(structured_outputs))
concrete_function_proto.bound_inputs.extend(bound_inputs)
return concrete_function_proto
def _set_type_spec(self, type_spec): self._type_spec = func_graph.convert_structure_to_signature(type_spec)
def _extract_signatures(self, wrapped, meta_graph_def):
"""Creates ConcreteFunctions for signatures in `meta_graph_def`."""
signature_functions = {}
for signature_key, signature_def in meta_graph_def.signature_def.items(
):
if signature_def.inputs:
input_items = sorted(signature_def.inputs.items(),
key=lambda item: item[1].name)
original_input_names, input_specs = zip(*input_items)
else:
original_input_names = []
input_specs = []
# TODO(allenl): Support optional arguments
feeds = [
wrap_function._get_element_from_tensor_info(
input_spec, wrapped.graph) # pylint: disable=protected-access
for input_spec in input_specs
]
input_names = []
input_tensors = []
for original_input_name, feed in zip(original_input_names, feeds):
if isinstance(feed, sparse_tensor.SparseTensor):
# We have to give explicit name for SparseTensor arguments, because
# these are not present in the TensorInfo.
indices_name = "%s_indices" % original_input_name
values_name = "%s_values" % original_input_name
dense_shape_name = "%s_dense_shape" % original_input_name
input_names.extend(
[indices_name, values_name, dense_shape_name])
input_tensors.extend(
[feed.indices, feed.values, feed.dense_shape])
elif isinstance(feed, composite_tensor.CompositeTensor):
component_tensors = nest.flatten(feed,
expand_composites=True)
input_names.extend("%s_component_%d" %
(original_input_name, n)
for n in range(len(component_tensors)))
input_tensors.extend(component_tensors)
else:
input_names.append(original_input_name)
input_tensors.append(feed)
fetches = {
name: out
for name, out in signature_def.outputs.items()
}
try:
signature_fn = wrapped.prune(feeds=feeds, fetches=fetches)
except lift_to_graph.UnliftableError as ex:
# Mutate the exception to add a bit more detail.
args = ex.args
if not args:
message = ""
else:
message = args[0]
message = ((
"A SavedModel signature needs an input for each placeholder the "
"signature's outputs use. An output for signature '{}' depends on "
"a placeholder which is not an input (i.e. the placeholder is not "
"fed a value).\n\n").format(signature_key) + message)
ex.args = (message, ) + args[1:]
raise
# pylint: disable=protected-access
signature_fn._arg_keywords = input_names
signature_fn._func_graph.structured_input_signature = (
(),
func_graph.convert_structure_to_signature(
dict(zip(input_names, input_tensors))))
if len(input_names) == 1:
# Allowing positional arguments does not create any ambiguity if there's
# only one.
signature_fn._num_positional_args = 1
else:
signature_fn._num_positional_args = 0
# pylint: enable=protected-access
signature_functions[signature_key] = signature_fn
return signature_functions
