def testStrippedOpListNestedFunctions(self):
with self.test_session():
# Square two levels deep
@function.Defun(dtypes.int32)
def f0(x):
return math_ops.square(x)
@function.Defun(dtypes.int32)
def f1(x):
return f0(x)
# At this point we've defined two functions but haven't called them, so
# there should be no used ops.
op_list = meta_graph.stripped_op_list_for_graph(ops.get_default_graph()
.as_graph_def())
self.assertEqual(len(op_list.op), 0)
# If we call the function on a constant, there should be two ops
_ = f1(constant_op.constant(7))
op_list = meta_graph.stripped_op_list_for_graph(ops.get_default_graph()
.as_graph_def())
self.assertEqual(["Const", "Square"], [op.name for op in op_list.op])
def testStrippedOpListNestedFunctions(self):
with self.cached_session():
# Square two levels deep
@function.Defun(dtypes.int32)
def f0(x):
return math_ops.square(x)
@function.Defun(dtypes.int32)
def f1(x):
return f0(x)
# At this point we've defined two functions but haven't called them, so
# there should be no used ops.
op_list = meta_graph.stripped_op_list_for_graph(ops.get_default_graph()
.as_graph_def())
self.assertEqual(len(op_list.op), 0)
# If we call the function on a constant, there should be two ops
_ = f1(constant_op.constant(7))
op_list = meta_graph.stripped_op_list_for_graph(ops.get_default_graph()
.as_graph_def())
self.assertEqual(["Const", "Square"], [op.name for op in op_list.op])
def testStrippedOpListRecursiveFunctions(self):
# The function module doesn't support recursive functions, so we build a
# recursive function situation by ourselves: A calls B calls A and Const.
graph = graph_pb2.GraphDef()
a = graph.library.function.add()
b = graph.library.function.add()
a.signature.name = "A"
b.signature.name = "B"
a.node_def.add().op = "B"
b.node_def.add().op = "Const"
b.node_def.add().op = "A"
# Use A in the graph
graph.node.add().op = "A"
# The stripped op list should contain just Const.
op_list = meta_graph.stripped_op_list_for_graph(graph)
self.assertEqual(["Const"], [op.name for op in op_list.op])
def testStrippedOpListRecursiveFunctions(self):
# The function module doesn't support recursive functions, so we build a
# recursive function situation by ourselves: A calls B calls A and Const.
graph = graph_pb2.GraphDef()
a = graph.library.function.add()
b = graph.library.function.add()
a.signature.name = "A"
b.signature.name = "B"
a.node_def.add().op = "B"
b.node_def.add().op = "Const"
b.node_def.add().op = "A"
# Use A in the graph
graph.node.add().op = "A"
# The stripped op list should contain just Const.
op_list = meta_graph.stripped_op_list_for_graph(graph)
self.assertEqual(["Const"], [op.name for op in op_list.op])
def testStrippedOpListPartitionedCalls(self):
# Function A calls B via StatefulPartitionedCall.
graph = graph_pb2.GraphDef()
a = graph.library.function.add()
b = graph.library.function.add()
a.signature.name = "A"
b.signature.name = "B"
node_in_a = a.node_def.add()
node_in_a.op = "StatefulPartitionedCall"
node_in_a.attr["f"].func.name = "B"
b.node_def.add().op = "Const"
b.node_def.add().op = "A"
# Use A in the graph via PartitionedCall.
node = graph.node.add()
node.op = "PartitionedCall"
node.attr["f"].func.name = "A"
op_list = meta_graph.stripped_op_list_for_graph(graph)
self.assertSameElements(
["Const", "PartitionedCall", "StatefulPartitionedCall"],
[op.name for op in op_list.op])
def _fill_meta_graph_def(meta_graph_def, saveable_view, signature_functions,
namespace_whitelist):
"""Generates a MetaGraph which calls `signature_functions`.
Args:
meta_graph_def: The MetaGraphDef proto to fill.
saveable_view: The _SaveableView being exported.
signature_functions: A dictionary mapping signature keys to concrete
functions containing signatures to add to the MetaGraph.
namespace_whitelist: List of strings containing whitelisted op namespaces.
Returns:
A tuple of (_AssetInfo, Graph) containing the captured assets and
exported Graph generated from tracing the saveable_view.
"""
# List objects from the eager context to make sure Optimizers give us the
# right Graph-dependent variables.
accessible_objects = saveable_view.nodes
resource_initializer_functions = _trace_resource_initializers(
accessible_objects)
exported_graph = ops.Graph()
resource_initializer_ops = []
with exported_graph.as_default():
object_map, resource_map, asset_info = saveable_view.map_resources()
for resource_initializer_function in resource_initializer_functions:
asset_dependencies = []
for capture in resource_initializer_function.graph.external_captures:
asset_initializer = asset_info.asset_initializers_by_resource.get(
capture, None)
if asset_initializer is not None:
asset_dependencies.append(asset_initializer)
with ops.control_dependencies(asset_dependencies):
resource_initializer_ops.append(
_call_function_with_mapped_captures(
resource_initializer_function, [], resource_map))
resource_initializer_ops.extend(
asset_info.asset_initializers_by_resource.values())
with ops.control_dependencies(resource_initializer_ops):
init_op = control_flow_ops.no_op()
# Add the same op to the main_op collection and to the init_op
# signature. The collection is for compatibility with older loader APIs;
# only one will be executed.
meta_graph_def.collection_def[constants.MAIN_OP_KEY].node_list.value.append(
init_op.name)
meta_graph_def.signature_def[constants.INIT_OP_SIGNATURE_KEY].CopyFrom(
signature_def_utils.op_signature_def(
init_op, constants.INIT_OP_SIGNATURE_KEY))
# Saving an object-based checkpoint again gathers variables. We need to do the
# gathering from the eager context so Optimizers save the right set of
# variables, but want any operations associated with the save/restore to be in
# the exported graph (thus the `to_graph` argument).
saver = functional_saver.MultiDeviceSaver(
saveable_view.checkpoint_view.frozen_saveable_objects(
object_map=object_map, to_graph=exported_graph))
with exported_graph.as_default():
signatures = _generate_signatures(signature_functions, resource_map)
for concrete_function in saveable_view.concrete_functions:
concrete_function.add_to_graph()
saver_def = saver.to_proto()
meta_graph_def.saver_def.CopyFrom(saver_def)
graph_def = exported_graph.as_graph_def(add_shapes=True)
_verify_ops(graph_def, namespace_whitelist)
meta_graph_def.graph_def.CopyFrom(graph_def)
meta_graph_def.meta_info_def.tags.append(tag_constants.SERVING)
meta_graph_def.meta_info_def.tensorflow_version = versions.__version__
meta_graph_def.meta_info_def.tensorflow_git_version = (
versions.__git_version__)
# We currently always strip default attributes.
meta_graph_def.meta_info_def.stripped_default_attrs = True
meta_graph_def.meta_info_def.stripped_op_list.MergeFrom(
meta_graph.stripped_op_list_for_graph(meta_graph_def.graph_def))
meta_graph_def.asset_file_def.extend(asset_info.asset_defs)
for signature_key, signature in signatures.items():
meta_graph_def.signature_def[signature_key].CopyFrom(signature)
meta_graph.strip_graph_default_valued_attrs(meta_graph_def)
return asset_info, exported_graph
def _fill_meta_graph_def(meta_graph_def, saveable_view, signature_functions):
"""Generates a MetaGraph which calls `signature_functions`.
Args:
meta_graph_def: The MetaGraphDef proto to fill.
saveable_view: The _SaveableView being exported.
signature_functions: A dictionary mapping signature keys to concrete
functions containing signatures to add to the MetaGraph.
Returns:
An _AssetInfo, which contains information to help creating the SavedModel.
"""
# List objects from the eager context to make sure Optimizers give us the
# right Graph-dependent variables.
accessible_objects = saveable_view.nodes
resource_initializer_functions = _trace_resource_initializers(
accessible_objects)
exported_graph = ops.Graph()
resource_initializer_ops = []
with exported_graph.as_default():
object_map, resource_map, asset_info = saveable_view.map_resources()
for resource_initializer_function in resource_initializer_functions:
asset_dependencies = []
for capture in resource_initializer_function.graph.external_captures:
asset_initializer = asset_info.asset_initializers_by_resource.get(
capture, None)
if asset_initializer is not None:
asset_dependencies.append(asset_initializer)
with ops.control_dependencies(asset_dependencies):
resource_initializer_ops.append(
_call_function_with_mapped_captures(
resource_initializer_function, [], resource_map))
resource_initializer_ops.extend(
asset_info.asset_initializers_by_resource.values())
with ops.control_dependencies(resource_initializer_ops):
init_op = control_flow_ops.no_op()
# Add the same op to the main_op collection and to the init_op
# signature. The collection is for compatibility with older loader APIs;
# only one will be executed.
meta_graph_def.collection_def[constants.MAIN_OP_KEY].node_list.value.append(
init_op.name)
meta_graph_def.signature_def[constants.INIT_OP_SIGNATURE_KEY].CopyFrom(
signature_def_utils.op_signature_def(
init_op, constants.INIT_OP_SIGNATURE_KEY))
# Saving an object-based checkpoint again gathers variables. We need to do the
# gathering from the eager context so Optimizers save the right set of
# variables, but want any operations associated with the save/restore to be in
# the exported graph (thus the `to_graph` argument).
saver = functional_saver.MultiDeviceSaver(
saveable_view.checkpoint_view.frozen_saveable_objects(
object_map=object_map, to_graph=exported_graph))
with exported_graph.as_default():
signatures = _generate_signatures(signature_functions, resource_map)
for concrete_function in saveable_view.concrete_functions:
concrete_function.add_to_graph()
saver_def = saver.to_proto()
meta_graph_def.saver_def.CopyFrom(saver_def)
graph_def = exported_graph.as_graph_def(add_shapes=True)
meta_graph_def.graph_def.CopyFrom(graph_def)
meta_graph_def.meta_info_def.tags.append(tag_constants.SERVING)
meta_graph_def.meta_info_def.tensorflow_version = versions.__version__
meta_graph_def.meta_info_def.tensorflow_git_version = (
versions.__git_version__)
# We currently always strip default attributes.
meta_graph_def.meta_info_def.stripped_default_attrs = True
meta_graph_def.meta_info_def.stripped_op_list.MergeFrom(
meta_graph.stripped_op_list_for_graph(meta_graph_def.graph_def))
meta_graph_def.asset_file_def.extend(asset_info.asset_defs)
for signature_key, signature in signatures.items():
meta_graph_def.signature_def[signature_key].CopyFrom(signature)
meta_graph.strip_graph_default_valued_attrs(meta_graph_def)
return asset_info, exported_graph
