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# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
"""Utlity to convert FunctionDef to GraphDef and Graph."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
from tensorflow.core.framework import graph_pb2
from tensorflow.core.framework import types_pb2
from tensorflow.core.framework import versions_pb2
from tensorflow.python.framework import function
from tensorflow.python.framework import importer
from tensorflow.python.framework import ops
from tensorflow.python.framework import versions
from tensorflow.python.ops import cond_v2_impl
# This is to avoid a circular dependency with cond_v2_impl.
cond_v2_impl._function_def_to_graph = sys.modules[__name__] # pylint: disable=protected-access
def function_def_to_graph(fdef, input_shapes=None):
"""Converts a FunctionDef to a function._FuncGraph (sub-class Graph).
The returned _FuncGraph's `name`, `inputs` and `outputs` fields will be set.
The input tensors are represented as placeholders.
Note: `_FuncGraph.inputs` and `_FuncGraph._captured` are not set and may be
set by the caller.
Args:
fdef: FunctionDef.
input_shapes: Optional. A list of TensorShape objects of the shapes of
function inputs. If specified, its length must match length of
`fdef.signature.input_arg`. If a shape is None, the corresponding input
placeholder will have unknown shape.
Returns:
A _FuncGraph.
"""
func_graph = function._FuncGraph(fdef.signature.name, capture_by_value=False) # pylint: disable=protected-access
graph_def, nested_to_flat_tensor_name = function_def_to_graph_def(
fdef, input_shapes)
with func_graph.as_default():
# Add all function nodes to the graph.
importer.import_graph_def(graph_def, name="")
# Initialize fields specific to _FuncGraph.
# inputs
input_tensor_names = [
nested_to_flat_tensor_name[arg.name] for arg in fdef.signature.input_arg
]
func_graph.inputs = [
func_graph.get_tensor_by_name(name) for name in input_tensor_names
]
# outputs
output_tensor_names = [
nested_to_flat_tensor_name[fdef.ret[arg.name]]
for arg in fdef.signature.output_arg
]
func_graph.outputs = [
func_graph.get_tensor_by_name(name) for name in output_tensor_names
]
return func_graph
def function_def_to_graph_def(fdef, input_shapes=None):
"""Convert a FunctionDef to a GraphDef.
Steps:
1. Creates placeholder nodes corresponding to inputs in
`FunctionDef.signature.input_arg`.
2. Adds NodeDefs in `FunctionDef.node_def` to `GraphDef.node`.
3. Renames inputs of all nodes to use the convention of GraphDef instead of
FunctionDef. See comment on `FunctionDef.node_def` on how the tensor naming
in FunctionDefs is different from GraphDefs.
Args:
fdef: FunctionDef.
input_shapes: Optional. A list of TensorShape objects of the shapes of
function inputs. If specified, its length must match length of
`fdef.signature.input_arg`. If a shape is None, the corresponding input
placeholder will have unknown shape.
Returns:
A tuple of (GraphDef, dict<string, string>). The dict contains a mapping
from nested tensor names (in FunctionDef) to flattened names (in GraphDef).
Raises:
ValueError: If the length of input_shapes does not match the number of
input_args or if the FunctionDef is invalid.
"""
graph_def = graph_pb2.GraphDef()
graph_def.versions.CopyFrom(
versions_pb2.VersionDef(
producer=versions.GRAPH_DEF_VERSION,
min_consumer=versions.GRAPH_DEF_VERSION_MIN_CONSUMER))
# Copy *all* functions from outer graph to `graph_def` so that both direct
# and indirect references are safely handled.
ops.get_default_graph()._copy_functions_to_graph_def(graph_def, 0) # pylint: disable=protected-access
if input_shapes and len(input_shapes) != len(fdef.signature.input_arg):
raise ValueError("Length of input_shapes must match the number of " +
"input_args. len(input_shapes): {} len(input_arg): {}".
format(len(input_shapes), len(fdef.signature.input_arg)))
# 1. Create placeholders for input nodes.
for i, arg_def in enumerate(fdef.signature.input_arg):
node_def = graph_def.node.add()
node_def.name = arg_def.name
node_def.op = "Placeholder"
node_def.attr["dtype"].type = arg_def.type
if input_shapes and input_shapes[i] is not None:
node_def.attr["shape"].shape.CopyFrom(input_shapes[i].as_proto())
# 2. Copy all body NodeDefs to the GraphDef.
graph_def.node.extend(fdef.node_def)
# 3. Perform the renaming.
# Build the tensor name mapping then flatten the tensor names.
# See comment on `FunctionDef.node_def` on how the tensor naming in
# FunctionDefs is different from GraphDefs.
nested_to_flat_tensor_name = {}
for arg_def in fdef.signature.input_arg:
nested_to_flat_tensor_name[arg_def.name] = "{}:0".format(arg_def.name)
for node_def in fdef.node_def:
op_def = ops.get_default_graph()._get_op_def(node_def.op) # pylint: disable=protected-access
for attr in op_def.attr:
if attr.type == "func":
fname = node_def.attr[attr.name].func.name
if not ops.get_default_graph()._is_function(fname): # pylint: disable=protected-access
raise ValueError("%s function not found." % fname)
elif attr.type == "list(func)":
for fn in node_def.attr[attr.name].list.func:
fname = fn.name
if not ops.get_default_graph()._is_function(fname): # pylint: disable=protected-access
raise ValueError("%s function not found." % fname)
# Iterate over output_args in op_def to build the map.
# Index of the output tensor in the flattened list of *all* output
# tensors of the op.
flattened_index = 0
for arg_def in op_def.output_arg:
num_args = _get_num_args(arg_def, node_def)
for i in range(num_args):
# Map tensor names from "node_name:output_arg_name:index" to
# "node_name:flattened_index".
nested_name = "{}:{}:{}".format(node_def.name, arg_def.name, i)
flat_name = "{}:{}".format(node_def.name, flattened_index)
nested_to_flat_tensor_name[nested_name] = flat_name
flattened_index += 1
# Update inputs of all nodes in graph.
for node_def in graph_def.node:
for i in range(len(node_def.input)):
node_def.input[i] = nested_to_flat_tensor_name[node_def.input[i]]
return graph_def, nested_to_flat_tensor_name
# Based on implementation in core/framework/node_def_util.cc::ComputeArgRange.
def _get_num_args(arg_def, node_def):
if arg_def.number_attr:
return node_def.attr[arg_def.number_attr].i
elif arg_def.type_list_attr:
return len(node_def.attr[arg_def.type_list_attr].list.type)
elif arg_def.type_attr or arg_def.type != types_pb2.DT_INVALID:
return 1
else:
raise ValueError("Invalid arg_def:\n\n{}".format(str(arg_def)))
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