def _from_definition(fdef, grad_func=None):
"""Creates a _DefinedFunction initialized from a FunctionDef proto.
Args:
fdef: a FunctionDef
grad_func: a _DefinedFunction or None
Returns:
A _DefinedFunction representing fdef
"""
# TODO(iga): This method does major surgery on _DefinedFunction.
# Make it a named constructor using @classmethod of _DefinedFunction.
# The Python callable is only needed to create a FunctionDef. Since we have
# the FunctionDef here, we don't need to set _DefinedFunction._func (nor do we
# have access to such a callable here).
func = None
argnames = [arg.name for arg in fdef.signature.input_arg]
input_types = tuple(
dtypes.as_dtype(arg.type) for arg in fdef.signature.input_arg)
func_name = fdef.signature.name
# Note: FunctionDefs do not include python gradient functions, so if the
# original _DefinedFunction included one it will not be reflected here.
python_grad_func = None
out_names = [arg.name for arg in fdef.signature.output_arg]
result = _DefinedFunction(func, argnames, input_types, func_name,
grad_func, python_grad_func, out_names)
# pylint: disable=protected-access
serialized = fdef.SerializeToString()
c_func = c_api.TF_FunctionImportFunctionDef(serialized)
result._c_func = c_api_util.ScopedTFFunction(c_func)
result._extra_inputs = []
# pylint: enable=protected-access
return result
def _create_new_tf_function(func_graph):
"""Converts func_graph to a TF_Function and adds it to the current graph.
Args:
func_graph: function._FuncGraph
Returns:
The name of the new TF_Function.
"""
c_func = c_api.TF_GraphToFunction_wrapper(
func_graph._c_graph,
compat.as_str(func_graph.name),
False, # append_hash_to_fn_name
None, # opers
[t._as_tf_output() for t in func_graph.inputs],
[t._as_tf_output() for t in func_graph.outputs],
[],
None, # opts
None) # description
_ = c_api_util.ScopedTFFunction(c_func)
# TODO(b/109833212): this sucks, we're serializing the TF_Function*,
# deserializing it into a Python FunctionDef, then reserializing it to create
# a new TF_Function that we add to the graph.
fdef = _function.function_def_from_tf_function(c_func)
defined_func = _function._from_definition(fdef)
defined_func._sub_functions = func_graph._functions
defined_func.add_to_graph(func_graph._outer_graph)
return func_graph.name
def __init__(self, name, graph, operations, inputs, outputs, attrs):
"""Initializes an eager defined function.
Args:
name: str, the name for the created function.
graph: Graph, the graph containing the operations in the function
operations: list of Operation; the subset of operations in the graph
which will be in the function
inputs: the tensors in the graph to be used as inputs to the function
outputs: the tensors in the graph which will be outputs to the function
attrs: dict mapping names of attributes to their AttrValue values
"""
fn = pywrap_tensorflow.TF_GraphToFunction_wrapper(
graph._c_graph, # pylint: disable=protected-access
compat.as_str(name),
False,
[o._c_op for o in operations], # pylint: disable=protected-access
[t._as_tf_output() for t in inputs], # pylint: disable=protected-access
[t._as_tf_output() for t in outputs], # pylint: disable=protected-access
[],
None,
compat.as_str(""))
for name, attr_value in attrs.items():
serialized = attr_value.SerializeToString()
# TODO(iga): this creates and deletes a new TF_Status for every attr.
# It might be worth creating a convenient way to re-use status.
pywrap_tensorflow.TF_FunctionSetAttrValueProto(
fn, compat.as_str(name), serialized)
# TODO(apassos) avoid creating a FunctionDef (specially to grab the
# signature, but also in general it's nice not to depend on it.
with c_api_util.tf_buffer() as buffer_:
pywrap_tensorflow.TF_FunctionToFunctionDef(fn, buffer_)
proto_data = pywrap_tensorflow.TF_GetBuffer(buffer_)
function_def = function_pb2.FunctionDef()
function_def.ParseFromString(compat.as_bytes(proto_data))
if context.executing_eagerly():
_register(fn)
self.definition = function_def
self.name = function_def.signature.name
self.signature = function_def.signature
self.grad_func_name = None
self.python_grad_func = None
self._c_func = c_api_util.ScopedTFFunction(fn)
self._grad_func = None
def __init__(self, name, graph, operations, inputs, outputs):
"""Initializes an eager defined function.
Args:
name: str, the name for the created function.
graph: Graph, the graph containing the operations in the function
operations: list of Operation; the subset of operations in the graph
which will be in the function
inputs: the tensors in the graph to be used as inputs to the function
outputs: the tensors in the graph which will be outputs to the function
"""
fn = pywrap_tensorflow.TF_GraphToFunction_wrapper(
graph._c_graph, # pylint: disable=protected-access
compat.as_str(name),
False,
[o._c_op for o in operations], # pylint: disable=protected-access
[t._as_tf_output() for t in inputs], # pylint: disable=protected-access
[t._as_tf_output() for t in outputs], # pylint: disable=protected-access
[],
None,
compat.as_str(""))
# TODO(apassos) avoid creating a FunctionDef (specially to grab the
# signature, but also in general it's nice not to depend on it.
with c_api_util.tf_buffer() as buffer_:
pywrap_tensorflow.TF_FunctionToFunctionDef(fn, buffer_)
proto_data = pywrap_tensorflow.TF_GetBuffer(buffer_)
function_def = function_pb2.FunctionDef()
function_def.ParseFromString(compat.as_bytes(proto_data))
if context.executing_eagerly():
_register(fn)
self.definition = function_def
self.name = function_def.signature.name
self.signature = function_def.signature
self.grad_func_name = None
self.python_grad_func = None
self._c_func = c_api_util.ScopedTFFunction(fn)
self._grad_func = None
def _create_new_tf_function(func_graph):
"""Converts func_graph to a TF_Function and adds it to the current graph.
Args:
func_graph: function._FuncGraph
Returns:
The name of the new TF_Function.
"""
func_graph.name = "%s_" % func_graph.name
c_func = c_api.TF_GraphToFunction_wrapper(
func_graph._c_graph,
func_graph.name,
False, # append_hash_to_fn_name
None, # opers
[t._as_tf_output() for t in func_graph.inputs],
[t._as_tf_output() for t in func_graph.outputs],
[],
None, # opts
None) # description
c_func = c_api_util.ScopedTFFunction(c_func)
c_api.TF_GraphCopyFunction(ops.get_default_graph()._c_graph, c_func.func,
None)
return func_graph.name
def _create_definition_if_needed_impl(self):
"""This is not what you want, see _create_definition_if_needed."""
if self._definition is not None or self._c_func is not None:
return
# Copy variable collections (by reference) from the parent graph such that
# name based variable sharing (e.g. via tf.make_template) works between the
# func graph and parent graph.
variable_keys = []
variable_keys.extend(ops.GraphKeys._VARIABLE_COLLECTIONS) # pylint: disable=protected-access
variable_keys.append(vs._VARSTORE_KEY) # pylint: disable=protected-access
parent_graph = ops.get_default_graph()
collections_ref = {
key: parent_graph.get_collection_ref(key) for key in variable_keys}
temp_graph = func_graph_from_py_func(
self._func,
self._arg_names,
self._arg_types,
self._func_name,
self._capture_by_value,
self._caller_device,
collections_ref=collections_ref,
allowlisted_stateful_ops=self._allowlisted_stateful_ops,
capture_resource_var_by_value=self._capture_resource_var_by_value)
self._extra_inputs = temp_graph.extra_inputs
# pylint: disable=protected-access
self._sub_functions = temp_graph._functions
# pylint: enable=protected-access
# Extra kwargs are treated as attrs on the function def.
if self._func_name:
base_func_name = self._func_name
else:
base_func_name = function_utils.get_func_name(self._func)
if self._grad_func:
base_func_name += ("_%s" % self._grad_func.name)
kwargs_attr = _parse_kwargs_as_attrs(base_func_name, **self._extra_kwargs)
if not temp_graph._c_graph: # pylint: disable=protected-access
# Build the FunctionDef
self._definition = graph_to_function_def.graph_to_function_def(
temp_graph,
temp_graph.get_operations(),
temp_graph.inputs,
temp_graph.outputs,
out_names=self._out_names)
for k in kwargs_attr:
self._definition.attr[k].CopyFrom(kwargs_attr[k])
# Hash the definition and its dependencies.
self._hash_str = self._create_hash_str(
self._definition.signature.input_arg,
self._definition.signature.output_arg, self._definition.node_def)
# Finally, we decide the function name to use. If not specified,
# make up something which is almost certainly unique (but deterministic).
if not self._func_name:
self._func_name = "_".join([base_func_name, self._hash_str])
self._definition.signature.name = self._func_name
if self._func.__doc__:
self._definition.signature.description = self._func.__doc__
self._op_def = self._definition.signature
else: # C API is enabled
output_names = ([compat.as_bytes(x) for x in self._out_names]
if self._out_names else [])
description = self._func.__doc__ or None
# pylint: disable=protected-access
c_func = c_api.TF_GraphToFunction_wrapper(
temp_graph._c_graph,
base_func_name,
self._func_name is None, # append_hash_to_fn_name
None, # opers
[t._as_tf_output() for t in temp_graph.inputs],
[t._as_tf_output() for t in temp_graph.outputs],
output_names,
[], # control_outputs
[], # control_output_names
None, # opts
description)
self._c_func = c_api_util.ScopedTFFunction(c_func)
# pylint: enable=protected-access
self._set_c_attrs(kwargs_attr)
# Set cached fields: _op_def and _func_name (if not already set)
self._op_def = self.definition.signature
if self._func_name:
assert self._func_name == self._op_def.name
else:
self._func_name = compat.as_str(self._op_def.name)
self._stateful_ops = [(op.name, op.type)
for op in temp_graph.get_operations()
if op._is_stateful] # pylint: disable=protected-access
def _create_definition_if_needed_impl(self):
"""This is not what you want, see _create_definition_if_needed."""
if self._definition is not None or self._c_func is not None:
return
temp_graph = func_graph_from_py_func(
self._func,
self._arg_names,
self._arg_types,
self._func_name,
self._capture_by_value,
self._caller_device,
whitelisted_stateful_ops=self._whitelisted_stateful_ops)
self._extra_inputs = temp_graph.extra_inputs
# pylint: disable=protected-access
self._sub_functions = temp_graph._functions
# pylint: enable=protected-access
# Extra kwargs are treated as attrs on the function def.
if self._func_name:
base_func_name = self._func_name
else:
base_func_name = function_utils.get_func_name(self._func)
if self._grad_func:
base_func_name += ("_%s" % self._grad_func.name)
kwargs_attr = _parse_kwargs_as_attrs(base_func_name,
**self._extra_kwargs)
if not temp_graph._c_graph: # pylint: disable=protected-access
# Build the FunctionDef
self._definition = graph_to_function_def.graph_to_function_def(
temp_graph,
temp_graph.get_operations(),
temp_graph.inputs,
temp_graph.outputs,
out_names=self._out_names)
for k in kwargs_attr:
self._definition.attr[k].CopyFrom(kwargs_attr[k])
# Hash the definition and its dependencies.
self._hash_str = self._create_hash_str(
self._definition.signature.input_arg,
self._definition.signature.output_arg,
self._definition.node_def)
# Finally, we decide the function name to use. If not specified,
# make up something which is almost certainly unique (but deterministic).
if not self._func_name:
self._func_name = "_".join([base_func_name, self._hash_str])
self._definition.signature.name = self._func_name
if self._func.__doc__:
self._definition.signature.description = self._func.__doc__
self._op_def = self._definition.signature
else: # C API is enabled
output_names = ([compat.as_bytes(x) for x in self._out_names]
if self._out_names else [])
description = self._func.__doc__ or None
# pylint: disable=protected-access
c_func = c_api.TF_GraphToFunction_wrapper(
temp_graph._c_graph,
base_func_name,
self._func_name is None, # append_hash_to_fn_name
None, # opers
[t._as_tf_output() for t in temp_graph.inputs],
[t._as_tf_output() for t in temp_graph.outputs],
output_names,
None, # opts
description)
self._c_func = c_api_util.ScopedTFFunction(c_func)
# pylint: enable=protected-access
self._set_c_attrs(kwargs_attr)
# Set cached fields: _op_def and _func_name (if not already set)
self._op_def = self.definition.signature
if self._func_name:
assert self._func_name == self._op_def.name
else:
self._func_name = compat.as_str(self._op_def.name)
self._stateful_ops = [(op.name, op.type)
for op in temp_graph.get_operations()
if op.op_def.is_stateful]
def _create_definition_if_needed_impl(self):
"""This is not what you want, see _create_definition_if_needed."""
if self._definition is not None or self._c_func is not None:
return
# Create the func_def object.
temp_graph = _FuncGraph(capture_by_value=self._capture_by_value)
with temp_graph.as_default():
# List of placeholders for the function_def.
inputs = []
for (argname, argtype) in self._args:
argholder = array_ops.placeholder(argtype, name=argname)
inputs.append(argholder)
# Call func and gather the output tensors.
with vs.variable_scope("", custom_getter=temp_graph.getvar):
outputs = self._func(*inputs)
# There is no way of distinguishing between a function not returning
# anything and a function returning None in Python.
# We need to allow the former and ideally want to forbid the latter as
# it is most likely user error.
# TODO(iga): Consider adding a @NoOutput decorator on top of @Defun to
# allow users to explicitly mark the function as not returning anything.
# For now, we allow a single None return and interpret it as a function
# with no output.
if outputs is None:
outputs = []
else:
# If func only returned one value, make it a tuple.
if not isinstance(outputs, (list, tuple)):
outputs = (outputs, )
if any([_ is None for _ in outputs]):
raise ValueError("Function can not return None.")
# Ensures each output is a Tensor in the function graph.
outputs = [ops.convert_to_tensor(t) for t in outputs]
outputs = [
temp_graph.capture(t) if t.graph is not temp_graph else t
for t in outputs
]
self._extra_inputs = temp_graph.extra_inputs
inputs.extend(temp_graph.extra_args)
# pylint: disable=protected-access
self._sub_functions = temp_graph._functions
# pylint: enable=protected-access
# Extra kwargs are treated as attrs on the function def.
if self._func_name:
base_func_name = self._func_name
else:
base_func_name = _get_func_name(self._func)
if self._grad_func:
base_func_name += ("_%s" % self._grad_func.name)
kwargs_attr = _parse_kwargs_as_attrs(base_func_name,
**self._extra_kwargs)
if not temp_graph._c_graph: # pylint: disable=protected-access
# Build the FunctionDef
self._definition = graph_to_function_def.graph_to_function_def(
temp_graph,
temp_graph.get_operations(),
inputs,
outputs,
out_names=self._out_names)
for k in kwargs_attr:
self._definition.attr[k].CopyFrom(kwargs_attr[k])
# Hash the definition and its dependencies.
self._hash_str = self._create_hash_str(
self._definition.signature.input_arg,
self._definition.signature.output_arg,
self._definition.node_def)
# Finally, we decide the function name to use. If not specified,
# make up something which is almost certainly unique (but deterministic).
if not self._func_name:
self._func_name = "_".join([base_func_name, self._hash_str])
self._definition.signature.name = self._func_name
if self._func.__doc__:
self._definition.signature.description = self._func.__doc__
self._op_def = self._definition.signature
else: # C API is enabled
output_names = ([compat.as_bytes(x) for x in self._out_names]
if self._out_names else [])
description = self._func.__doc__ or None
# pylint: disable=protected-access
c_func = c_api.TF_GraphToFunction_wrapper(
temp_graph._c_graph,
base_func_name,
self._func_name is None, # append_hash_to_fn_name
None, # opers
[t._as_tf_output() for t in inputs],
[t._as_tf_output() for t in outputs],
output_names,
None, # opts
description)
self._c_func = c_api_util.ScopedTFFunction(c_func)
# pylint: enable=protected-access
self._set_c_attrs(kwargs_attr)
# Set cached fields: _op_def and _func_name (if not already set)
self._op_def = self.definition.signature
if self._func_name:
assert self._func_name == self._op_def.name
else:
self._func_name = compat.as_str(self._op_def.name)
