# 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.

# =============================================================================

"""Python front-end supports for functions.

NOTE: At this time, functions are experimental and subject to change!. Proceed

with caution.

"""

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

import collections

import hashlib

from tensorflow.core.framework import attr_value_pb2

from tensorflow.core.framework import function_pb2

from tensorflow.python import pywrap_tensorflow as c_api

from tensorflow.python.eager import context

from tensorflow.python.framework import c_api_util

from tensorflow.python.framework import dtypes

from tensorflow.python.framework import graph_to_function_def

from tensorflow.python.framework import ops

from tensorflow.python.ops import array_ops

from tensorflow.python.ops import resource_variable_ops

from tensorflow.python.ops import variable_scope as vs

from tensorflow.python.util import compat

from tensorflow.python.util import function_utils

from tensorflow.python.util import tf_contextlib

from tensorflow.python.util import tf_inspect

class Defun(object):

"""Decorator used to define TensorFlow functions.

Use this decorator to make a Python function usable directly as a TensorFlow

function.

The decorated function must add ops to the default graph and return zero or

more `Tensor` objects. Call the decorator with named arguments, one for each

argument of the function to decorate, with the expected type of the argument

as value.

For example if the function to decorate accepts two `tf.float32` arguments

named `x` and `y`, call the decorator with:

@Defun(tf.float32, tf.float32)

def foo(x, y):

...

When you call the decorated function, it adds the `call` ops to the

default graph. In addition, it adds the definition of the function into the

default graph. Because the addition of the function into the graph

is deferred, the decorator can be used anywhere in the program.

Any variables created inside of the function are hoisted into the outer graph.

Note that the variables are created in the variable scope that was active

during the first call to the function. Subsequent function calls will refer to

the same set of variables.

Definitions of functions in a graph are frozen as soon as the graph is used to

create a session. However, new functions and new calls to existing functions

may be added to the graph, with the new functions themselves becoming

immediately frozen.

Example, but also see the [How To on functions](link_needed).

```python

# Defining the function.

@tf.Defun(tf.float32, tf.float32)

def MyFunc(x, y):

return x + y, x - y

# Building the graph.

a = tf.constant([1.0])

b = tf.constant([2.0])

c, d = MyFunc(a, b, name='mycall')

```

"""

def __init__(self, *input_types, **kwargs):

"""Create a `Defun` decorator.

Args:

*input_types: A list of `tf.DType`

**kwargs: Optional keyword arguments, including

func_name - (optional). A python string, the name to use to

declare this `Function` in the graph.

grad_func - (optional). A function implementing the gradient

of the function-to-register. This is must be a

`_DefinedFunction` object. The gradient

function must satisfy the criterion defined in

function.proto:GradientDef.

python_grad_func - (optional). A function implementing the

gradient of the function python-side. This function must

take the current op and the gradients w.r.t. its outputs,

and return the gradients w.r.t. the inputs. That is it must

implement the interface expected by `tf.RegisterGradient`).

This will be called by tf.gradients to add the gradient ops

to the graph. At most one of grad_func and python_grad_func

can be specified.

out_names = (optional). A list of strings, one per output

tensor.

shape_func - (optional). A function taking the op and returning a list

of static shapes to set for the function's outputs.

"""

self._input_types = input_types

self._func_name = kwargs.pop("func_name", None)

self._grad_func = kwargs.pop("grad_func", None)

self._python_grad_func = kwargs.pop("python_grad_func", None)

self._out_names = kwargs.pop("out_names", None)

self._extra_kwargs = kwargs

def __call__(self, func):

# Various sanity checks on the callable func.

if not callable(func):

raise ValueError("function %s must be callable" % func)

# Func should not use kwargs and defaults.

argspec = tf_inspect.getargspec(func)

if argspec.keywords or argspec.defaults:

raise ValueError(

"function with argument defaults or keywords arguments are not"

" supported. {} has defaults {} and keywords {}.".format(

func, argspec.defaults, argspec.keywords))

# Computes how many arguments 'func' has.

min_args = len(argspec.args)

max_args = min_args

if argspec.varargs:

max_args = 1000000

argnames = argspec.args

if tf_inspect.ismethod(func):

# 1st argument is the "class" type.

min_args -= 1

argnames = argnames[1:]

if self._input_types:

# If Defun is given a list of types for the inputs, the number

# of input types should be compatible with 'func'.

num = len(self._input_types)

if num < min_args or num > max_args:

raise ValueError(

"The function has fewer arguments than the number of specified "

"input types.")

return _DefinedFunction(

func,

argnames,

self._input_types,

self._func_name,

self._grad_func,

self._python_grad_func,

out_names=self._out_names,

**self._extra_kwargs)

# 'func' expects no arguments and input types is an empty list.

if min_args == 0 and max_args == 0:

return _DefinedFunction(

func, [], [],

self._func_name,

self._grad_func,

self._python_grad_func,

out_names=self._out_names,

**self._extra_kwargs)

# Input types are unknown. It's an overloaded function and hence

# its definition needs to be deferred until it's called.

return _OverloadedFunction(

func,

argnames,

self._func_name,

self._grad_func,

self._python_grad_func,

out_names=self._out_names,

**self._extra_kwargs)

class _DefinedFunction(object):

"""_DefinedFunction encapsulates a function definition and its properties.

Attributes:

name: The function name.

definition: The definition of this function. A FunctionDef proto.

grad_func_name: If not None, the name of this function's gradient function.

python_grad_func: A python callable implementing the gradient of

the function python-side.

"""

def __init__(self,

func,

argnames,

input_types,

func_name=None,

grad_func=None,

python_grad_func=None,

out_names=None,

shape_func=None,

capture_by_value=False,

whitelisted_stateful_ops=None,

capture_resource_var_by_value=True,

**kwargs):

"""Creates _DefinedFunction.

Args:

func: A python callable which constructs a tf function body.

argnames: A list of strings for function argument names.

input_types: The function's argument types. Can be a tuple, list of

tf data types.

func_name: The function name. Defaults to None, in which derives from

'func'.

grad_func: This function's gradient function, if not None. Defaults

to None.

python_grad_func: A python callable implementing the gradient of

the function python-side.

out_names: An optional list of strings for the function return value

names.

shape_func: An optional function mapping an op to a list of static

output shapes.

capture_by_value: Boolean (defaults to False). If True, captured values

will be copied into the function body.

whitelisted_stateful_ops: A set of ops that if stateful we ignore and

copy into the function body, when `capture_by_value` is True.

capture_resource_var_by_value: Boolean (defaults to True). If False,

captured resource variable returns the handle instead of value.

**kwargs: The keyword arguments. **kwargs is passed to every call

site of this function.

Raises:

ValueError: The function definition is invalid.

"""

self._func = func

self._input_types = input_types

self._func_name = func_name

self._grad_func = grad_func

self._python_grad_func = python_grad_func

self._out_names = out_names

self._shape_func = shape_func

self._capture_by_value = capture_by_value

self._whitelisted_stateful_ops = whitelisted_stateful_ops

if self._whitelisted_stateful_ops is None:

self._whitelisted_stateful_ops = set()

self._capture_resource_var_by_value = capture_resource_var_by_value

self._extra_kwargs = kwargs

# Constructed only when C API is disabled, lazily

self._definition = None

# Constructed only when C API is enabled, lazily

self._c_func = None

self._sub_functions = {} # Constructed with _definition or _c_func

# pylint: disable=protected-access

device_funcs = ops.get_default_graph()._device_functions_outer_to_inner

# pylint: enable=protected-access

# Get the innermost device if possbile.

self._caller_device = device_funcs[-1] if device_funcs else None

# Cached OpDef for this function. When C API is enabled, this is

# the only part of FunctionDef that we cache in Python. When C API

# is disabled the whole _definition is available and this is simply

# another reference to _definition.signature

self._op_def = None

assert isinstance(input_types, (list, tuple))

self._arg_types = input_types

self._arg_names = [argnames[i] if i < len(argnames) else ("arg%d" % i)

for i in range(len(input_types))]

@property

def name(self):

"""Function name."""

self._create_definition_if_needed()

return self._func_name

@property

def definition(self):

"""Function definition proto."""

self._create_definition_if_needed()

if self._c_func:

with c_api_util.tf_buffer() as buf:

c_api.TF_FunctionToFunctionDef(self._c_func.func, buf)

fdef = function_pb2.FunctionDef()

proto_data = c_api.TF_GetBuffer(buf)

fdef.ParseFromString(compat.as_bytes(proto_data))

return fdef

return self._definition

@property

def _signature(self):

self._create_definition_if_needed()

return self._op_def

def set_grad_func(self, grad_func):

"""Specifies the gradient function of this function."""

assert not self._grad_func

assert isinstance(grad_func, _DefinedFunction)

self._grad_func = grad_func

@property

def grad_func_name(self):

"""Returns the name of the gradient function."""

return self._grad_func.name if self._grad_func else None

@property

def python_grad_func(self):

"""Python gradient function callable."""

return self._python_grad_func

@property

def declared_input_types(self):

"""Returns the list of data types of explicit declared inputs."""

return self._input_types

@property

def captured_inputs(self):

"""Returns the list of implicitly captured inputs."""

self._create_definition_if_needed()

return self._extra_inputs

@property

def stateful_ops(self):

"""Returns the list of stateful ops in function definition.

Returns:

A list of (op.name, op.type) pairs.

"""

self._create_definition_if_needed()

return self._stateful_ops

def _create_definition_if_needed(self):

"""Creates the function definition if it's not created yet."""

with context.graph_mode():

self._create_definition_if_needed_impl()

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,

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.op_def.is_stateful]

def _set_c_attrs(self, attrs):

"""Sets `attrs` as attributes of self._c_func.

Requires that self._c_func is not None.

Args:

attrs: a dictionary from attribute name to attribute proto value

"""

for name, attr_value in attrs.items():

serialized = attr_value.SerializeToString()

# TODO(skyewm): this creates and deletes a new TF_Status for every attr.

# It might be worth creating a convenient way to re-use the same status.

c_api.TF_FunctionSetAttrValueProto(self._c_func.func, compat.as_str(name),

serialized)

def _create_hash_str(self, input_arg, output_arg, node_def):

"""Creates an 8-character string unique to this input.

Args:

input_arg: the input_arg field of an OpDef

(e.g. self._definition.signature.input_arg)

output_arg: the output_arg field of an OpDef

(e.g. self._definition.signature.output_arg)

node_def: the node_def field of a FunctionDef

(e.g. self._definition.node_def)

Returns:

The unique string for this input

"""

hasher = hashlib.sha1()

def update_num(n):

hasher.update(compat.as_bytes("%x" % n))

def update_str(s):

update_num(len(s))

hasher.update(compat.as_bytes(s))

def update_strs(slist):

update_num(len(slist))

for s in slist:

update_str(s)

for adef in input_arg:

update_str(adef.SerializeToString())

for adef in output_arg:

update_str(adef.SerializeToString())

for n in sorted(node_def, key=lambda n: n.name):

update_str(n.name)

update_str(n.op)

update_strs(n.input)

update_num(len(n.attr))

# NOTE: protobuf map serialization does not guarantee ordering.

for k in sorted(n.attr):

update_str(k)

update_str(n.attr[k].SerializeToString())

return hasher.hexdigest()[:8]

def add_to_graph(self, g):

"""Adds this function into the graph g."""

self._create_definition_if_needed()

# Adds this function into 'g'.

# pylint: disable=protected-access

if context.executing_eagerly():

context.context().add_function_def(self.definition)

else:

g._add_function(self)

# pylint: enable=protected-access

# Ensures related sub-routines are defined in 'g', too.

for f in self._sub_functions.values():

f.add_to_graph(g)

# Adds its gradient function, too.

if self._grad_func:

self._grad_func.add_to_graph(g)

def __call__(self, *args, **kwargs):

self.add_to_graph(ops.get_default_graph())

args = [ops.convert_to_tensor(_) for _ in args] + self._extra_inputs

ret, op = _call(self._signature, *args, **kwargs)

# Set a hidden attr in 'op' so that gradients_impl can refer back

# to this _DefinedFunction instance to access python_grad_func.

assert isinstance(op, ops.Operation)

setattr(op, "__defun", self)

if self._shape_func is not None:

shapes = self._shape_func(op)

if len(shapes) != len(op.outputs):

raise ValueError("shape_func produced %d shapes for %d outputs" %

(len(shapes), len(op.outputs)))

for (t, shape) in zip(op.outputs, shapes):

t.set_shape(shape)

return ret

class _OverloadedFunction(object):

"""_OverloadedFunction encapsulates an overloaded function.

_OverloadedFunction maintains a mapping from input types to

instantiated _DefinedFunction in self._overload.

"""

def __init__(self,

func,

argnames,

func_name=None,

grad_func=None,

python_grad_func=None,

out_names=None,

**kwargs):

"""Creates _DefinedFunction.

Args:

func: A python callable which constructs a tf function body.

argnames: A list of strings for function argument names.

func_name: The function name. Defaults to None, in which derives from

'func'.

grad_func: This function's gradient function, if not None. Defaults

to None.

python_grad_func: A python callable implementing the gradient of

the function python-side.

out_names: A list of strings for the function return value names.

**kwargs: The keyword arguments. **kwargs is passed to every call

site of this function.

Raises:

ValueError: The function definition is invalid.

"""

self._func = func

self._argnames = argnames

self._func_name = func_name

assert grad_func is None or isinstance(grad_func, _OverloadedFunction)

self._grad_func = grad_func

self._python_grad_func = python_grad_func

self._out_names = out_names

self._extra_kwargs = kwargs

self._overload = {}

def instantiate(self, input_types):

"""Instantiate this function given input argument types.

Args:

input_types: A list of data types for the inputs.

Returns:

_DefinedFunction for the given input types.

"""

# Stringify the type list.

key = _type_list_to_str(input_types)

defined = self._overload.get(key)

if not defined:

# If not defined yet, define the function given the input types.

name = self._func_name

if name is not None:

name = "_".join([name, key])

defined = _DefinedFunction(

self._func,

self._argnames,

input_types,

name,

None,

self._python_grad_func,

out_names=self._out_names,

**self._extra_kwargs)

_ = defined.name # Fully instantiate the function definition.

if self._grad_func:

# If _grad_func is given, it is another

# _OverloadedFunction. We need to instantiate it with the

# right input types.

output_types = [

dtypes.DType(_.type) for _ in defined._signature.output_arg # pylint: disable=protected-access

]

# pylint: disable=protected-access

defined._grad_func = self._grad_func.instantiate(input_types +

output_types)

# pylint: enable=protected-access

self._overload[key] = defined

return defined

def __call__(self, *args, **kwargs):

input_types = []

args = list(args)

for (i, x) in enumerate(args):

x = ops.convert_to_tensor(x)

if not isinstance(x, ops.Tensor):

raise ValueError("Expect a Tensor but get ", x)

input_types.append(x.dtype)

args[i] = x

return self.instantiate(input_types)(*args, **kwargs)

class _FuncGraph(ops.Graph):

"""A helper for constructing a function.

_FuncGraph overrides ops.Graph's create_op() so that we can keep

track of all inputs into every op created inside the function. If

any input is from other graphs, we keep track of it in self.capture

and substitute the input with a place holder.

Each captured input's corresponding place holder is converted into a

function argument and the caller passes in the captured tensor.

"""

def __init__(self, name, capture_by_value, whitelisted_stateful_ops,

capture_resource_var_by_value, *args, **kwargs):

super(_FuncGraph, self).__init__(*args, **kwargs)

self._capture_by_value = capture_by_value

self._whitelisted_stateful_ops = whitelisted_stateful_ops

self._capture_resource_var_by_value = capture_resource_var_by_value

self._building_function = True

self._outer_graph = ops.get_default_graph()

self._vscope = vs.get_variable_scope()

self._old_custom_getter = self._vscope.custom_getter

# The name of the function.

self.name = name

# Placeholder tensors representing the inputs to this function. The tensors

# are in this _FuncGraph.

self.inputs = []

# Tensors that will be returned this function. The tensors are in this

# _FuncGraph.

self.outputs = []

# Maps external tensor -> internal tensor (e.g. input placeholder).

self._captured = {}

# The external tensors that have been captured as inputs and must be passed

# to this function (empty if capturing by value, otherwise these are the

# keys of _captured).

self.extra_inputs = []

# Input placeholders that been added for captured values (empty if capturing

# by value).

self.extra_args = []

# Captured variables.

# TODO(skyewm): is this needed?

self.extra_vars = []

# pylint: disable=g-doc-return-or-yield

@tf_contextlib.contextmanager

def container(self, container_name):

"""Returns a context manager that specifies the resource container to use.

Overridden from `tf.Graph` to update both the init_scope container

and the present inner container. This is necessary to make sure setting

containers applies correctly both to created variables and to stateful

ops.

Args:

container_name: container name string.

Returns:

A context manager for defining resource containers for stateful ops,

yields the container name.

"""

original_container = self._container

# pylint: disable=protected-access

with ops.init_scope():

original_init_container = ops.get_default_graph()._container

try:

self._container = container_name

with ops.init_scope():

ops.get_default_graph()._container = container_name

yield self._container

finally:

self._container = original_container

with ops.init_scope():

ops.get_default_graph()._container = original_init_container

# pylint: enable=protected-access

# pylint: enable=g-doc-return-or-yield

def getvar(

self,

getter,

name,

shape=None,

dtype=None,

initializer=None,

reuse=None,

trainable=True,

collections=None, # pylint: disable=redefined-outer-name

use_resource=None,

**kwargs):

"""A custom variable getter."""

# Here, we switch the default graph to the outer graph and ask the

# variable scope in which the function is defined to give us the

# variable. The variable is stashed in extra_vars and returned to

# the caller.

#

# We capture these variables so that the variable definition is

# hoisted upward to the outer most graph.

with self._outer_graph.as_default():

# pylint: disable=protected-access

var = self._vscope.get_variable(

vs._get_default_variable_store(),

name,

shape=shape,

dtype=dtype,

initializer=initializer,

reuse=reuse,

trainable=trainable,

collections=collections,

use_resource=use_resource)

self.extra_vars.append(var)

if (isinstance(var, resource_variable_ops.ResourceVariable) and

self._capture_resource_var_by_value):

# For resource-based variables read the variable outside the function

# and pass in the value. This ensures that the function is pure and

# differentiable. TODO(apassos) this may have performance problems if

# the function will only do embedding lookups on the variable.

return var.value()

return var

def create_op(self, op_type, inputs, dtypes=None, **kwargs): # pylint: disable=redefined-outer-name

for i, x in enumerate(inputs):

if isinstance(x, ops.EagerTensor) or x.graph is not self:

inputs[i] = self.capture(x)

return super(_FuncGraph, self).create_op(op_type, inputs,

dtypes=dtypes, **kwargs)

def capture(self, tensor, name=None):

"""Adds the given tensor to this graph and returns the captured tensor."""

if tensor in self._captured:

# Captured already.

return self._captured[tensor]

elif self._capture_by_value:

return self._add_tensor_and_parents(tensor)

else:

return self._capture_tensor_as_extra_input(tensor, name)

def _capture_tensor_as_extra_input(self, tensor, name=None):

# Substitute with a placeholder.

self.extra_inputs.append(tensor)

# Hoist the new input placeholder out of any control flow context

# we're currently in.

with ops.control_dependencies(None):

ph = array_ops.placeholder(

tensor.dtype, shape=tensor.get_shape(), name=name)

# pylint: disable=protected-access

if isinstance(tensor, ops.EagerTensor):

handle_data = tensor._handle_data

if handle_data:

handle_data = handle_data.SerializeToString()

else:

handle_data = c_api.GetHandleShapeAndType(tensor.graph._c_graph,

tensor._as_tf_output())

if handle_data:

c_api.SetHandleShapeAndType(ph.graph._c_graph, ph._as_tf_output(),

compat.as_bytes(handle_data))

# pylint: enable=protected-access

self.inputs.append(ph)

self._captured[tensor] = ph

self.extra_args.append(ph)

if _is_guaranteed_const(tensor):

with ops.control_dependencies(None):

return array_ops.guarantee_const(ph)

else:

return ph

def _add_tensor_and_parents(self, tensor):

op = self._add_op_and_parents(tensor.op)

return op.outputs[tensor.value_index]

def _add_op_and_parents(self, op):

# pylint: disable=protected-access

op_def = graph_to_function_def._get_op_def(op)

# pylint: enable=protected-access

if op_def.is_stateful and op not in self._whitelisted_stateful_ops:

raise ValueError("Cannot capture a stateful node (name:%s, type:%s) "

"by value." % (op.name, op.type))

elif op.type in ("Placeholder", "PlaceholderV2"):

raise ValueError("Cannot capture a placeholder (name:%s, type:%s) "

"by value." % (op.name, op.type))

captured_inputs = [self._add_tensor_and_parents(x) for x in op.inputs]

captured_op = self.create_op(

op.type,

captured_inputs, [o.dtype for o in op.outputs],

name=op.name,

attrs=op.node_def.attr,

op_def=op_def)

for t, captured_t in zip(op.outputs, captured_op.outputs):

self._captured[t] = captured_t

return captured_op

def func_graph_from_py_func(func,

arg_names,

arg_types,

name=None,

capture_by_value=False,

device=None,

colocation_stack=None,

container=None,

collections_ref=None,

arg_shapes=None,

whitelisted_stateful_ops=None,

capture_resource_var_by_value=True):

"""Returns a _FuncGraph generated from `func`.

Args:

func: A Python callable which constructs a TF function body. The arguments

must correspond to `arg_types`. Returns a value or list/tuple of values.

No returned value can be None.

arg_names: A sequence of strings for the function argument names.

arg_types: A sequence of the function's argument types.

name: The function name. If None, the name is derived from `func`.

capture_by_value: boolean. If True, captured values will be copied into the

function body.

device: device name or function.

colocation_stack: A colocation stack (list) the _FuncGraph should use.

container: A container name the _FuncGraph should start with.

collections_ref: A reference to a collections dict the _FuncGraph should

use internally.

arg_shapes: A sequence of the function's argument shapes.

whitelisted_stateful_ops: A set of ops that if stateful we ignore and

re-create.

capture_resource_var_by_value: Boolean (defaults to True). If False,

captured resource variable returns the handle instead of value.

Returns:

A _FuncGraph.

Raises:

ValueError: if func returns None.

"""

if not name:

name = function_utils.get_func_name(func)

func_graph = _FuncGraph(name, capture_by_value, whitelisted_stateful_ops,

capture_resource_var_by_value)

with func_graph.as_default(), ops.device(device):

# pylint: disable=protected-access

if collections_ref is not None:

func_graph._collections = collections_ref

if container is not None:

func_graph._container = container

if colocation_stack is not None:

func_graph._colocation_stack = colocation_stack

# pylint: enable=protected-access

if arg_shapes is None:

arg_shapes = [None] * len(arg_types)

# Create placeholders for the function arguments.

for (argname, argtype, argshape) in zip(arg_names, arg_types, arg_shapes):

argholder = array_ops.placeholder(argtype, shape=argshape, name=argname)

func_graph.inputs.append(argholder)

# Call func and gather the output tensors.

with vs.variable_scope("", custom_getter=func_graph.getvar):

outputs = func(*func_graph.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 %s can not return None." % name)

# Ensures each output is a Tensor in the function graph.

outputs = [ops.convert_to_tensor(t) for t in outputs]

outputs = [func_graph.capture(t) if t.graph is not func_graph else t

for t in outputs]

func_graph.outputs = outputs

return func_graph

def _is_guaranteed_const(tensor):

"""Determines whether `tensor` is guaranteed to be a constant.

A tensor is guaranteed to be a constant if either it was produced by

a `GuaranteeConst` op or if all of its children are guaranteed to be

constants.

Args:

tensor: The tensor for which to determine const-ness.

Returns:

True if `tensor` is guaranteed to be a constant, False otherwise.

"""

if isinstance(tensor, ops.EagerTensor):

return False

class Work(object):

def __init__(self, op, leaving):

self.op = op

self.leaving = leaving

is_guaranteed_const = lambda op: op.node_def.op == "GuaranteeConst"

constants = set([])

def all_inputs_const(op):

# If all inputs of an op are guaranteed constants, then we can infer that

# the op produces a constant as well.

return op.inputs and all(inp.op in constants for inp in op.inputs)

visited = set([])

stack = [Work(tensor.op, leaving=False)]

while stack:

work = stack.pop()

if work.leaving:

if all_inputs_const(work.op):

constants.add(work.op)

continue

visited.add(work.op)

if is_guaranteed_const(work.op):

constants.add(work.op)

continue

# This op will be revisited after all its inputs are checked for const-ness.

stack.append(Work(work.op, leaving=True))

for inp in work.op.inputs:

if inp.op not in visited:

stack.append(Work(inp.op, leaving=False))

return tensor.op in constants

def _call(sig, *inputs, **kwargs):

"""Adds a node calling a function.

This adds a `call` op to the default graph that calls the function

of signature `sig`, passing the tensors in `inputs` as arguments.

It returns the outputs of the call, which are one or more tensors.

`sig` is OpDefArg.a `_DefinedFunction` object.

You can pass an optional keyword parameter `name=string` to name the

added operation.

You can pass an optional keyword parameter `noinline=True|False` to

instruct the runtime not to inline the function body into the call

site.

Args:

sig: OpDefArg. The signature of the function.

*inputs: arguments to the function.

**kwargs: Optional keyword arguments. Can only contain 'name' or

'noinline'.

Returns:

A 2-element tuple. First element: a Tensor if the function returns a single

value; a list of Tensors if the function returns multiple value; the