# 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.

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

"""FuncGraph and related functionality."""

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

import collections as py_collections

import itertools

import weakref

import numpy as np

from tensorflow.core.framework import attr_value_pb2

from tensorflow.python.eager import context

from tensorflow.python.eager import execute

from tensorflow.python.eager import tape

from tensorflow.python.eager.graph_only_ops import graph_placeholder

from tensorflow.python.framework import composite_tensor

from tensorflow.python.framework import constant_op

from tensorflow.python.framework import dtypes

from tensorflow.python.framework import errors

from tensorflow.python.framework import ops

from tensorflow.python.framework import tensor_spec

from tensorflow.python.framework import type_spec

from tensorflow.python.framework.auto_control_deps import AutomaticControlDependencies

from tensorflow.python.ops import array_ops

from tensorflow.python.ops import custom_gradient

from tensorflow.python.ops import resource_variable_ops

from tensorflow.python.ops import tensor_array_ops

from tensorflow.python.ops import variable_scope

from tensorflow.python.util import compat

from tensorflow.python.util import memory

from tensorflow.python.util import nest

from tensorflow.python.util import object_identity

from tensorflow.python.util import tf_contextlib

from tensorflow.python.util import tf_decorator

from tensorflow.python.util.lazy_loader import LazyLoader

# This is to avoid a circular dependency:

# function -> func_graph

function = LazyLoader("function", globals(),

"tensorflow.python.eager.function")

def_function = LazyLoader(

"def_function", globals(),

"tensorflow.python.eager.def_function")

WHITELIST_COLLECTIONS = [

ops.GraphKeys.GLOBAL_VARIABLES,

ops.GraphKeys.LOCAL_VARIABLES,

ops.GraphKeys.TRAINABLE_VARIABLES,

variable_scope._VARSTORE_KEY, # pylint: disable=protected-access

variable_scope._VARSCOPESTORE_KEY # pylint: disable=protected-access

]

_EAGER_CONST_THRESHOLD = 128

class UnknownArgument(object):

"""Signifies an argument which is not currently handled."""

pass

def convert_structure_to_signature(structure, arg_names=None):

"""Convert a potentially nested structure to a signature.

Args:

structure: Structure to convert, where top level collection is a list or a

tuple.

arg_names: Optional list of arguments that has equal number of elements as

`structure` and is used for naming corresponding TensorSpecs.

Returns:

Identical structure that has TensorSpec objects instead of Tensors and

UknownArgument instead of any unsupported types.

"""

def encode_arg(arg, path):

"""A representation for this argument, for converting into signatures."""

if isinstance(arg, ops.Tensor):

user_specified_name = None

try:

user_specified_name = compat.as_str(

arg.op.get_attr("_user_specified_name"))

except ValueError:

pass

if path and user_specified_name and user_specified_name != path[0]:

# The user has explicitly named the argument differently than the name

# of the function argument.

name = user_specified_name

else:

name = "/".join([str(p) for p in path])

return tensor_spec.TensorSpec(arg.shape, arg.dtype, name)

if isinstance(arg, composite_tensor.CompositeTensor):

# TODO(b/133606651) Do we need to inject arg_name?

return arg._type_spec # pylint: disable=protected-access

if isinstance(arg, (

int,

float,

bool,

type(None),

dtypes.DType,

tensor_spec.TensorSpec,

type_spec.TypeSpec,

)):

return arg

return UnknownArgument()

# We are using the flattened paths to name the TensorSpecs. We need an

# explicit name for them downstream.

flattened = nest.flatten_with_tuple_paths(structure)

if arg_names:

if len(arg_names) != len(structure):

raise ValueError(

"Passed in arg_names don't match actual signature (%s)." % arg_names)

# Replace all top-level names with their actual arg_names. If a path before

# was "(2,'a',1)", it will become "(arg_names[2],'a',1)".

flattened = [

((arg_names[path[0]],) + path[1:], arg) for path, arg in flattened

]

mapped = [encode_arg(arg, path) for path, arg in flattened]

return nest.pack_sequence_as(structure, mapped)

class FuncGraph(ops.Graph):

"""Graph representing a function body.

Attributes:

name: The name of the function.

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

tensors are in this FuncGraph. This represents "regular" inputs as well as

captured inputs (i.e. the values of self.captures), with the regular

inputs coming first.

outputs: Tensors that will be returned by this function. The tensors are in

this FuncGraph.

control_outputs: Operations that must be executed before the function

represented by this graph can be said to have been executed.

structured_input_signature: A tuple of (args, kwargs), which are both

possibly-nested python objects that were received by this function. Note

that these structures might contain Python `None`s.

structured_outputs: A possibly-nested python object which will be returned

by this function. The Tensors in this structure are the same as those of

self.outputs. Note that this structure might contain Python `None`s.

variables: Variables that should be watched during function execution.

outer_graph: The graph this function is defined in. May be another FuncGraph

or the global default Graph.

captures: Maps external tensor -> internal tensor (i.e. input placeholder).

The entries are in the order they were captured.

control_captures: Set of external ops on which this graph has a control

dependency.

seed: The graph-level random seed.

capture_by_value: If True, the func graph will capture Variables by value

instead of reference.

"""

def __init__(self, name, collections=None, capture_by_value=None):

"""Construct a new FuncGraph.

The graph will inherit its graph key, collections, seed, and distribution

strategy stack from the current context or graph.

Args:

name: the name of the function.

collections: a dictionary of collections this FuncGraph should start

with. If not specified (None), the FuncGraph will read (but not write

to) the outer graph's collections that are not whitelisted, and both

read and write to the outer graph's collections that are whitelisted.

The current whitelisted collections are the global variables, the

local variables, and the trainable variables.

Defaults to None.

capture_by_value: An optional boolean. If True, the func graph will

capture Variables by value instead of reference. By default inherit

from outer graphs, and failing that will default to False.

"""

super(FuncGraph, self).__init__()

self.name = name

self.inputs = []

self.outputs = []

self.control_outputs = []

self.control_captures = set()

self.structured_input_signature = None

self.structured_outputs = None

self._weak_variables = []

self._watched_variables = object_identity.ObjectIdentityWeakSet()

self.outer_graph = ops.get_default_graph()

self._captures = py_collections.OrderedDict()

# If not None, records the names of output args of this function. Used to

# preserve the output names in the signature of a serialized+deserialized

# function. Private at the moment mostly because it's often out of date.

self._output_names = None

# Maps arbitrary key -> (closure, nest of placeholders), where at function

# call time the value of closure() will be used to feed the nest of

# placeholders.

self._deferred_captures = py_collections.OrderedDict()

# Inherit capture-by-value from outer graph.

if capture_by_value is not None:

self.capture_by_value = capture_by_value

elif self.outer_graph is not None and isinstance(

self.outer_graph, FuncGraph):

self.capture_by_value = self.outer_graph.capture_by_value

else:

self.capture_by_value = False

self._building_function = True

# Map from resource tensor name to last op (in program order) which uses

# this tensor. Used to enforce that execution order matches program order

# for resource tensors.

self._last_op_using_resource_tensor = {}

graph = self.outer_graph

if context.executing_eagerly():

self.seed = context.global_seed()

# [for tf-data user migration from TF1.0 to 2.0] seed_used keep track of

# any None op_seed for random_op in the function, in which case we end up

# using function seed, which could be unintended behavior for the op.

self._seed_used = False

else:

self.seed = graph.seed

self._seed_used = False

# TODO(allenl): Figure out if we can remove colocation stack

# specialization (currently used in cond_v2), here and in the cache key.

self._colocation_stack = graph._colocation_stack.copy() # pylint: disable=protected-access

if collections is None:

for collection_name in graph.get_all_collection_keys():

if collection_name not in WHITELIST_COLLECTIONS:

self._collections[collection_name] = graph.get_collection(

collection_name)

for collection_name in WHITELIST_COLLECTIONS:

self._collections[collection_name] = graph.get_collection_ref(

collection_name)

else:

self._collections = collections

# Keep track of whether this FuncGraph is exportable to SavedModel. Use

# `graph.mark_as_unsaveable(reason)` to mark this FuncGraph and any

# dependent functions as unsaveable.

self._saveable = True

self._saving_errors = set()

def __str__(self):

return "FuncGraph(name=%s, id=%s)" % (self.name, id(self))

def watch_variable(self, v):

"""Marks the variable v as accessed while building this graph."""

while self is not None and isinstance(self, FuncGraph):

self._watched_variables.add(v)

self = self.outer_graph

def capture_call_time_value(self, closure, spec, key=None):

"""Creates a placeholder which at call time has the value closure().

Useful, for example, to respect TensorFlow context managers, which are often

dynamically scoped.

Args:

closure: function which takes no arguments, to be evaluated at function

call time, returning a nest of tensors compatible with `spec`.

spec: nest of TypeSpec for the value to capture.

key: optional. If not None, multiple calls to lazy_capture with the same

key in the same graph will return the same placeholder, and the

first closure will be used at function call time.

Returns:

Nest of placeholders which, at function call time, will be fed with the

result of calling closure().

Raises:

ValueError: at function call time, if the return value of closure() is

not compatible with `spec`.

"""

if key is None:

key = object()

if key not in self._deferred_captures:

def convert_to_placeholder(s):

if not isinstance(s, tensor_spec.TensorSpec):

raise TypeError(

"Expected a nest of `TypeSpec` objects, found %s of type %s." %

(s, type(s)))

return array_ops.placeholder(dtype=s.dtype, shape=s.shape)

placeholder = nest.map_structure(

convert_to_placeholder, spec, expand_composites=True)

def wrapped_closure():

ret_nest = closure()

nest.assert_same_structure(spec, ret_nest, expand_composites=True)

# This uses the tensor dtype defined in `spec` when converting values

# in `ret_nest` to tensors.

# pylint: disable=protected-access

y = nest.map_structure(lambda s, r: s._to_components(r), spec, ret_nest,

expand_composites=False)

# pylint: enable=protected-access

return nest.flatten(y, expand_composites=True)

self._deferred_captures[key] = (wrapped_closure, placeholder)

return self._deferred_captures[key][1]

def control_dependencies(self, control_inputs):

"""Handles control dependencies.

FuncGraph wraps Graph's control_dependencies logic by first filtering out

any external tensors / operations and storing them in the graph's

control_captures member. Any consumers of this function graph must then

decide how to handle the control captures.

Args:

control_inputs: A list of `Operation` or `Tensor` objects which

must be executed or computed before running the operations

defined in the context. Can also be `None` to clear the control

dependencies.

Returns:

A context manager that specifies control dependencies for all

operations constructed within the context.

Raises:

TypeError: If `control_inputs` is not a list of `Operation` or

`Tensor` objects.

"""

if control_inputs is None:

return super(FuncGraph, self).control_dependencies(control_inputs)

filtered_control_inputs = []

for c in control_inputs:

# Check for _UnreadVariable

if (isinstance(c, ops.IndexedSlices) or

(hasattr(c, "_handle") and hasattr(c, "op"))):

c = c.op

graph_element = ops._as_graph_element(c) # pylint: disable=protected-access

if graph_element is None:

graph_element = c

if graph_element is not None and getattr(

graph_element, "graph", None) is not self:

self.control_captures.add(graph_element)

else:

filtered_control_inputs.append(graph_element)

return super(FuncGraph, self).control_dependencies(filtered_control_inputs)

def as_default(self):

outer_cm = super(FuncGraph, self).as_default()

@tf_contextlib.contextmanager

def inner_cm():

"""Context manager for copying distribute.Strategy scope information."""

graph = ops.get_default_graph()

# pylint: disable=protected-access

# TODO(b/112906995, nareshmodi): distribution strategy depends on

# inheriting this stack from the default graph even in eager mode. Maybe

# it should be part of the eager context? This would also allow us to

# remove a get_default_graph() call from the function cache lookup.

old_strategy_stack = self._distribution_strategy_stack

self._distribution_strategy_stack = list(

graph._distribution_strategy_stack)

# We ignore device placements from any outer scopes while tracing the

# function when possible, to avoid hard-coding them in the function

# graph. "Default" placements come from the PartitionedCallOp's placement,

# so that the same trace of the Python function may be placed on several

# different devices and saved functions may be placed on new devices when

# restored.

old_device_stack = self._device_function_stack

if context.executing_eagerly():

if self._distribution_strategy_stack:

self._device_function_stack = self._device_function_stack.copy()

self._add_device_to_stack(context.context().device_name)

else:

if (self._distribution_strategy_stack

or device_stack_has_callable(graph._device_function_stack)):

# Hard-code devices from device functions in the function body

self._device_function_stack = graph._device_function_stack.copy()

old_creator_stack = self._variable_creator_stack

self._variable_creator_stack = graph._variable_creator_stack

# Inherit the graph key, since this is used for matching variables in

# optimizers.

old_graph_key = self._graph_key

self._graph_key = graph._graph_key

# Inherit the auto_cast_variable_read_dtype, since this should not change

# inside a function.

old_auto_cast_var_read_dtype = self._auto_cast_variable_read_dtype

self._auto_cast_variable_read_dtype = graph._auto_cast_variable_read_dtype

# pylint: enable=protected-access

with outer_cm as g:

try:

yield g

finally:

self._distribution_strategy_stack = old_strategy_stack

self._device_function_stack = old_device_stack

self._variable_creator_stack = old_creator_stack

self._graph_key = old_graph_key

self._auto_cast_variable_read_dtype = old_auto_cast_var_read_dtype

return inner_cm()

@property

def output_types(self):

return [t.dtype for t in self.outputs]

@property

def output_shapes(self):

return [t.shape for t in self.outputs]

@property

def variables(self):

"""A list of variables accessed by this FuncGraph.

Note that functions keep only weak references to variables. Calling the

function after a variable it accesses has been deleted is an error.

Yields:

Strong references to variables accessed by this FuncGraph.

"""

for weak_v in self._weak_variables:

v = weak_v()

if v is None:

raise AssertionError(

"Called a function referencing variables which have been deleted. "

"This likely means that function-local variables were created and "

"not referenced elsewhere in the program. This is generally a "

"mistake; consider storing variables in an object attribute on "

"first call.")

yield v

@variables.setter

def variables(self, var_list):

self._weak_variables = [weakref.ref(v) for v in var_list]

def _capture_by_value(

self,

op_type,

inputs,

dtypes, # pylint: disable=redefined-outer-name

input_types=None,

name=None,

attrs=None,

op_def=None,

compute_device=True):

# When capturing by value, do the read outside

reverse_captures = dict((id(v), k) for k, v in self.captures)

uncaptured_inputs = [reverse_captures.get(id(t), t) for t in inputs]

with ops.init_scope():

if context.executing_eagerly():

attr_list = ("dtype", int(attrs["dtype"].type))

value, = execute.execute(

compat.as_bytes(op_type), 1, uncaptured_inputs, attr_list,

context.context())

else:

op = ops.get_default_graph()._create_op_internal( # pylint: disable=protected-access

op_type,

uncaptured_inputs,

dtypes,

input_types,

name,

attrs,

op_def,

compute_device)

value = op.outputs[0]

captured_value = self.capture(value)

return captured_value.op

def create_op(

self,

op_type,

inputs,

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

input_types=None,

name=None,

attrs=None,

op_def=None,

compute_shapes=True,

compute_device=True):

"""Like Graph.create_op, except handles external input tensors.

This overload adds functionality to create_op to "capture" any external

input tensors, i.e. tensors from the eager context or outer function graphs

if this is a nested function. See `capture` for more information.

Args:

op_type: The `Operation` type to create. This corresponds to the

`OpDef.name` field for the proto that defines the operation.

inputs: A list of `Tensor` objects that will be inputs to the `Operation`.

dtypes: (Optional) A list of `DType` objects that will be the types of the

tensors that the operation produces.

input_types: (Optional.) A list of `DType`s that will be the types of

the tensors that the operation consumes. By default, uses the base

`DType` of each input in `inputs`. Operations that expect

reference-typed inputs must specify `input_types` explicitly.

name: (Optional.) A string name for the operation. If not specified, a

name is generated based on `op_type`.

attrs: (Optional.) A dictionary where the key is the attribute name (a

string) and the value is the respective `attr` attribute of the

`NodeDef` proto that will represent the operation (an `AttrValue`

proto).

op_def: (Optional.) The `OpDef` proto that describes the `op_type` that

the operation will have.

compute_shapes: (Optional.) Deprecated. Has no effect (shapes are always

computed).

compute_device: (Optional.) If True, device functions will be executed

to compute the device property of the Operation.

Returns:

An `Operation` object.

"""

del compute_shapes

if self.capture_by_value and op_type in ["ReadVariableOp",

"ResourceGather"]:

return self._capture_by_value(op_type, inputs, dtypes, input_types, name,

attrs, op_def, compute_device)

# This capturing logic interacts poorly with control flow contexts which

# want to replace inputs of ops far too late in the process. This can lead

# the context to get confused and try to create an Enter for an Enter. We

# can detect this here and skip the additional Enter which can confuse loop

# validation logic.

if op_type == "Enter" and inputs[0].op.type == "Enter":

if inputs[0].op.get_attr("frame_name") == attrs["frame_name"].s:

return inputs[0].op

# Calling AddValue on the control flow contexts to force creation of the

# backward accumulators in the original graph before we create placeholders

# to capture the inputs.

ctxt = ops.get_default_graph()._control_flow_context # pylint: disable=protected-access

for i, inp in enumerate(inputs):

# TPU Estimator defines a control flow context with no AddValue method.

if ctxt is not None and hasattr(ctxt, "AddValue"):

inp = ctxt.AddValue(inp)

inp = self.capture(inp)

inputs[i] = inp

return super(FuncGraph, self)._create_op_internal( # pylint: disable=protected-access

op_type, inputs, dtypes, input_types, name, attrs, op_def,

compute_device)

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

"""Captures `tensor` if it's external to this graph.

If `tensor` is from a different graph, returns a placeholder for it.

`tensor` and the placeholder will appear in self.captures, and the

placeholder will appear in self.inputs. Multiple calls to this method with

the same `tensor` argument will return the same placeholder. If `tensor` is

from this graph, returns `tensor`.

Args:

tensor: Tensor. May be from this FuncGraph or a different graph.

name: Optional name if a placeholder is created.

Returns:

Tensor from this FuncGraph.

Raises:

InaccessibleTensorError: if any tensors are accessed in a manner that

bypasses the mechanisms required for the data dependencies to be correctly

wired.

"""

# Note: _forward_func_graph is currently only set when building the gradient

# graph graph of a defun call. If the backwards graph tries to capture

# tensors those will be captured first in the forward graph. This

# makes sure that any tensor needed by a custom_gradient is correctly

# captured.

if (getattr(tensor, "graph", None) is not self and

hasattr(self, "_forward_func_graph") and

isinstance(self._forward_func_graph, FuncGraph)):

tensor = self._forward_func_graph.capture(tensor)

if isinstance(tensor, ops.EagerTensor):

if name is None:

name = str(ops.uid())

# Small EagerTensors are captured with Const ops

if (tensor.dtype in dtypes.TF_VALUE_DTYPES and

np.prod(tensor.shape) <= _EAGER_CONST_THRESHOLD):

return self.capture_eager_tensor(tensor, name)

# Large EagerTensors and resources are captured with Placeholder ops

return self._capture_helper(tensor, name)

if tensor.graph is not self:

if name is None:

name = tensor.op.name

inner_graph = tensor.graph

while inner_graph is not None and isinstance(inner_graph, FuncGraph):

if inner_graph is self:

raise errors.InaccessibleTensorError(

"The tensor '%s' cannot be accessed here: it is defined"

" in another function or code block. Use return values,"

" explicit Python locals or TensorFlow collections to access"

" it. Defined in: %s; accessed from: %s.\n"

% (tensor, tensor.graph, self))

inner_graph = inner_graph.outer_graph

return self._capture_helper(tensor, name)

return tensor

def _capture_helper(self, tensor, name):

capture = self._captures.get(ops.tensor_id(tensor))

if capture is None:

placeholder = _create_substitute_placeholder(

tensor, name=name, dtype=tensor.dtype)

self.add_capture(tensor, placeholder)

else:

placeholder = capture[1]

tape.record_operation("captured_value", [placeholder], [tensor],

lambda x: [x])

return placeholder

@property

def captures(self):

"""Order list of tuples containing external and internal captures."""

return self._captures.values()

def add_capture(self, tensor, placeholder):

"""Capture a specific tensor and utilize the provided placeholder.

Args:

tensor: Tensor to captures.

placeholder: Provided placeholder for the tensor.

"""

self._captures[ops.tensor_id(tensor)] = (tensor, placeholder)

self.inputs.append(placeholder)

def reset_captures(self, capture_list):

"""Set the captures with the provided list of captures & placeholder."""

self._captures = py_collections.OrderedDict()

for tensor, placeholder in capture_list:

self._captures[ops.tensor_id(tensor)] = (tensor, placeholder)

def pop_capture(self, tensor):

"""Remove the capture and return the generated placeholder."""

capture = self._captures.pop(ops.tensor_id(tensor), None)

if capture is None:

return None

return capture[1]

def clear_captures(self):

# TODO(b/115366440): Delete this method when a custom OrderedDict is added.

# Clearing captures using clear() leaves some cycles around.

while self._captures:

self._captures.popitem()

memory.dismantle_ordered_dict(self._captures)

while self._deferred_captures:

self._deferred_captures.popitem()

memory.dismantle_ordered_dict(self._deferred_captures)

def capture_distributed_variable(self, variable, placeholder):

"""Add given distributed variable to captures with given placeholder."""

self._captures[ops.tensor_id(variable)] = (variable, placeholder)

tape.record_operation("captured_value", [placeholder], [variable],

lambda x: [x])

def capture_eager_tensor(self, tensor, name):

capture = self._captures.get(ops.tensor_id(tensor))

if capture is None:

# We clear all control dependencies and place the Const op on the same

# device as the source tensor. The device placement may be relaxed at

# a later date.

with ops.control_dependencies(None), self.device(tensor.device):

graph_const = constant_op.constant(tensor.numpy(), dtype=tensor.dtype,

shape=tensor.shape, name=name)

self.add_capture(tensor, graph_const)

else:

graph_const = capture[1]

tape.record_operation("captured_value", [graph_const], [tensor],

lambda x: [x])

return graph_const

@property

def external_captures(self):

"""External tensors captured by this function."""

return [c[0] for c in self._captures.values()]

@property

def internal_captures(self):

"""Placeholders in this function corresponding captured tensors."""

return [c[1] for c in self._captures.values()]

@property

def deferred_external_captures(self):

"""Ordered nest of tensors whose placeholders will be fed at call time."""

return [c[0] for c in self._deferred_captures.values()]

@property

def deferred_internal_captures(self):

"""List of nest of placeholders which at call time will be fed."""

return [c[1] for c in self._deferred_captures.values()]

@property

def variable_captures(self):

"""Map of tensor ids of variable handles to variables which are captured."""

return {

ops.tensor_id(self._captures[ops.tensor_id(v.handle)][1]): v

for v in self.variables

if ops.tensor_id(v.handle) in self._captures

}

def mark_as_unsaveable(self, error_message):

"""Marks this FuncGraph as unsaveable.

Any attempts to export this FuncGraph will raise an error with the specified

message.

Args:

error_message: List or string containing the error message to be raised

when saving this FuncGraph to SavedModel.

"""

self._saveable = False

if isinstance(error_message, str):

error_message = [error_message]

self._saving_errors.update(error_message)

@property

def saveable(self):

"""Returns whether this FuncGraph is saveable."""

return self._saveable

@property

def saving_errors(self):

"""Returns set of errors preventing this FuncGraph from being saved."""

return self._saving_errors

def func_graph_from_py_func(name,

python_func,

args,

kwargs,

signature=None,

func_graph=None,

autograph=False,

autograph_options=None,

add_control_dependencies=True,

arg_names=None,

op_return_value=None,

collections=None,

capture_by_value=None,

override_flat_arg_shapes=None):

"""Returns a `FuncGraph` generated from `python_func`.

Args:

name: an identifier for the function.

python_func: the Python function to trace.

args: the positional args with which the Python function should be called;

ignored if a signature is provided.

kwargs: the keyword args with which the Python function should be called;

ignored if a signature is provided.

signature: a possibly nested sequence of `TensorSpecs` specifying the shapes

and dtypes of the arguments. When a signature is provided, `args` and

`kwargs` are ignored, and `python_func` is traced with Tensors conforming

to `signature`. If `None`, the shapes and dtypes are inferred from the

inputs.

func_graph: Optional. An instance of FuncGraph. If provided, we will use

this graph else a new one is built and returned.

autograph: whether to use autograph to compile `python_func`.

See https://www.tensorflow.org/guide/autograph for more information.

autograph_options: additional knobs to control when `autograph=True`.

See https://www.tensorflow.org/guide/autograph for more information.

add_control_dependencies: If True, automatically adds control dependencies

to ensure program order matches execution order and stateful ops always

execute.

arg_names: Optional list of argument names, used to give input placeholders

recognizable names.

op_return_value: Optional. A Tensor. If set and `python_func` returns

Operations, those return values will be replaced with this value. If not

set, returning an Operation triggers an error.

collections: a dictionary of collections this FuncGraph should start

with. If not specified (None), the FuncGraph will read (but not write to)

the outer graph's collections that are not whitelisted, and both

read and write to the outer graph's collections that are whitelisted.

The current whitelisted collections are the global variables, the

local variables, and the trainable variables.

Defaults to None.

capture_by_value: An optional boolean. If True, the func graph will capture

Variables by value instead of reference. By default inherit from outer

graphs, and failing that will default to False.

override_flat_arg_shapes: An optional list of instances that are either

`None` or `TensorShape`. The length must match that of

`nest.flatten((args, kwargs), expand_composites=True)`. The entries

containing value `None` must match entries in flattened arguments

containing non-tensors, while entries containing a `TensorShape` must

match entries in the flattened arguments containing tensors.

Returns:

A FuncGraph.

Raises:

TypeError: If any of `python_func`'s return values is neither `None` nor a

`Tensor`.

ValueError: If both `signature` and `override_flat_arg_shapes` are

passed in.

"""

if op_return_value is not None:

assert isinstance(op_return_value, ops.Tensor), op_return_value

if func_graph is None:

func_graph = FuncGraph(name, collections=collections,

capture_by_value=capture_by_value)

assert isinstance(func_graph, FuncGraph)

if add_control_dependencies:

control_manager = AutomaticControlDependencies()

else:

control_manager = ops.NullContextmanager()

with func_graph.as_default(), control_manager as a:

current_scope = variable_scope.get_variable_scope()

default_use_recource = current_scope.use_resource

current_scope.set_use_resource(True)

if signature is not None and override_flat_arg_shapes is not None:

raise ValueError(

"Passed both signature and override_flat_arg_shapes: %s and %s."

% (signature, override_flat_arg_shapes))

if signature is not None:

args = signature

kwargs = {}

# Creates and names placeholders for all arguments.

if override_flat_arg_shapes is not None:

flat_args = nest.flatten(args, expand_composites=True)

arg_shapes = override_flat_arg_shapes[:len(flat_args)]

kwarg_shapes = override_flat_arg_shapes[len(flat_args):]

else:

arg_shapes = None

kwarg_shapes = None

func_args = _get_defun_inputs_from_args(

args, arg_names, flat_shapes=arg_shapes)

func_kwargs = _get_defun_inputs_from_kwargs(

kwargs, flat_shapes=kwarg_shapes)

# Convert all Tensors into TensorSpecs before saving the structured inputs.

# If storing pure concrete functions that are not called through polymorphic

# functions, we don't have access to FunctionSpec, so we need to call the

# TensorSpecs by their `arg_names` for later binding.

func_graph.structured_input_signature = (

convert_structure_to_signature(func_args, arg_names),

convert_structure_to_signature(func_kwargs))

flat_func_args = nest.flatten(func_args, expand_composites=True)

flat_func_kwargs = nest.flatten(func_kwargs, expand_composites=True)

# Temporarily set inputs to allow graph building code to inspect

# them. Reassigned below.

func_graph.inputs = [arg for arg in flat_func_args + flat_func_kwargs

if isinstance(arg, ops.Tensor)]

# Note: `nest.flatten` sorts by keys, as does `_deterministic_dict_values`.

# Variables to help check whether mutation happens in calling the function

# Copy the recursive list, tuple and map structure, but not base objects

func_args_before = nest.pack_sequence_as(func_args, flat_func_args,

expand_composites=True)

func_kwargs_before = nest.pack_sequence_as(

func_kwargs, flat_func_kwargs, expand_composites=True)

def convert(x):

"""Converts a function output to a Tensor."""

if x is None:

return None

if op_return_value is not None and isinstance(x, ops.Operation):

# TODO(b/79881896): we currently can't capture external control deps, so

# this won't work if x needs to be captured (i.e. if python_func returns

# captured Operations).

with ops.control_dependencies([x]):

x = array_ops.identity(op_return_value)

elif not isinstance(x, tensor_array_ops.TensorArray):

try:

x = ops.convert_to_tensor_or_composite(x)

except (ValueError, TypeError):

raise TypeError(

"To be compatible with tf.contrib.eager.defun, Python functions "

"must return zero or more Tensors; in compilation of %s, found "

"return value of type %s, which is not a Tensor." %

(str(python_func), type(x)))

if add_control_dependencies:

x = a.mark_as_return(x)

return x

try:

if autograph:

from tensorflow.python import autograph # pylint: disable=g-import-not-at-top

_, original_func = tf_decorator.unwrap(python_func)

def wrapper(*args, **kwargs):

"""Calls a converted version of original_func."""

# TODO(mdan): Push this block higher in tf.function's call stack.

try:

return autograph.converted_call(

original_func,

autograph.ConversionOptions(

recursive=True,

optional_features=autograph_options,

user_requested=True,

), args, kwargs)

except Exception as e: # pylint:disable=broad-except

if hasattr(e, "ag_error_metadata"):

raise e.ag_error_metadata.to_exception(e)

else:

raise

# Wrapping around a decorator allows checks like tf_inspect.getargspec

# to be accurate.

converted_func = tf_decorator.make_decorator(original_func, wrapper)

python_func = tf_decorator.rewrap(python_func, original_func,

converted_func)

func_outputs = python_func(*func_args, **func_kwargs)

# invariant: `func_outputs` contains only Tensors, CompositeTensors,

# TensorArrays and `None`s.

func_outputs = nest.map_structure(convert, func_outputs,

expand_composites=True)

check_mutation(func_args_before, func_args)

check_mutation(func_kwargs_before, func_kwargs)

finally:

current_scope.set_use_resource(default_use_recource)

# Variables in `func_args`, `func_kwargs` should be explicit inputs

# to the function, not captured inputs.

graph_variables = list(func_graph._watched_variables) # pylint: disable=protected-access

arg_variables = object_identity.ObjectIdentitySet()

inputs = []

for arg in (nest.flatten(func_args, expand_composites=True) +

nest.flatten(func_kwargs, expand_composites=True)):

if isinstance(arg, resource_variable_ops.BaseResourceVariable):

# Even if an argument variable was not used in the function, we've

# already manually captured the resource Tensor when creating argument

# placeholders.

resource_placeholder = func_graph.pop_capture(arg.handle)

if resource_placeholder is None:

continue

arg_variables.add(arg)

inputs.append(resource_placeholder)

elif isinstance(arg, ops.Tensor):

inputs.append(arg)

variables = [v for v in graph_variables if v not in arg_variables]

func_graph.inputs = (

inputs + func_graph.internal_captures + nest.flatten(

func_graph.deferred_internal_captures, expand_composites=True))

func_graph.structured_outputs = func_outputs

# Returning a closed-over tensor does not trigger convert_to_tensor.

func_graph.outputs.extend(

func_graph.capture(x)

for x in flatten(func_graph.structured_outputs)

if x is not None)

func_graph.variables = variables

if add_control_dependencies:

func_graph.control_outputs.extend(control_manager.ops_which_must_run)

return func_graph

def maybe_captured(tensor):

"""If t is a captured value placeholder, returns the original captured value.

Args:

tensor: Tensor.

Returns:

A tensor, potentially from a different Graph/FuncGraph.

"""

if (not isinstance(tensor, ops.EagerTensor) and

tensor.op.graph.building_function and tensor.op.type == "Placeholder"):

for input_t, placeholder_t in tensor.op.graph.captures:

if tensor == placeholder_t:

return maybe_captured(input_t)

# pylint: enable=protected-access

return tensor

def device_stack_has_callable(device_stack):

"""Checks whether a device stack contains a callable."""

return any(callable(spec._device_name_or_function) # pylint: disable=protected-access

for spec in device_stack.peek_objs())

def check_mutation(n1, n2):

"""Check if two list of arguments are exactly the same."""

errmsg = ("Function to be traced should not modify structure of input "

"arguments. Check if your function has list and dictionary "

"operations that alter input arguments, "

"such as `list.pop`, `list.append`")

try:

nest.assert_same_structure(n1, n2, expand_composites=True)

except ValueError:

raise ValueError(errmsg)

for arg1, arg2 in zip(nest.flatten(n1, expand_composites=True),

nest.flatten(n2, expand_composites=True)):