# 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

import weakref

from tensorflow.core.framework import attr_value_pb2

from tensorflow.python.eager import context

from tensorflow.python.eager import tape

from tensorflow.python.eager.graph_only_ops import graph_placeholder

from tensorflow.python.framework import ops

from tensorflow.python.framework import tensor_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 variable_scope

from tensorflow.python.util import compat

from tensorflow.python.util import nest

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")

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

]

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.

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.

seed: The graph-level random seed.

"""

def __init__(self, name, read_only_collections=True):

"""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.

read_only_collections: whether to not write function graph collections

back to default graph. Defaults to True.

"""

super(FuncGraph, self).__init__()

self.name = name

self.inputs = []

self.outputs = []

self.structured_outputs = None

self._read_only_collections = read_only_collections

self._weak_variables = []

self.outer_graph = ops.get_default_graph()

self.captures = collections.OrderedDict()

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

# 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.

self._distribution_strategy_stack = 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.

if context.executing_eagerly():

self.seed = context.global_seed()

self._xla_compile = (context.context().device_spec.device_type == "TPU")

if self._distribution_strategy_stack or self._xla_compile:

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

else:

self.seed = graph.seed

self._xla_compile = getattr(graph, "_xla_compile", 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()

if (self._distribution_strategy_stack

or self._xla_compile

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()

if not self._read_only_collections:

self._collections = graph._collections

else:

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)

self._variable_creator_stack = graph._variable_creator_stack

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

# optimizers.

self._graph_key = graph._graph_key

# pylint: enable=protected-access

@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 create_op(

self,

op_type,

inputs,

dtypes,

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: 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.

"""

# 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(

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

compute_device=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.

"""

if isinstance(tensor, ops.EagerTensor):

if name is None:

name = str(ops.uid())

return self._capture_helper(tensor, name)

if tensor.graph is not self:

if name is None:

name = tensor.op.name

return self._capture_helper(tensor, name)

return tensor

def _capture_helper(self, tensor, name):

captured_tensor = self.captures.get(tensor, None)

if captured_tensor is None:

captured_tensor = _create_substitute_placeholder(tensor, name=name,

dtype=tensor.dtype)

self.captures[tensor] = captured_tensor

self.inputs.append(captured_tensor)

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

lambda x: [x])

return captured_tensor

@property

def external_captures(self):

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

return list(self.captures.keys())

@property

def internal_captures(self):

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

return list(self.captures.values())

def func_graph_from_py_func(name,

python_func,

args,

kwargs,

signature=None,

func_graph=None,

experimental_autograph=False,

add_control_dependencies=True,

arg_names=None,

op_return_value=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.

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

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.

Returns:

A FuncGraph.

Raises:

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

`Tensor`.

"""

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)

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:

args = signature

kwargs = {}

func_args = _get_defun_inputs_from_args(args, arg_names)

func_kwargs = _get_defun_inputs_from_kwargs(kwargs)

# 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, nest.flatten(func_args))

func_kwargs_before = nest.pack_sequence_as(

func_kwargs, nest.flatten(func_kwargs))

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)

else:

try:

x = ops.convert_to_tensor_or_indexed_slices(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

this_tape = tape.push_new_tape()

try:

if experimental_autograph:

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

func_outputs = autograph.converted_call(

python_func, None,

autograph.ConversionOptions(

verbose=True,

recursive=True,

strip_decorators=(function.defun,),

optional_features=(),

), *func_args, **func_kwargs)

else:

func_outputs = python_func(*func_args, **func_kwargs)

# invariant: `func_outputs` contains only Tensors and `None`s.

func_outputs = nest.map_structure(convert, func_outputs)

check_mutation(func_args_before, func_args)

check_mutation(func_kwargs_before, func_kwargs)

finally:

tape.pop_tape(this_tape)

current_scope.set_use_resource(default_use_recource)

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

# to the function, not captured inputs.

tape_variables = this_tape.watched_variables()

arg_variables = set()

inputs = []

for arg in nest.flatten(func_args) + nest.flatten(func_kwargs):

if isinstance(arg, resource_variable_ops.ResourceVariable):

try:

resource_placeholder = func_graph.captures.pop(arg.handle)

arg_variables.add(arg)

except KeyError:

# This case occurs if a Variable among the inputs is not actually

# used by the function; we still add an explicit input for it

# because the user should presumably pass the Variable as an input

# to the corresponding graph function.

resource_placeholder = _create_substitute_placeholder(arg.handle)

inputs.append(resource_placeholder)

elif isinstance(arg, ops.Tensor):

inputs.append(arg)

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

func_graph.inputs = inputs + list(func_graph.captures.values())

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

# Register any other functions defined in the graph.

with ops.init_scope():

if context.executing_eagerly():

for f in func_graph._functions.values(): # pylint: disable=protected-access

# TODO(ashankar): What about the gradient registry?

context.add_function(f._c_func.func) # pylint: disable=protected-access

return func_graph

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)

except ValueError:

raise ValueError(errmsg)

for arg1, arg2 in zip(nest.flatten(n1), nest.flatten(n2)):

if arg1 is not arg2:

raise ValueError(errmsg)

def flatten(sequence):

"""A wrapper around `nest.flatten` that also unpacks `IndexedSlices`."""

# TODO(akshayka): Support `SparseTensor` in a similar fashion.

flat_sequence = nest.flatten(sequence)

outputs = []

for item in flat_sequence:

if isinstance(item, ops.IndexedSlices):

if item.dense_shape is not None:

outputs.extend([item.values, item.indices, item.dense_shape])

else:

outputs.extend([item.values, item.indices])

else:

outputs.append(item)

return outputs

def _create_substitute_placeholder(value, name=None, dtype=None):

"""Creates a placeholder for `value` and propagates shape info to it."""

# Note: setting ops.control_dependencies(None) ensures we always put

# capturing placeholders outside of any control flow context.

with ops.control_dependencies(None):

placeholder = graph_placeholder(

dtype=dtype or value.dtype, shape=value.shape, name=name)

custom_gradient.copy_handle_data(value, placeholder)

return placeholder

def _get_defun_inputs_from_args(args, names):

"""Maps Python function positional args to graph-construction inputs."""

return _get_defun_inputs(args, names, structure=args)

def _get_defun_inputs(flat_args, names, structure):

"""Maps python function args to graph-construction inputs.

Args:

flat_args: A flat list of user-specified arguments.

names: A list of strings with user-specified argument names, same length as

`flat_args`. May be `None`, in which case a generic name is used.

structure: The original argument list or dictionary.

Returns:

Placeholders with the same structure as `structure`.

"""

function_inputs = []

if names is None:

names = [None] * len(flat_args)

for arg_value, name in zip(flat_args, names):

for arg in nest.flatten(arg_value):

if isinstance(arg, (ops.Tensor, tensor_spec.TensorSpec)):

if isinstance(arg, tensor_spec.TensorSpec) and arg.name:

requested_name = arg.name

else:

requested_name = name

placeholder = graph_placeholder(

arg.dtype, arg.shape,

name=requested_name)

if name is not None:

# Record the requested/user-specified name in case it's different than

# the uniquified name, for validation when exporting signatures.

placeholder.op._set_attr( # pylint: disable=protected-access

"_user_specified_name",

attr_value_pb2.AttrValue(s=compat.as_bytes(requested_name)))

function_inputs.append(placeholder)

else:

function_inputs.append(arg)

return nest.pack_sequence_as(structure, function_inputs)

def _get_defun_inputs_from_kwargs(kwargs):

"""Maps Python function keyword args to graph-construction inputs."""

if kwargs:

names, flat_args = zip(*sorted(kwargs.items()))

else:

names = []

flat_args = []

return _get_defun_inputs(flat_args, names, structure=kwargs)