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# Copyright 2017 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.
# ==============================================================================
"""Decorator that provides a warning if the wrapped object is never used."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools
import types
import six # pylint: disable=unused-import
from tensorflow.python.eager import context
from tensorflow.python.util import tf_decorator
# pylint: enable=g-bad-import-order,g-import-not-at-top
# TODO(b/65412899): Re-implement to avoid leaking python objects.
# This function / class remains since the API is public (mark_used()).
def _add_should_use_warning(x, fatal_error=False):
"""Wraps object x so that if it is never used, a warning is logged.
Does nothing when executing eagerly.
Args:
x: Python object.
fatal_error: Python bool. If `True`, tf.logging.fatal is raised
if the returned value is never used.
Returns:
An instance of `TFShouldUseWarningWrapper` which subclasses `type(x)`
and is a very shallow wrapper for `x` which logs access into `x`.
"""
del fatal_error
if x is None: # special corner case where x is None
return x
if context.in_eager_mode():
# Typically not needed when executing eagerly (the main use case is for ops
# which need to be incorporated into the graph), and even the no-op wrapper
# creates reference cycles which require garbage collection.
return x
def override_method(method):
def fn(self, *args, **kwargs):
return method(self, *args, **kwargs)
return fn
class TFShouldUseWarningWrapper(type(x)):
"""Wrapper for objects that keeps track of their use."""
def __init__(self, true_self):
self.__dict__ = true_self.__dict__
# Not sure why this pylint warning is being used; this is not an
# old class form.
# pylint: disable=super-on-old-class
def __getattribute__(self, name):
return super(TFShouldUseWarningWrapper, self).__getattribute__(name)
def mark_used(self, *args, **kwargs):
return
# pylint: enable=super-on-old-class
for name in dir(TFShouldUseWarningWrapper):
method = getattr(TFShouldUseWarningWrapper, name)
if not isinstance(method, types.FunctionType):
continue
if name in ('__init__', '__getattribute__', '__del__', 'mark_used'):
continue
setattr(TFShouldUseWarningWrapper, name,
functools.wraps(method)(override_method(method)))
wrapped = TFShouldUseWarningWrapper(x)
wrapped.__doc__ = x.__doc__ # functools.wraps fails on some objects.
return wrapped
def should_use_result(fn):
"""Function wrapper that ensures the function's output is used.
If the output is not used, a `tf.logging.error` is logged.
An output is marked as used if any of its attributes are read, modified, or
updated. Examples when the output is a `Tensor` include:
- Using it in any capacity (e.g. `y = t + 0`, `sess.run(t)`)
- Accessing a property (e.g. getting `t.name` or `t.op`).
Note, certain behaviors cannot be tracked - for these the object may not
be marked as used. Examples include:
- `t != 0`. In this case, comparison is done on types / ids.
- `isinstance(t, tf.Tensor)`. Similar to above.
Does nothing when executing eagerly.
Args:
fn: The function to wrap.
Returns:
The wrapped function.
"""
def wrapped(*args, **kwargs):
return _add_should_use_warning(fn(*args, **kwargs))
return tf_decorator.make_decorator(
fn, wrapped, 'should_use_result',
((fn.__doc__ or '') +
('\n\n '
'**NOTE** The output of this function should be used. If it is not, '
'a warning will be logged. To mark the output as used, '
'call its .mark_used() method.')))
def must_use_result_or_fatal(fn):
"""Function wrapper that ensures the function's output is used.
If the output is not used, a `tf.logging.fatal` error is raised.
An output is marked as used if any of its attributes are read, modified, or
updated. Examples when the output is a `Tensor` include:
- Using it in any capacity (e.g. `y = t + 0`, `sess.run(t)`)
- Accessing a property (e.g. getting `t.name` or `t.op`).
Note, certain behaviors cannot be tracked - for these the object may not
be marked as used. Examples include:
- `t != 0`. In this case, comparison is done on types / ids.
- `isinstance(t, tf.Tensor)`. Similar to above.
Does nothing when executing eagerly.
Args:
fn: The function to wrap.
Returns:
The wrapped function.
"""
def wrapped(*args, **kwargs):
return _add_should_use_warning(fn(*args, **kwargs), fatal_error=True)
return tf_decorator.make_decorator(
fn, wrapped, 'must_use_result_or_fatal',
((fn.__doc__ or '') +
('\n\n '
'**NOTE** The output of this function must be used. If it is not, '
'a fatal error will be raised. To mark the output as used, '
'call its .mark_used() method.')))
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