tensorflow/python/autograph/core/ag_ctx.py

# Copyright 2019 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.
# ==============================================================================
"""Thread-local context managers for AutoGraph."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import enum
import inspect
import threading

from tensorflow.python.autograph.utils import ag_logging
from tensorflow.python.util.tf_export import tf_export


stacks = threading.local()


def _control_ctx():
  if not hasattr(stacks, 'control_status'):
    stacks.control_status = [_default_control_status_ctx()]
  return stacks.control_status


@tf_export('__internal__.autograph.control_status_ctx', v1=[])
def control_status_ctx():
  """Returns the current control context for autograph.

  This method is useful when calling `tf.__internal__.autograph.tf_convert`,
  The context will be used by tf_convert to determine whether it should convert
  the input function. See the sample usage like below:

  ```
  def foo(func):
    return tf.__internal__.autograph.tf_convert(
       input_fn, ctx=tf.__internal__.autograph.control_status_ctx())()
  ```

  Returns:
    The current control context of autograph.
  """
  ret = _control_ctx()[-1]
  return ret


class Status(enum.Enum):
  UNSPECIFIED = 0
  ENABLED = 1
  DISABLED = 2


class ControlStatusCtx(object):
  """A context that tracks whether autograph is enabled by the user."""

  def __init__(self, status, options=None):
    self.status = status
    self.options = options

  def __enter__(self):
    _control_ctx().append(self)
    return self

  def __repr__(self):
    return '{}[status={}, options={}]'.format(
        self.__class__.__name__, self.status, self.options)

  def __exit__(self, unused_type, unused_value, unused_traceback):
    assert _control_ctx()[-1] is self
    _control_ctx().pop()


class NullCtx(object):
  """Helper substitute for contextlib.nullcontext."""

  def __enter__(self):
    pass

  def __exit__(self, unused_type, unused_value, unused_traceback):
    pass


def _default_control_status_ctx():
  return ControlStatusCtx(status=Status.UNSPECIFIED)


INSPECT_SOURCE_SUPPORTED = True
try:
  inspect.getsource(ag_logging.log)
except OSError:
  INSPECT_SOURCE_SUPPORTED = False
  ag_logging.warning(
      'AutoGraph is not available in this environment: functions lack code'
      ' information. This is typical of some environments like the interactive'
      ' Python shell. See'
      ' https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/autograph/g3doc/reference/limitations.md#access-to-source-code'
      ' for more information.')