138 lines
4.2 KiB
Python
138 lines
4.2 KiB
Python
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# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Classes used to handle thread-local stacks."""
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from collections.abc import Iterator
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import threading
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from typing import Generic, Optional, TypeVar
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from tensorflow.python.util import tf_contextlib
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from tensorflow.python.util.tf_export import tf_export
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T = TypeVar("T")
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class DefaultStack(threading.local, Generic[T]):
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"""A thread-local stack of objects for providing implicit defaults."""
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def __init__(self):
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super().__init__()
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self._enforce_nesting = True
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self.stack: list[T] = []
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def get_default(self) -> Optional[T]:
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return self.stack[-1] if self.stack else None
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def reset(self) -> None:
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self.stack = []
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def is_cleared(self) -> bool:
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return not self.stack
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@property
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def enforce_nesting(self) -> bool:
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return self._enforce_nesting
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@enforce_nesting.setter
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def enforce_nesting(self, value: bool):
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self._enforce_nesting = value
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@tf_contextlib.contextmanager
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def get_controller(self, default: T) -> Iterator[T]:
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"""A context manager for manipulating a default stack."""
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self.stack.append(default)
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try:
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yield default
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finally:
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# stack may be empty if reset() was called
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if self.stack:
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if self._enforce_nesting:
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if self.stack[-1] is not default:
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raise AssertionError(
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"Nesting violated for default stack of %s objects" %
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type(default))
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self.stack.pop()
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else:
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self.stack.remove(default)
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_default_session_stack = DefaultStack()
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def default_session(session):
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"""Python "with" handler for defining a default session.
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This function provides a means of registering a session for handling
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Tensor.eval() and Operation.run() calls. It is primarily intended for use
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by session.Session, but can be used with any object that implements
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the Session.run() interface.
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Use with the "with" keyword to specify that Tensor.eval() and Operation.run()
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invocations within the scope of a block should be executed by a particular
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session.
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The default session applies to the current thread only, so it is always
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possible to inspect the call stack and determine the scope of a default
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session. If you create a new thread, and wish to use the default session
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in that thread, you must explicitly add a "with ops.default_session(sess):"
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block in that thread's function.
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Example:
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The following code examples are equivalent:
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# 1. Using the Session object directly:
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sess = ...
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c = tf.constant(5.0)
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sess.run(c)
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# 2. Using default_session():
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sess = ...
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with ops.default_session(sess):
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c = tf.constant(5.0)
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result = c.eval()
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# 3. Overriding default_session():
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sess = ...
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with ops.default_session(sess):
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c = tf.constant(5.0)
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with ops.default_session(...):
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c.eval(session=sess)
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Args:
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session: The session to be installed as the default session.
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Returns:
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A context manager for the default session.
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"""
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return _default_session_stack.get_controller(session)
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@tf_export(v1=["get_default_session"])
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def get_default_session():
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"""Returns the default session for the current thread.
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The returned `Session` will be the innermost session on which a
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`Session` or `Session.as_default()` context has been entered.
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NOTE: The default session is a property of the current thread. If you
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create a new thread, and wish to use the default session in that
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thread, you must explicitly add a `with sess.as_default():` in that
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thread's function.
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Returns:
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The default `Session` being used in the current thread.
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"""
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return _default_session_stack.get_default()
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