Consolidate the device function and device string handling in `tf.device()`.

The effect of this CL is to treat `with tf.device(device_name):` as
supplying a device function that *merges* `device_name` into the
device of ops created in that scope. (Merging is defined by
`tensorflow.python.framework.device.merge_device()`: essentially, for
each field defined in `device_name`, the merge function sets an op's
device to that if it has not already been set.) This makes it possible
to compose device blocks that set different parts of a device, and use
device strings in composition with device functions.

A secondary effect of this CL is that it causes `with
tf.device(None):` to interoperate properly with device functions. As
with other `tf.Graph` contexts, entering a `with tf.device(None):` now
has the effect of ignoring all currently set device functions in the
outer context.

This CL makes some breaking changes to corner cases in the
`tf.device()`, `tf.Graph`, `tf.Operation`, and `tf.Tensor` APIs:

* Within a `with tf.device(device_string):` scope, the given device
  string will now be *merged* into the device for ops created in that
  scope. See the implementation of
  `tensorflow.python.framework.device.merge_device()` for details.

  Previously, device strings were maintained in a single "default
  device" field, rather than a stack, so device strings from outer
  contexts would be completely ignored. To obtain the previous
  behavior, use `with tf.device(None), tf.device(device_string):`
  instead.

* Within a `with tf.Graph.device(None):` scope, no device functions
  from the outer context will be executed.

  Previously, the `None` applied only to the device string, and all
  device functions would be applied unconditionally.

* The `tf.Graph.get_default_device()` method is removed, because it no
  longer has a well-defined meaning. To create a no-op device scope,
  you can simply use `with tf.device(""):`.

* The `tf.Operation.device` and `tf.Tensor.device` properties now
  return an empty string when no device has been set for an op. This
  makes it easier to write code like `with tf.device(op.device):`,
  which is robust to `op` having or not having a device (in which case
  the scope should be a no-op).
Change: 114003979

1 files changed, 4 insertions, 4 deletions

diff --git a/tensorflow/python/kernel_tests/init_ops_test.py b/tensorflow/python/kernel_tests/init_ops_test.py
index 145058579e..28aa3eccc3 100644
--- a/tensorflow/python/kernel_tests/init_ops_test.py
+++ b/tensorflow/python/kernel_tests/init_ops_test.py
@@ -288,15 +288,15 @@ class DeviceTest(tf.test.TestCase):
   def testNoDevice(self):
     with tf.Graph().as_default():
       var = tf.Variable([[1.0, 1.0]])
-    self.assertEqual(None, var.device)
-    self.assertEqual(None, var.initializer.device)
+    self.assertDeviceEqual(None, var.device)
+    self.assertDeviceEqual(None, var.initializer.device)
 
   def testDevice(self):
     with tf.Graph().as_default():
       with tf.device("/job:ps"):
         var = tf.Variable([[1.0, 1.0]])
-    self.assertEqual("/job:ps", var.device)
-    self.assertEqual("/job:ps", var.initializer.device)
+    self.assertDeviceEqual("/job:ps", var.device)
+    self.assertDeviceEqual("/job:ps", var.initializer.device)
 
 
 if __name__ == "__main__":