Install _distributed_container only at variable creation

With a following change we're going to create new DistributedVariable as return
value of assign*() and scatter*(). Installing _distributed_container multiple
times will be messy.

PiperOrigin-RevId: 307753201
Change-Id: I3c87abc301ea32b0169034324a108d6967229889

tensorflow/python/distribute/values.py

@@ -433,10 +433,6 @@ def __init__(self, strategy, values, aggregation):
     self._aggregation = aggregation
     super(DistributedVariable, self).__init__(values)
     self._common_name = self._primary.name.split(":")[0]
-    # Use a weakref to make it easy to map from the contained values
-    # to the container without introducing a reference cycle.
-    for v in values:
-      v._distributed_container = weakref.ref(self)  # pylint: disable=protected-access
     # tf.keras keeps track of variables initialized using this attribute. When
     # tf.keras gets the default session, it initializes all uninitialized vars.
     # We need to make _keras_initialized a member of DistributedVariable because
@@ -774,6 +770,13 @@ def create_mirrored_variable(  # pylint: disable=missing-docstring
     value_list = real_mirrored_creator(**kwargs)
     var_cls = sync_on_read_cls if is_sync_on_read else mirrored_cls
     result = var_cls(strategy, value_list, aggregation)
+    # Install the created DistributedVariable as _distributed_container property
+    # of the underlying variables, to make it easy to map back to the container.
+    for v in result.values:
+      # Hold a strong reference to avoid the container from being GC-ed. After
+      # v = v.assign(), the user code may no longer holds references to the
+      # original container, since v.assign() returns a new DistributedVariable.
+      v._distributed_container = result  # pylint: disable=protected-access
 
   # Add the wrapped variable to the requested collections.
   # The handling of eager mode and the global step matches
@@ -1240,10 +1243,10 @@ def regroup(values, wrap_class=PerReplica, always_wrap=False):
     # pylint: disable=protected-access
     assert not isinstance(v0, MirroredVariable), (
         "ids = %s, values = %s" % ([id(v) for v in values], values))
-    distributed_container = v0._distributed_container()
+    distributed_container = v0._distributed_container
     assert distributed_container is not None
     for v in values[1:]:
-      assert distributed_container is v._distributed_container()
+      assert distributed_container is v._distributed_container
     return distributed_container
   # pylint: enable=protected-access
 
@@ -1331,7 +1334,7 @@ def value_container(val):
       # DistributedVariable has _distributed_container defined
       # but we don't want to return it.
       not isinstance(val, DistributedVariable)):
-    container = val._distributed_container()  # pylint: disable=protected-access
+    container = val._distributed_container  # pylint: disable=protected-access
     if container is not None:
       return container
   return val

tensorflow/python/distribute/values_test.py

@@ -365,7 +365,7 @@ def _make_mirrored():
   return mirrored
 
 
-class RegroupAndSelectDeviceTest(test.TestCase):
+class RegroupAndSelectDeviceTest(test.TestCase, parameterized.TestCase):
 
   def _is_per_replica(self, result, expected, klass=values.PerReplica):
     self.assertIsInstance(result, klass)
@@ -448,12 +448,20 @@ def testWrapAListOfTwoTuples(self):
     self._is_per_replica(result[0], ("1", "3"), values.PerReplica)
     self._is_per_replica(result[1], ("2", "4"), values.PerReplica)
 
-  def testMirroredContainer(self):
-    if context.num_gpus() < 1 and context.executing_eagerly():
-      self.skipTest("A GPU is not available for this test in eager mode.")
-    mirrored = _make_mirrored()
-    result = values.regroup(mirrored.values)
-    self.assertIs(mirrored, result)
+  @combinations.generate(
+      combinations.combine(
+          distribution=[
+              strategy_combinations.mirrored_strategy_with_gpu_and_cpu,
+              strategy_combinations.mirrored_strategy_with_one_cpu,
+          ],
+          mode=["graph", "eager"],
+      ))
+  def testMirroredContainer(self, distribution):
+    with distribution.scope():
+      v = variable_scope.variable(
+          1., aggregation=variable_scope.VariableAggregation.SUM)
+    self.assertTrue(values.is_distributed_variable(v))
+    self.assertTrue(values.is_distributed_variable(values.regroup(v.values)))
 
   def testSameId(self):
     foo = object()
@@ -479,18 +487,7 @@ def testOneDevice(self):
     result = values.regroup((_nested_value("1"),))
     # On one device regroup() and select_replica() are basically identity.
     self.assertEqual(_nested_value("1"), result)
-    self.assertEqual(_nested_value("1"),
-                     values.select_replica(0, result))
-
-    # The one exception has to do with MirroredVariables.
-    d = "/device:CPU:0"
-    with ops.device(d):
-      v = variable_scope.get_variable(
-          name="v", initializer=1., use_resource=True)
-    mirrored = values.MirroredVariable(None, (v,),
-                                       variable_scope.VariableAggregation.SUM)
-    result = values.regroup((v,))
-    self.assertIs(mirrored, result)
+    self.assertEqual(_nested_value("1"), values.select_replica(0, result))
 
   def testNamedTuple(self):
 

tensorflow/python/keras/optimizer_v2/optimizer_v2.py

@@ -1274,7 +1274,7 @@ def _var_key(var):
   # pylint: disable=protected-access
   # Get the distributed variable if it exists.
   if hasattr(var, "_distributed_container"):
-    var = var._distributed_container()
+    var = var._distributed_container
   if var._in_graph_mode:
     return var._shared_name
   return var._unique_id

tensorflow/python/training/optimizer.py

@@ -759,7 +759,7 @@ def get_slot(self, var, name):
     if hasattr(var, "_distributed_container"):
       # NOTE: If this isn't patched, then there is no `handle` in
       # `_resource_apply_dense`.
-      distributed_container = var._distributed_container()
+      distributed_container = var._distributed_container
       assert distributed_container is not None
       if ops.executing_eagerly_outside_functions():
         key = distributed_container._unique_id