Automated rollback of commit 19ac5f4f6c

PiperOrigin-RevId: 295802414
Change-Id: I344ec4bb8a0a2cb9921f2f36fa86da9c7f2b55e3

tensorflow/python/distribute/cross_device_ops.py

@@ -1032,7 +1032,7 @@ class CollectiveAllReduce(CrossDeviceOps):
           else:
             # TODO(josh11b): Once we add support for model parallelism, get the
             # copy from the corresponding replica instead of the primary.
-            index.append(array_ops.identity(all_reduced._primary))  # pylint: disable=protected-access
+            index.append(array_ops.identity(all_reduced.primary))
     return value_lib.regroup(index, wrap_class=value_lib.Mirrored)
 
   def batch_reduce_implementation(self, reduce_op, value_destination_pairs):

tensorflow/python/distribute/mirrored_strategy_test.py

@@ -1334,7 +1334,7 @@ class FunctionTest(test.TestCase):
     def forward(x, w, b):
       return x * w + b
     x = constant_op.constant([1.0], name="x_useless")
-    concrete_forward = forward.get_concrete_function(x, w._primary, b._primary)
+    concrete_forward = forward.get_concrete_function(x, w.primary, b.primary)
 
     with ms.scope():
       def replica_fn():
@@ -1350,8 +1350,8 @@ class FunctionTest(test.TestCase):
       g1, g2 = step_fn()
       run_metadata = context.export_run_metadata()
       context.disable_run_metadata()
-      self.assertEqual(self.evaluate(g1._primary), 1.0)
+      self.assertEqual(self.evaluate(g1.primary), 1.0)
-      self.assertEqual(self.evaluate(g2._primary), 1.0)
+      self.assertEqual(self.evaluate(g2.primary), 1.0)
 
       # Verify that this node runs on both devices.
       node_name = "gradients_mul_grad_mul_1_x"

tensorflow/python/distribute/parameter_server_strategy.py

@@ -487,7 +487,7 @@ class ParameterServerStrategyExtended(distribute_lib.StrategyExtendedV1):
     def _select_fn(x):  # pylint: disable=g-missing-docstring
       if isinstance(x, values.Mirrored):
         if len(x.devices) == 1:
-          return x._primary  # pylint: disable=protected-access
+          return x.primary
         else:
           raise ValueError(
               "You cannot update variable with a Mirrored object with multiple "

tensorflow/python/distribute/values.py

@@ -75,7 +75,7 @@ class DistributedValues(object):
         "replica accesses.")
 
   def _get_closest(self):
-    """Returns value in same replica or device if possible, else the _primary."""
+    """Returns value in same replica or device if possible, else the primary."""
     replica_id = _get_current_replica_id_as_int()
     if replica_id is None:
       # Try to find a value on the current device.
@@ -83,12 +83,12 @@ class DistributedValues(object):
       for value in self._values:
         if device_util.canonicalize(value.device) == current_device:
           return value
-      return self._primary
+      return self.primary
     else:
       return self._values[replica_id]
 
   @property
-  def _primary(self):
+  def primary(self):
     """Returns a representative component."""
     return self._values[0]
 
@@ -368,7 +368,7 @@ class DistributedVariable(DistributedDelegate, variables_lib.Variable):
   def __init__(self, strategy, values):
     self._distribute_strategy = strategy
     super(DistributedVariable, self).__init__(values)
-    self._common_name = self._primary.name.split(":")[0]
+    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:
@@ -395,7 +395,7 @@ class DistributedVariable(DistributedDelegate, variables_lib.Variable):
       The op that evaluates to True or False depending on if all the
       component variables are initialized.
     """
-    result = self._primary.is_initialized()
+    result = self.primary.is_initialized()
     # We iterate through the list of values except the last one to allow us to
     # name the final `logical_and` op the same name that is passed by the user
     # to the `is_initialized` op. For distributed variables, the
@@ -426,11 +426,11 @@ class DistributedVariable(DistributedDelegate, variables_lib.Variable):
 
   @property
   def constraint(self):
-    return self._primary.constraint
+    return self.primary.constraint
 
   @property
   def graph(self):
-    return self._primary.graph
+    return self.primary.graph
 
   @property
   def _shared_name(self):
@@ -438,28 +438,28 @@ class DistributedVariable(DistributedDelegate, variables_lib.Variable):
 
   @property
   def _unique_id(self):
-    return self._primary._unique_id  # pylint: disable=protected-access
+    return self.primary._unique_id  # pylint: disable=protected-access
 
   @property
   def _graph_key(self):
     """Lets Optimizers know which graph this variable is from."""
-    return self._primary._graph_key  # pylint: disable=protected-access
+    return self.primary._graph_key  # pylint: disable=protected-access
 
   @property
   def name(self):
-    return self._primary.name
+    return self.primary.name
 
   @property
   def dtype(self):
-    return self._primary.dtype
+    return self.primary.dtype
 
   @property
   def shape(self):
-    return self._primary.shape
+    return self.primary.shape
 
   @property
   def synchronization(self):
-    return self._primary.synchronization
+    return self.primary.synchronization
 
   @property
   def handle(self):
@@ -475,10 +475,10 @@ class DistributedVariable(DistributedDelegate, variables_lib.Variable):
 
   @property
   def _save_slice_info(self):
-    return self._primary._save_slice_info  # pylint: disable=protected-access
+    return self.primary._save_slice_info  # pylint: disable=protected-access
 
   def _get_save_slice_info(self):
-    return self._primary._get_save_slice_info()  # pylint: disable=protected-access
+    return self.primary._get_save_slice_info()  # pylint: disable=protected-access
 
   def _set_save_slice_info(self, save_slice_info):
     for v in self._values:
@@ -490,17 +490,17 @@ class DistributedVariable(DistributedDelegate, variables_lib.Variable):
 
   @property
   def trainable(self):
-    return self._primary.trainable
+    return self.primary.trainable
 
   @property
   def distribute_strategy(self):
     return self._distribute_strategy
 
   def get_shape(self):
-    return self._primary.get_shape()
+    return self.primary.get_shape()
 
   def to_proto(self, export_scope=None):
-    return self._primary.to_proto(export_scope=export_scope)
+    return self.primary.to_proto(export_scope=export_scope)
 
   @property
   def op(self):
@@ -508,13 +508,13 @@ class DistributedVariable(DistributedDelegate, variables_lib.Variable):
     # to work (even if the current device isn't in self.devices), but
     # other uses of var.op in a cross-replica context to fail.
     if distribution_strategy_context.in_cross_replica_context():
-      return DistributedVarOp(self._primary.op.name, self._primary.op.graph,
+      return DistributedVarOp(self.primary.op.name, self.primary.op.graph,
-                              self._primary.op.traceback, self._primary.op.type)
+                              self.primary.op.traceback, self.primary.op.type)
     return self._get().op
 
   @property
   def _in_graph_mode(self):
-    return self._primary._in_graph_mode  # pylint: disable=protected-access
+    return self.primary._in_graph_mode  # pylint: disable=protected-access
 
   def read_value(self):
     with _enter_or_assert_strategy(self._distribute_strategy):
@@ -567,7 +567,7 @@ class TPUVariableMixin(object):
     # Handle ID is needed for `get_replicated_var_handle` to cache the variables
     # correctly since in eager mode different variables can have the same name.
     if ops.executing_eagerly_outside_functions():
-      self._handle_id = self._common_name + "_" + str(id(self._primary))
+      self._handle_id = self._common_name + "_" + str(id(self.primary))
     else:
       self._handle_id = self._common_name
 
@@ -592,7 +592,7 @@ class TPUVariableMixin(object):
     if _enclosing_tpu_context() is None:
       return super(TPUVariableMixin, self)._get_closest()
     else:
-      return self._primary
+      return self.primary
 
   def numpy(self):
     if context.executing_eagerly():
@@ -644,8 +644,8 @@ class TPUVariableMixin(object):
 
   @property
   def op(self):
-    return DistributedVarOp(self._primary.op.name, self._primary.op.graph,
+    return DistributedVarOp(self.primary.op.name, self.primary.op.graph,
-                            self._primary.op.traceback, self._primary.op.type)
+                            self.primary.op.traceback, self.primary.op.type)
 
   def _dense_var_to_tensor(self, dtype=None, name=None, as_ref=False):
     """Converts a variable to a tensor."""
@@ -900,7 +900,7 @@ class MirroredVariable(DistributedVariable, Mirrored):
     """
 
     def _saveable_factory(name=self._common_name):
-      return _MirroredSaveable(self, self._primary, name)
+      return _MirroredSaveable(self, self.primary, name)
 
     return {trackable.VARIABLE_VALUE_KEY: _saveable_factory}
 
@@ -1003,8 +1003,7 @@ class _SyncOnReadSaveable(saver.BaseSaverBuilder.SaveableObject):
         slice_spec="",
         name=name,
         dtype=sync_on_read_variable.dtype,
-        device=sync_on_read_variable._primary.device)  # pylint: disable=protected-access
+        device=sync_on_read_variable.primary.device)
-
     super(_SyncOnReadSaveable, self).__init__(tensor, [spec], name)
 
   def restore(self, restored_tensors, restored_shapes):
@@ -1104,7 +1103,7 @@ class SyncOnReadVariable(DistributedVariable):
 
   def _get_cross_replica(self):
     if self._aggregation == vs.VariableAggregation.ONLY_FIRST_REPLICA:
-      return self._primary
+      return self.primary
 
     with _enter_or_assert_strategy(self._distribute_strategy):
       return self._distribute_strategy.reduce(

tensorflow/python/saved_model/save.py

@@ -274,7 +274,7 @@ class _SaveableView(object):
         self.captured_tensor_node_ids[obj.resource_handle] = node_id
       elif (ds_values.is_distributed_variable(obj) or
             resource_variable_ops.is_resource_variable(obj)):
-        obj_to_copy = obj._primary if ds_values.is_distributed_variable(  # pylint: disable=protected-access
+        obj_to_copy = obj.primary if ds_values.is_distributed_variable(
             obj) else obj
         new_variable = resource_variable_ops.copy_to_graph_uninitialized(
             obj_to_copy)