Clean up the ResourceVariable inheritance hierarchy a bit.

PiperOrigin-RevId: 253592362

tensorflow/contrib/eager/python/parameter_server.py

@@ -63,7 +63,7 @@ def _eager_safe_variable_handle(shape, dtype, shared_name, name, graph_mode):
   return handle
 
 
-class SharedVariable(resource_variable_ops.ResourceVariable):
+class SharedVariable(resource_variable_ops.BaseResourceVariable):
   """Experimental Variable designed for parameter server training.
 
   A SharedVariable has a name and two instances of SharedVariable with the
@@ -231,7 +231,7 @@ class SharedVariable(resource_variable_ops.ResourceVariable):
           self._graph_element = None
           self._cached_value = None
 
-    self._handle_deleter = None
+    self._handle_deleter = object()
     self._cached_shape_as_list = None
 
 

tensorflow/python/distribute/cross_device_ops.py

@@ -48,7 +48,7 @@ def check_destinations(destinations):
     Boolean which is True if `destinations` is not empty.
   """
   # Calling bool() on a ResourceVariable is not allowed.
-  if isinstance(destinations, resource_variable_ops.ResourceVariable):
+  if isinstance(destinations, resource_variable_ops.BaseResourceVariable):
     return bool(destinations.device)
   return bool(destinations)
 
@@ -56,7 +56,7 @@ def check_destinations(destinations):
 def validate_destinations(destinations):
   if not isinstance(destinations,
                     (value_lib.DistributedValues,
-                     resource_variable_ops.ResourceVariable,
+                     resource_variable_ops.BaseResourceVariable,
                      value_lib.AggregatingVariable,
                      six.string_types,
                      value_lib.TPUMirroredVariable,

tensorflow/python/distribute/parameter_server_strategy.py

@@ -479,7 +479,7 @@ class ParameterServerStrategyExtended(distribute_lib.StrategyExtendedV1):
   def _update(self, var, fn, args, kwargs, group):
     if isinstance(var, values.AggregatingVariable):
       var = var.get()
-    if not isinstance(var, resource_variable_ops.ResourceVariable):
+    if not isinstance(var, resource_variable_ops.BaseResourceVariable):
       raise ValueError(
           "You can not update `var` %r. It must be a Variable." % var)
     with ops.colocate_with(var), distribute_lib.UpdateContext(var.device):

tensorflow/python/distribute/tpu_strategy.py

@@ -519,7 +519,7 @@ class TPUExtended(distribute_lib.StrategyExtendedV1):
 
   def _update(self, var, fn, args, kwargs, group):
     assert isinstance(var, values.TPUMirroredVariable) or isinstance(
-        var, resource_variable_ops.ResourceVariable)
+        var, resource_variable_ops.BaseResourceVariable)
     if values._enclosing_tpu_context() is not None:  # pylint: disable=protected-access
       if group:
         return fn(var, *args, **kwargs)
@@ -540,7 +540,7 @@ class TPUExtended(distribute_lib.StrategyExtendedV1):
 
   def read_var(self, var):
     assert isinstance(var, values.TPUMirroredVariable) or isinstance(
-        var, resource_variable_ops.ResourceVariable)
+        var, resource_variable_ops.BaseResourceVariable)
     return var.read_value()
 
   def _local_results(self, val):

tensorflow/python/eager/def_function.py

@@ -111,6 +111,8 @@ class UnliftedInitializerVariable(resource_variable_ops.UninitializedVariable):
         shape and `validate_shape` is `True`.
       RuntimeError: If called outside of a function definition.
     """
+    with ops.init_scope():
+      self._in_graph_mode = not context.executing_eagerly()
     if not ops.inside_function():
       # If we've been init_scope()d out of the function definition nothing to do
       # here; we can't really do the capturing or conditional logic.

tensorflow/python/eager/function.py

@@ -598,7 +598,7 @@ class ConcreteFunction(object):
     return self._call_flat(
         (t for t in nest.flatten((args, kwargs), expand_composites=True)
          if isinstance(t, (ops.Tensor,
-                           resource_variable_ops.ResourceVariable))),
+                           resource_variable_ops.BaseResourceVariable))),
         self.captured_inputs)
 
   def _call_flat(self, args, captured_inputs):
@@ -632,7 +632,7 @@ class ConcreteFunction(object):
     tensor_inputs = []
     variables_used = set([])
     for i, arg in enumerate(args):
-      if isinstance(arg, resource_variable_ops.ResourceVariable):
+      if isinstance(arg, resource_variable_ops.BaseResourceVariable):
         # We can pass a variable more than once, and in this case we need to
         # pass its handle only once.
         if arg.handle in variables_used:

tensorflow/python/eager/wrap_function.py

@@ -156,7 +156,7 @@ def _lift_unlifted_variables(graph, variable_holder):
     def _should_lift_variable(v):
       return ((v._in_graph_mode  # pylint: disable=protected-access
                and v.graph.building_function)
-              and isinstance(v, resource_variable_ops.ResourceVariable)
+              and isinstance(v, resource_variable_ops.BaseResourceVariable)
               and v.handle not in existing_captures)
 
     for old_variable in global_collection_variables:

tensorflow/python/framework/func_graph.py

@@ -794,7 +794,7 @@ def func_graph_from_py_func(name,
     inputs = []
     for arg in (nest.flatten(func_args, expand_composites=True) +
                 nest.flatten(func_kwargs, expand_composites=True)):
-      if isinstance(arg, resource_variable_ops.ResourceVariable):
+      if isinstance(arg, resource_variable_ops.BaseResourceVariable):
         # Even if an argument variable was not used in the function, we've
         # already manually captured the resource Tensor when creating argument
         # placeholders.
@@ -1003,7 +1003,7 @@ def _get_defun_inputs(args, names, structure, flat_shapes=None):
               "_user_specified_name",
               attr_value_pb2.AttrValue(s=compat.as_bytes(requested_name)))
         function_inputs.append(placeholder)
-      elif isinstance(arg, resource_variable_ops.ResourceVariable):
+      elif isinstance(arg, resource_variable_ops.BaseResourceVariable):
         # Capture arg variables to create placeholders for them. These will be
         # removed as captures after the function is traced (since otherwise we'd
         # just add it back with a new placeholder when the variable was

tensorflow/python/framework/function.py

@@ -790,7 +790,7 @@ class _FuncGraph(ops.Graph):
           collections=collections,
           use_resource=use_resource)
       self.extra_vars.append(var)
-      if (isinstance(var, resource_variable_ops.ResourceVariable) and
+      if (isinstance(var, resource_variable_ops.BaseResourceVariable) and
           self._capture_resource_var_by_value):
         # For resource-based variables read the variable outside the function
         # and pass in the value. This ensures that the function is pure and

tensorflow/python/saved_model/function_deserialization.py

@@ -38,7 +38,7 @@ from tensorflow.python.util import tf_inspect
 
 
 def _is_tensor(t):
-  return isinstance(t, (ops.Tensor, resource_variable_ops.ResourceVariable))
+  return isinstance(t, (ops.Tensor, resource_variable_ops.BaseResourceVariable))
 
 
 def _call_concrete_function(function, inputs):

tensorflow/python/training/optimizer.py

@@ -604,9 +604,9 @@ class Optimizer(
         # We colocate all ops created in _apply_dense or _apply_sparse
         # on the same device as the variable.
         # TODO(apassos): figure out how to get the variable name here.
-        if context.executing_eagerly() or isinstance(
-            var,
-            resource_variable_ops.ResourceVariable) and not var._in_graph_mode:  # pylint: disable=protected-access
+        if (context.executing_eagerly() or
+            isinstance(var, resource_variable_ops.BaseResourceVariable)
+            and not var._in_graph_mode):  # pylint: disable=protected-access
           scope_name = ""
         else:
           scope_name = var.op.name
@@ -617,7 +617,8 @@ class Optimizer(
       else:
         with ops.control_dependencies([self._finish(update_ops, "update")]):
           with ops.colocate_with(global_step):
-            if isinstance(global_step, resource_variable_ops.ResourceVariable):
+            if isinstance(
+                global_step, resource_variable_ops.BaseResourceVariable):
               # TODO(apassos): the implicit read in assign_add is slow; consider
               # making it less so.
               apply_updates = resource_variable_ops.assign_add_variable_op(

tensorflow/python/training/saving/saveable_object_util.py

@@ -179,7 +179,7 @@ def saveable_objects_for_op(op, name):
     # pylint: enable=protected-access
   else:
     # A variable or tensor.
-    if isinstance(op, resource_variable_ops.ResourceVariable):
+    if isinstance(op, resource_variable_ops.BaseResourceVariable):
       # pylint: disable=protected-access
       if op._in_graph_mode:
         variable = op._graph_element
@@ -233,7 +233,7 @@ def op_list_to_dict(op_list, convert_variable_to_tensor=True):
   # pylint: disable=protected-access
   for var in op_list:
     resource_or_ref_variable = (
-        isinstance(var, resource_variable_ops.ResourceVariable) or
+        isinstance(var, resource_variable_ops.BaseResourceVariable) or
         isinstance(var, variables.RefVariable))
 
     if isinstance(var, saveable_object.SaveableObject):
@@ -263,7 +263,7 @@ def op_list_to_dict(op_list, convert_variable_to_tensor=True):
       # indicating whether they were created in a graph building context. We
       # also get Tensors when graph building, which do not have this property.
       if not getattr(var, "_in_graph_mode", True):
-        if not isinstance(var, resource_variable_ops.ResourceVariable):
+        if not isinstance(var, resource_variable_ops.BaseResourceVariable):
           raise ValueError(
               "Can only save/restore ResourceVariables when eager execution "
               "is enabled, type: %s." % type(var))
@@ -277,7 +277,7 @@ def op_list_to_dict(op_list, convert_variable_to_tensor=True):
               (var._shared_name,))
       else:
         if convert_variable_to_tensor:
-          if isinstance(var, resource_variable_ops.ResourceVariable):
+          if isinstance(var, resource_variable_ops.BaseResourceVariable):
             var = var._graph_element  # pylint: disable=protected-access
           else:
             var = ops.internal_convert_to_tensor(var, as_ref=True)