[TF optimizers (v1)] Non-slot variables are ResourceVariables iff the input vars are.

This fixes a bug where Adam beta*_power variables were always created as RefVars
even if the optimizer acts on ResourceVars.  This broke certain defun + Adam
use cases.

Also fixed the unit tests, which *always* created ResourceVariables
(ever since variables.Variable() constructor became aliased to ResourceVariables).

PiperOrigin-RevId: 224869338

tensorflow/python/training/adam_test.py

@@ -68,8 +68,8 @@ class AdamOptimizerTest(test.TestCase):
           var0 = resource_variable_ops.ResourceVariable(var0_np)
           var1 = resource_variable_ops.ResourceVariable(var1_np)
         else:
-          var0 = variables.Variable(var0_np)
-          var1 = variables.Variable(var1_np)
+          var0 = variables.RefVariable(var0_np)
+          var1 = variables.RefVariable(var1_np)
         grads0_np_indices = np.array([0, 1], dtype=np.int32)
         grads0 = ops.IndexedSlices(
             constant_op.constant(grads0_np),
@@ -156,6 +156,9 @@ class AdamOptimizerTest(test.TestCase):
                               self.evaluate(repeated_index_update_var))
 
   def doTestBasic(self, use_resource=False, use_callable_params=False):
+    if context.executing_eagerly() and not use_resource:
+      self.skipTest(
+          "Skipping test with use_resource=False and executing eagerly.")
     for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]):
       with self.session(graph=ops.Graph()):
         # Initialize variables for numpy implementation.
@@ -171,8 +174,8 @@ class AdamOptimizerTest(test.TestCase):
           var1 = resource_variable_ops.ResourceVariable(
               var1_np, name="var1_%d" % i)
         else:
-          var0 = variables.Variable(var0_np)
-          var1 = variables.Variable(var1_np)
+          var0 = variables.RefVariable(var0_np)
+          var1 = variables.RefVariable(var1_np)
         grads0 = constant_op.constant(grads0_np)
         grads1 = constant_op.constant(grads1_np)
 
@@ -194,6 +197,14 @@ class AdamOptimizerTest(test.TestCase):
         self.assertTrue(beta2_power is not None)
         self.assertIn(beta1_power, opt_variables)
         self.assertIn(beta2_power, opt_variables)
+        # Ensure that non-slot variables are the same type as the requested
+        # variables.
+        self.assertEqual(
+            use_resource,
+            resource_variable_ops.is_resource_variable(beta1_power))
+        self.assertEqual(
+            use_resource,
+            resource_variable_ops.is_resource_variable(beta2_power))
 
         if not context.executing_eagerly():
           with ops.Graph().as_default():

tensorflow/python/training/optimizer.py

@@ -822,7 +822,10 @@ class Optimizer(
               name=name, shape=None)
           if restored_initial_value is not None:
             initial_value = restored_initial_value
-        v = variable_scope.variable(initial_value, name=name, trainable=False)
+        v = variable_scope.variable(
+            initial_value, name=name, trainable=False,
+            use_resource=resource_variable_ops.is_resource_variable(
+                colocate_with))
       # Restore this variable by name if necessary, but don't add a
       # Checkpointable dependency. Optimizers return the current graph's
       # non-slot variables from _checkpoint_dependencies explicitly rather