In the short term, making Feature Column V2 produce RefVariables instead of ResourceVariables till we figure out the performance regression issues.

PiperOrigin-RevId: 220535259

tensorflow/python/feature_column/feature_column_v2.py

@@ -184,6 +184,7 @@ class StateManager(object):
                       shape,
                       dtype=None,
                       trainable=True,
+                      use_resource=True,
                       initializer=None):
     """Creates a new variable.
 
@@ -193,12 +194,14 @@ class StateManager(object):
       shape: variable shape.
       dtype: The type of the variable. Defaults to `self.dtype` or `float32`.
       trainable: Whether this variable is trainable or not.
+      use_resource: If true, we use resource variables. Otherwise we use
+        RefVariable.
       initializer: initializer instance (callable).
 
     Returns:
       The created variable.
     """
-    del feature_column, name, shape, dtype, trainable, initializer
+    del feature_column, name, shape, dtype, trainable, use_resource, initializer
     raise NotImplementedError('StateManager.create_variable')
 
   def add_variable(self, feature_column, var):
@@ -270,6 +273,7 @@ class _StateManagerImpl(StateManager):
                       shape,
                       dtype=None,
                       trainable=True,
+                      use_resource=True,
                       initializer=None):
     if name in self._cols_to_vars_map[feature_column]:
       raise ValueError('Variable already exists.')
@@ -280,7 +284,7 @@ class _StateManagerImpl(StateManager):
         dtype=dtype,
         initializer=initializer,
         trainable=self._trainable and trainable,
-        use_resource=True,
+        use_resource=use_resource,
         # TODO(rohanj): Get rid of this hack once we have a mechanism for
         # specifying a default partitioner for an entire layer. In that case,
         # the default getter for Layers should work.
@@ -2539,6 +2543,8 @@ class EmbeddingColumn(
         shape=embedding_shape,
         dtype=dtypes.float32,
         trainable=self.trainable,
+        # TODO(rohanj): Make this True when b/118500434 is fixed.
+        use_resource=False,
         initializer=self.initializer)
 
   def _get_dense_tensor_internal_helper(self, sparse_tensors,

tensorflow/python/feature_column/feature_column_v2_test.py

@@ -1816,6 +1816,8 @@ class LinearModelTest(test.TestCase):
           'sparse_feature': [['a'], ['x']],
       }
       model(features)
+      for var in model.variables:
+        self.assertTrue(isinstance(var, variables_lib.RefVariable))
       variable_names = [var.name for var in model.variables]
       self.assertItemsEqual([
           'linear_model/dense_feature_bucketized/weights:0',
@@ -5592,6 +5594,7 @@ class _TestStateManager(fc.StateManager):
                       shape,
                       dtype=None,
                       trainable=True,
+                      use_resource=True,
                       initializer=None):
     if feature_column not in self._all_variables:
       self._all_variables[feature_column] = {}
@@ -5604,6 +5607,7 @@ class _TestStateManager(fc.StateManager):
           shape=shape,
           dtype=dtype,
           trainable=self._trainable and trainable,
+          use_resource=use_resource,
           initializer=initializer)
       var_dict[name] = var
       return var
@@ -6182,6 +6186,8 @@ class EmbeddingColumnTest(test.TestCase):
     global_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
     self.assertItemsEqual(('feature_layer/aaa_embedding/embedding_weights:0',),
                           tuple([v.name for v in global_vars]))
+    for v in global_vars:
+      self.assertTrue(isinstance(v, variables_lib.RefVariable))
     trainable_vars = ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)
     self.assertItemsEqual(('feature_layer/aaa_embedding/embedding_weights:0',),
                           tuple([v.name for v in trainable_vars]))
@@ -6964,6 +6970,8 @@ class SharedEmbeddingColumnTest(test.TestCase):
     self.assertItemsEqual(
         ['aaa_bbb_shared_embedding:0', 'ccc_ddd_shared_embedding:0'],
         tuple([v.name for v in global_vars]))
+    for v in global_vars:
+      self.assertTrue(isinstance(v, variables_lib.RefVariable))
     trainable_vars = ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)
     if trainable:
       self.assertItemsEqual(