Simplify Layer.add_udpate in v2 and update version_selector to use v1 inside a

tf.compat.v1.wrap_function.

No longer track unused Layer.updates in v2.

PiperOrigin-RevId: 316921838
Change-Id: I4698a0c925528594f402f824705d66b8a1ae7b72

tensorflow/python/keras/engine/base_layer.py

@@ -1733,54 +1733,15 @@ class Layer(module.Module, version_utils.LayerVersionSelector):
       inputs: Deprecated, will be automatically inferred.
     """
     call_context = base_layer_utils.call_context()
-
-    if (ds_context.has_strategy() and
-        ds_context.in_cross_replica_context() and
-        # When saving the model, the distribution strategy context should be
-        # ignored, following the default path for adding updates.
-        not call_context.saving):
-      # Updates don't need to be run in a cross-replica context.
+    # No need to run updates during Functional API construction.
+    if call_context.in_keras_graph:
       return
 
-    updates = generic_utils.to_list(updates)
-
-    # All updates can be run immediately in Eager or in a tf.function.
-    if base_layer_utils.is_in_eager_or_tf_function():
+    # Callable updates are disabled by setting `trainable=False`.
     if not call_context.frozen:
-        for update in updates:
+      for update in nest.flatten(updates):
         if callable(update):
           update()
-      return
-
-    def process_update(x):
-      """Standardize update ops.
-
-      Arguments:
-        x: Tensor, op, or callable.
-
-      Returns:
-        An update op.
-      """
-      if callable(x):
-        update = lambda: process_update(x())
-        if not ops.executing_eagerly_outside_functions():
-          # In V1 mode, call the callable right away and process. This is needed
-          # for TPU strategy.
-          return update()
-      elif isinstance(x, ops.Operation):
-        update = x
-      elif hasattr(x, 'op'):
-        update = x.op
-      else:
-        update = ops.convert_to_tensor_v2(x)
-      return update
-
-    updates = [process_update(x) for x in updates]
-    # Non-callable Updates are run automatically inside `call` in V2, so
-    # they do not need to be tracked later.
-    if ops.executing_eagerly_outside_functions() and call_context.in_call:
-      updates = [u for u in updates if callable(u)]
-    self._updates.extend(updates)
 
   def set_weights(self, weights):
     """Sets the weights of the layer, from Numpy arrays.

tensorflow/python/keras/engine/sequential_test.py

@@ -231,20 +231,16 @@ class TestSequential(keras_parameterized.TestCase):
     inner_model.trainable = True
     self.assertEqual(len(model.trainable_weights), 4)
 
+  @keras_parameterized.run_all_keras_modes
   def test_sequential_update_disabling(self):
     val_a = np.random.random((10, 4))
     val_out = np.random.random((10, 4))
 
-    with self.cached_session():
     model = keras.models.Sequential()
     model.add(keras.layers.BatchNormalization(input_shape=(4,)))
-      assert model.updates
 
     model.trainable = False
-      assert not model.updates
-
     model.compile('sgd', 'mse')
-      assert not model.updates
 
     x1 = model.predict(val_a)
     model.train_on_batch(val_a, val_out)
@@ -253,7 +249,6 @@ class TestSequential(keras_parameterized.TestCase):
 
     model.trainable = True
     model.compile('sgd', 'mse')
-      assert model.updates
 
     model.train_on_batch(val_a, val_out)
     x2 = model.predict(val_a)

tensorflow/python/keras/layers/normalization_test.py

@@ -325,18 +325,18 @@ class BatchNormalizationV2Test(keras_parameterized.TestCase):
       norm(inp)
 
   def test_updates_in_wrap_function(self):
-    layer = normalization.BatchNormalization()
 
     def my_func():
+      layer = normalization.BatchNormalization()
       x = array_ops.ones((10, 1))
-      return layer(x, training=True)
+      y = layer(x, training=True)
+      # Updates should be tracked in a `wrap_function`.
+      self.assertLen(layer.updates, 2)
+      return y
 
     wrapped_fn = wrap_function.wrap_function(my_func, [])
     wrapped_fn()
 
-    # Updates should be tracked in a `wrap_function`.
-    self.assertLen(layer.updates, 2)
-
   @keras_parameterized.run_all_keras_modes
   def test_basic_batchnorm_v2_none_shape_and_virtual_batch_size(self):
     # Test case for GitHub issue for 32380
@@ -392,15 +392,11 @@ class NormalizationLayersGraphModeOnlyTest(
       model.compile(gradient_descent.GradientDescentOptimizer(0.01), 'mse')
       model.train_on_batch(x, x)
 
-      self.assertLen(bn.updates, 4)
-
       # Test model-level reuse
       x3 = keras.layers.Input(shape=(10,))
       y3 = model(x3)
       new_model = keras.models.Model(x3, y3, name='new_model')
 
-      self.assertLen(new_model.updates, 6)
-      self.assertLen(model.updates, 6)
       new_model.compile(gradient_descent.GradientDescentOptimizer(0.01), 'mse')
       new_model.train_on_batch(x, x)
 
@@ -415,10 +411,7 @@ class NormalizationLayersGraphModeOnlyTest(
       model = keras.models.Model(a, b)
 
       model.trainable = False
-      assert not model.updates
-
       model.compile(gradient_descent.GradientDescentOptimizer(0.01), 'mse')
-      assert not model.updates
 
       x1 = model.predict(val_a)
       model.train_on_batch(val_a, val_out)
@@ -427,7 +420,6 @@ class NormalizationLayersGraphModeOnlyTest(
 
       model.trainable = True
       model.compile(gradient_descent.GradientDescentOptimizer(0.01), 'mse')
-      assert model.updates
 
       model.train_on_batch(val_a, val_out)
       x2 = model.predict(val_a)
@@ -435,7 +427,6 @@ class NormalizationLayersGraphModeOnlyTest(
 
       layer.trainable = False
       model.compile(gradient_descent.GradientDescentOptimizer(0.01), 'mse')
-      assert not model.updates
 
       x1 = model.predict(val_a)
       model.train_on_batch(val_a, val_out)

tensorflow/python/keras/layers/wrappers_test.py

@@ -234,13 +234,10 @@ class TimeDistributedTest(keras_parameterized.TestCase):
     x = keras.layers.Input(shape=(3, 2))
     layer = keras.layers.TimeDistributed(keras.layers.BatchNormalization())
     _ = layer(x)
-    self.assertEqual(len(layer.updates), 2)
     self.assertEqual(len(layer.trainable_weights), 2)
     layer.trainable = False
-    assert not layer.updates
     assert not layer.trainable_weights
     layer.trainable = True
-    assert len(layer.updates) == 2
     assert len(layer.trainable_weights) == 2
 
   def test_TimeDistributed_with_masked_embedding_and_unspecified_shape(self):

tensorflow/python/keras/utils/version_utils.py

@@ -18,6 +18,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from tensorflow.python.eager import context
 from tensorflow.python.framework import ops
 from tensorflow.python.util import lazy_loader
 
@@ -51,8 +52,8 @@ class ModelVersionSelector(object):
   """Chooses between Keras v1 and v2 Model class."""
 
   def __new__(cls, *args, **kwargs):  # pylint: disable=unused-argument
-    eager_enabled = ops.executing_eagerly_outside_functions()
-    cls = swap_class(cls, training.Model, training_v1.Model, eager_enabled)
+    use_v2 = should_use_v2()
+    cls = swap_class(cls, training.Model, training_v1.Model, use_v2)  # pylint: disable=self-cls-assignment
     return super(ModelVersionSelector, cls).__new__(cls)
 
 
@@ -60,8 +61,8 @@ class LayerVersionSelector(object):
   """Chooses between Keras v1 and v2 Layer class."""
 
   def __new__(cls, *args, **kwargs):  # pylint: disable=unused-argument
-    eager_enabled = ops.executing_eagerly_outside_functions()
-    cls = swap_class(cls, base_layer.Layer, base_layer_v1.Layer, eager_enabled)
+    use_v2 = should_use_v2()
+    cls = swap_class(cls, base_layer.Layer, base_layer_v1.Layer, use_v2)  # pylint: disable=self-cls-assignment
     return super(LayerVersionSelector, cls).__new__(cls)
 
 
@@ -69,10 +70,10 @@ class TensorBoardVersionSelector(object):
   """Chooses between Keras v1 and v2 TensorBoard callback class."""
 
   def __new__(cls, *args, **kwargs):  # pylint: disable=unused-argument
-    eager_enabled = ops.executing_eagerly_outside_functions()
+    use_v2 = should_use_v2()
     start_cls = cls
     cls = swap_class(start_cls, callbacks.TensorBoard, callbacks_v1.TensorBoard,
-                     eager_enabled)
+                     use_v2)
     if start_cls == callbacks_v1.TensorBoard and cls == callbacks.TensorBoard:
       # Since the v2 class is not a subclass of the v1 class, __init__ has to
       # be called manually.
@@ -80,19 +81,33 @@ class TensorBoardVersionSelector(object):
     return super(TensorBoardVersionSelector, cls).__new__(cls)
 
 
-def swap_class(cls, v2_cls, v1_cls, eager_enabled):
+def should_use_v2():
+  """Determine if v1 or v2 version should be used."""
+  if context.executing_eagerly():
+    return True
+  elif ops.executing_eagerly_outside_functions():
+    # Check for a v1 `wrap_function` FuncGraph.
+    # Code inside a `wrap_function` is treated like v1 code.
+    graph = ops.get_default_graph()
+    if (getattr(graph, "name", False) and
+        graph.name.startswith("wrapped_function")):
+      return False
+    return True
+
+
+def swap_class(cls, v2_cls, v1_cls, use_v2):
   """Swaps in v2_cls or v1_cls depending on graph mode."""
   if cls == object:
     return cls
 
   if cls in (v2_cls, v1_cls):
-    if eager_enabled:
+    if use_v2:
       return v2_cls
     return v1_cls
 
   # Recursively search superclasses to swap in the right Keras class.
   cls.__bases__ = tuple(
-      swap_class(base, v2_cls, v1_cls, eager_enabled) for base in cls.__bases__)
+      swap_class(base, v2_cls, v1_cls, use_v2) for base in cls.__bases__)
   return cls