eager: Rename in_eager_mode to executing_eagerly and get rid of in_graph_mode.

This is in preparation to introduce one public, stable symbol: tf.executing_eagerly()
(i.e., part of moving APIs related to eager execution from "contrib" to a namespace
where we provide API stability guarantees)

PiperOrigin-RevId: 188212646

6 files changed, 68 insertions, 65 deletions

diff --git a/tensorflow/python/layers/base.py b/tensorflow/python/layers/base.py
index 15f72786de..e9066d3fda 100644
--- a/tensorflow/python/layers/base.py
+++ b/tensorflow/python/layers/base.py
@@ -115,7 +115,7 @@ class Layer(checkpointable.CheckpointableBase):
     # Provides information about which inputs are compatible with the layer.
     self.input_spec = None
 
-    if activity_regularizer and context.in_eager_mode():
+    if activity_regularizer and context.executing_eagerly():
       raise ValueError(
           ('Activity regularization is not supported when executing eagerly. '
            'Got activity_regularizer=%s') % (activity_regularizer,))
@@ -228,7 +228,7 @@ class Layer(checkpointable.CheckpointableBase):
 
   @property
   def updates(self):
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       raise RuntimeError('Layer.updates not supported in Eager mode.')
     if not self.trainable and not self.stateful:
       return []
@@ -260,7 +260,7 @@ class Layer(checkpointable.CheckpointableBase):
         have is available at runtime.
         A step counter might fall into this category.
     """
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       return  # Updates already applied when in eager mode.
 
     updates = _to_list(updates)
@@ -286,7 +286,7 @@ class Layer(checkpointable.CheckpointableBase):
     Raises:
       RuntimeError: If called in Eager mode.
     """
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       raise RuntimeError('`get_updates_for()` not supported in Eager mode.')
 
     # Updates disabled if layer is not trainable and not explicitly stateful.
@@ -317,7 +317,7 @@ class Layer(checkpointable.CheckpointableBase):
     Returns:
       A list of tensors.
     """
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       # _losses may only contain variable regularization losses when executing
       # eagerly, and they have been saved as lambdas to be executed when
       # requested.
@@ -355,7 +355,7 @@ class Layer(checkpointable.CheckpointableBase):
     Raises:
       RuntimeError: If called in Eager mode.
     """
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       # TODO(fchollet): it should be possible (and highly desirable) to support
       # `add_loss` in eager mode. This allows great convenience and flexibility
       # in defining custom losses on the fly (e.g. in VAEs).
@@ -389,7 +389,7 @@ class Layer(checkpointable.CheckpointableBase):
     Raises:
       RuntimeError: If called in Eager mode.
     """
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       raise RuntimeError('Layer.get_losses_for not supported in Eager mode.')
 
     if inputs is None:
@@ -509,7 +509,7 @@ class Layer(checkpointable.CheckpointableBase):
     # will occur; it should be None if and only if initialization will take
     # place in the eager context.
     init_graph = None
-    if context.in_graph_mode():
+    if not context.executing_eagerly():
       default_graph = ops.get_default_graph()
       if default_graph.building_function:
         with ops.init_scope():
@@ -517,7 +517,7 @@ class Layer(checkpointable.CheckpointableBase):
           # will be lifted; if initialization ops will be lifted into
           # the eager context, then there is nothing to retrieve, since variable
           # collections are not supported when eager execution is enabled.
-          if context.in_graph_mode():
+          if not context.executing_eagerly():
             init_graph = ops.get_default_graph()
             existing_variables = set(tf_variables.global_variables())
       else:
@@ -624,17 +624,17 @@ class Layer(checkpointable.CheckpointableBase):
     self._set_scope(kwargs.pop('scope', None))
     input_list = nest.flatten(inputs)
 
-    in_graph_mode = context.in_graph_mode()
+    build_graph = not context.executing_eagerly()
     in_deferred_mode = isinstance(input_list[0], _DeferredTensor)
     # Ensure the Layer, if being reused, is working with inputs from
     # the same graph as where it was created.
-    if in_graph_mode:
+    if build_graph:
       try:
         # Set layer's "graph" at build time
         self._graph = ops._get_graph_from_inputs(input_list, graph=self._graph)  # pylint: disable=protected-access
       except ValueError as e:
         raise ValueError('Input graph and Layer graph are not the same: %s' % e)
-    if in_graph_mode or in_deferred_mode:
+    if build_graph or in_deferred_mode:
       user_kwargs = copy.copy(kwargs)
 
     # Handle Keras mask propagation from previous layer to current layer.
@@ -669,13 +669,14 @@ class Layer(checkpointable.CheckpointableBase):
     with scope_context_manager as scope:
       with ops.name_scope(self._name_scope_name(scope)):
         if not self.built:
-          if not in_graph_mode:
+          if not build_graph:
             # Activity regularization is currently unsupported in Eager mode.
             if self._activity_regularizer:
-              raise ValueError('activity_regularizer currently unsupported in '
-                               'Eager mode. Found an activity_regularizer in '
-                               '%s(%s).' % (self.__class__.__name__, self))
-          if not in_graph_mode and not in_deferred_mode:
+              raise ValueError(
+                  'activity_regularizer currently unsupported with '
+                  'eager execution enabled. Found an activity_regularizer in '
+                  '%s(%s).' % (self.__class__.__name__, self))
+          if not build_graph and not in_deferred_mode:
             # TODO(agarwal): support _keras_history in Eager mode.
             for x in input_list:
               if hasattr(x, '_keras_history'):
@@ -706,7 +707,7 @@ class Layer(checkpointable.CheckpointableBase):
         if call_has_scope_arg:
           kwargs['scope'] = scope
         # Check input assumptions set after layer building, e.g. input shape.
-        if in_graph_mode or in_deferred_mode:
+        if build_graph or in_deferred_mode:
           self._assert_input_compatibility(inputs)
 
         if not in_deferred_mode:
@@ -730,7 +731,7 @@ class Layer(checkpointable.CheckpointableBase):
           if len(outputs) == 1:
             outputs = outputs[0]
 
-        if in_graph_mode:
+        if build_graph:
           # Apply activity regularization.
           # Note that it should be applied every time the layer creates a new
           # output, since it is output-specific.
@@ -752,7 +753,7 @@ class Layer(checkpointable.CheckpointableBase):
             else:
               outputs._keras_mask = output_mask  # pylint: disable=protected-access
 
-    if in_graph_mode:
+    if build_graph:
       # If all input tensors have history metadata,
       # we update the output tensors
       # with corresponding history metadata, thus eventually allowing to use
@@ -775,7 +776,7 @@ class Layer(checkpointable.CheckpointableBase):
       # Update global default collections.
       _add_elements_to_collection(self.updates, ops.GraphKeys.UPDATE_OPS)
 
-    if in_deferred_mode or in_graph_mode:
+    if in_deferred_mode or build_graph:
       if _have_all_keras_metadata(inputs):
         # Add an inbound node to the layer, so it can keep track of this call.
         # This updates the layer history of the output tensor(s).
@@ -787,7 +788,7 @@ class Layer(checkpointable.CheckpointableBase):
 
   @property
   def graph(self):
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       raise RuntimeError('Layer.graph not supported in Eager mode.')
     return self._graph
 
@@ -891,7 +892,7 @@ class Layer(checkpointable.CheckpointableBase):
         mode.
         ValueError: If the index provided does not match any node.
     """
-    assert context.in_graph_mode()
+    assert not context.executing_eagerly()
     if not self._inbound_nodes:
       raise RuntimeError('The layer has never been called '
                          'and thus has no defined ' + attr_name + '.')
@@ -921,7 +922,7 @@ class Layer(checkpointable.CheckpointableBase):
     Raises:
       RuntimeError: If called in Eager mode.
     """
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       raise RuntimeError(
           'Layer.get_input_shape_at not supported in Eager mode.')
     return self._get_node_attribute_at_index(node_index, 'input_shapes',
@@ -943,7 +944,7 @@ class Layer(checkpointable.CheckpointableBase):
     Raises:
       RuntimeError: If called in Eager mode.
     """
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       raise RuntimeError(
           'Layer.get_output_shape_at not supported in Eager mode.')
     return self._get_node_attribute_at_index(node_index, 'output_shapes',
@@ -964,7 +965,7 @@ class Layer(checkpointable.CheckpointableBase):
     Raises:
       RuntimeError: If called in Eager mode.
     """
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       raise RuntimeError('Layer.get_input_at not supported in Eager mode.')
     return self._get_node_attribute_at_index(node_index, 'input_tensors',
                                              'input')
@@ -984,7 +985,7 @@ class Layer(checkpointable.CheckpointableBase):
     Raises:
       RuntimeError: If called in Eager mode.
     """
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       raise RuntimeError('Layer.get_output_at not supported in Eager mode.')
     return self._get_node_attribute_at_index(node_index, 'output_tensors',
                                              'output')
@@ -1007,7 +1008,7 @@ class Layer(checkpointable.CheckpointableBase):
       RuntimeError: If called in Eager mode.
       AttributeError: If no inbound nodes are found.
     """
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       raise RuntimeError('Layer.input not supported in Eager mode.')
     if not self._inbound_nodes:
       raise AttributeError('Layer ' + self.name +
@@ -1029,7 +1030,7 @@ class Layer(checkpointable.CheckpointableBase):
         layers.
       RuntimeError: if called in Eager mode.
     """
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       raise RuntimeError('Layer.output not supported in Eager mode.')
     if not self._inbound_nodes:
       raise AttributeError('Layer ' + self.name + ' has no inbound nodes.')
@@ -1051,7 +1052,7 @@ class Layer(checkpointable.CheckpointableBase):
         AttributeError: if the layer has no defined input_shape.
         RuntimeError: if called in Eager mode.
     """
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       raise RuntimeError('Layer.input_shape not supported in Eager mode.')
     if not self._inbound_nodes:
       raise AttributeError('The layer has never been called '
@@ -1112,7 +1113,7 @@ class Layer(checkpointable.CheckpointableBase):
         AttributeError: if the layer has no defined output shape.
         RuntimeError: if called in Eager mode.
     """
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       raise RuntimeError('Layer.output_shape not supported in Eager mode.')
     if not self._inbound_nodes:
       raise AttributeError('The layer has never been called '
@@ -1470,7 +1471,7 @@ def _to_list(x):
 
 
 def _add_elements_to_collection(elements, collection_list):
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     raise RuntimeError('Using collections from Layers not supported in Eager '
                        'mode. Tried to add %s to %s' % (elements,
                                                         collection_list))
diff --git a/tensorflow/python/layers/base_test.py b/tensorflow/python/layers/base_test.py
index 1ee9ec7f7a..9ed4afeaba 100644
--- a/tensorflow/python/layers/base_test.py
+++ b/tensorflow/python/layers/base_test.py
@@ -44,7 +44,7 @@ class BaseLayerTest(test.TestCase):
     self.assertEqual(layer.variables, [])
     self.assertEqual(layer.trainable_variables, [])
     self.assertEqual(layer.non_trainable_variables, [])
-    if context.in_graph_mode():
+    if not context.executing_eagerly():
       # updates, losses only supported in GRAPH mode
       self.assertEqual(layer.updates, [])
       self.assertEqual(layer.losses, [])
@@ -63,7 +63,7 @@ class BaseLayerTest(test.TestCase):
     self.assertEqual(layer.variables, [variable])
     self.assertEqual(layer.trainable_variables, [variable])
     self.assertEqual(layer.non_trainable_variables, [])
-    if context.in_graph_mode():
+    if not context.executing_eagerly():
       self.assertEqual(
           layer.variables,
           ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES))
@@ -77,7 +77,7 @@ class BaseLayerTest(test.TestCase):
     self.assertEqual(layer.variables, [variable, variable_2])
     self.assertEqual(layer.trainable_variables, [variable])
     self.assertEqual(layer.non_trainable_variables, [variable_2])
-    if context.in_graph_mode():
+    if not context.executing_eagerly():
       self.assertEqual(
           len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 1)
 
@@ -161,7 +161,7 @@ class BaseLayerTest(test.TestCase):
     inputs = random_ops.random_uniform((5,), seed=1)
     outputs = layer.apply(inputs)
     self.assertEqual(layer.built, True)
-    if context.in_graph_mode():
+    if not context.executing_eagerly():
       # op is only supported in GRAPH mode
       self.assertEqual(outputs.op.name, 'my_layer/Square')
 
@@ -210,7 +210,7 @@ class BaseLayerTest(test.TestCase):
     inputs = random_ops.random_uniform((5,), seed=1)
     outputs = layer.apply(inputs)
     self.assertEqual(layer.built, True)
-    if context.in_graph_mode():
+    if not context.executing_eagerly():
       # op only supported in GRAPH mode.
       self.assertEqual(outputs.op.name, 'my_layer/Square')
 
@@ -280,7 +280,7 @@ class BaseLayerTest(test.TestCase):
       def call(self, inputs):
         return inputs
 
-    if context.in_graph_mode():
+    if not context.executing_eagerly():
       layer = CustomerLayer()
       with self.assertRaisesRegexp(ValueError, r'requires a defined rank'):
         layer.apply(array_ops.placeholder('int32'))
@@ -307,7 +307,7 @@ class BaseLayerTest(test.TestCase):
       def call(self, inputs):
         return inputs
 
-    if context.in_graph_mode():
+    if not context.executing_eagerly():
       layer = CustomerLayer()
       with self.assertRaisesRegexp(ValueError, r'requires a defined rank'):
         layer.apply(array_ops.placeholder('int32'))
@@ -335,7 +335,7 @@ class BaseLayerTest(test.TestCase):
       def call(self, inputs):
         return inputs
 
-    if context.in_graph_mode():
+    if not context.executing_eagerly():
       layer = CustomerLayer()
       with self.assertRaisesRegexp(ValueError, r'requires a defined rank'):
         layer.apply(array_ops.placeholder('int32'))
@@ -430,7 +430,7 @@ class BaseLayerTest(test.TestCase):
     layer.apply(constant_op.constant(1))
 
     # Works
-    if context.in_graph_mode():
+    if not context.executing_eagerly():
       layer.apply(array_ops.placeholder('int32'))
       layer.apply(array_ops.placeholder('int32', shape=(2, 3)))
 
@@ -453,13 +453,7 @@ class BaseLayerTest(test.TestCase):
         return {'l' + key: inputs[key] for key in inputs}
 
     layer = DictLayer()
-    if context.in_graph_mode():
-      i1 = array_ops.placeholder('int32')
-      i2 = array_ops.placeholder('float32')
-      result = layer.apply({'abel': i1, 'ogits': i2})
-      self.assertTrue(isinstance(result, dict))
-      self.assertEqual(set(['label', 'logits']), set(result.keys()))
-    else:
+    if context.executing_eagerly():
       i1 = constant_op.constant(3)
       i2 = constant_op.constant(4.0)
       result = layer.apply({'abel': i1, 'ogits': i2})
@@ -467,6 +461,12 @@ class BaseLayerTest(test.TestCase):
       self.assertEqual(set(['label', 'logits']), set(result.keys()))
       self.assertEqual(3, result['label'].numpy())
       self.assertEqual(4.0, result['logits'].numpy())
+    else:
+      i1 = array_ops.placeholder('int32')
+      i2 = array_ops.placeholder('float32')
+      result = layer.apply({'abel': i1, 'ogits': i2})
+      self.assertTrue(isinstance(result, dict))
+      self.assertEqual(set(['label', 'logits']), set(result.keys()))
 
   def testActivityRegularizer(self):
     regularizer = math_ops.reduce_sum
diff --git a/tensorflow/python/layers/convolutional.py b/tensorflow/python/layers/convolutional.py
index bb10fe5e8b..74e7c63fb3 100644
--- a/tensorflow/python/layers/convolutional.py
+++ b/tensorflow/python/layers/convolutional.py
@@ -1664,7 +1664,7 @@ class Conv2DTranspose(Conv2D):
         padding=self.padding.upper(),
         data_format=utils.convert_data_format(self.data_format, ndim=4))
 
-    if context.in_graph_mode():
+    if not context.executing_eagerly():
       # Infer the static output shape:
       out_shape = inputs.get_shape().as_list()
       out_shape[c_axis] = self.filters
@@ -1969,7 +1969,7 @@ class Conv3DTranspose(Conv3D):
         data_format=utils.convert_data_format(self.data_format, ndim=5),
         padding=self.padding.upper())
 
-    if context.in_graph_mode():
+    if not context.executing_eagerly():
       # Infer the static output shape:
       out_shape = inputs.get_shape().as_list()
       out_shape[c_axis] = self.filters
diff --git a/tensorflow/python/layers/core.py b/tensorflow/python/layers/core.py
index bdbbc59eaf..e598d9f83a 100644
--- a/tensorflow/python/layers/core.py
+++ b/tensorflow/python/layers/core.py
@@ -156,7 +156,7 @@ class Dense(base.Layer):
       outputs = standard_ops.tensordot(inputs, self.kernel, [[len(shape) - 1],
                                                              [0]])
       # Reshape the output back to the original ndim of the input.
-      if context.in_graph_mode():
+      if not context.executing_eagerly():
         output_shape = shape[:-1] + [self.units]
         outputs.set_shape(output_shape)
     else:
@@ -374,7 +374,7 @@ class Flatten(base.Layer):
 
   def call(self, inputs):
     outputs = array_ops.reshape(inputs, (array_ops.shape(inputs)[0], -1))
-    if context.in_graph_mode():
+    if not context.executing_eagerly():
       outputs.set_shape(self.compute_output_shape(inputs.get_shape()))
     return outputs
 
diff --git a/tensorflow/python/layers/core_test.py b/tensorflow/python/layers/core_test.py
index 15ce6cba21..ae19866d7a 100644
--- a/tensorflow/python/layers/core_test.py
+++ b/tensorflow/python/layers/core_test.py
@@ -77,7 +77,7 @@ class DenseTest(test.TestCase):
     self.assertListEqual(dense.trainable_variables,
                          [dense.kernel, dense.bias])
     self.assertListEqual(dense.non_trainable_variables, [])
-    if context.in_graph_mode():
+    if not context.executing_eagerly():
       self.assertEqual(
           len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 2)
     self.assertEqual(dense.kernel.name, 'my_dense/kernel:0')
@@ -98,7 +98,7 @@ class DenseTest(test.TestCase):
     self.assertListEqual(dense.variables, [dense.kernel])
     self.assertListEqual(dense.trainable_variables, [dense.kernel])
     self.assertListEqual(dense.non_trainable_variables, [])
-    if context.in_graph_mode():
+    if not context.executing_eagerly():
       self.assertEqual(
           len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 1)
     self.assertEqual(dense.kernel.name, 'my_dense/kernel:0')
@@ -113,7 +113,7 @@ class DenseTest(test.TestCase):
     self.assertListEqual(dense.non_trainable_variables,
                          [dense.kernel, dense.bias])
     self.assertListEqual(dense.trainable_variables, [])
-    if context.in_graph_mode():
+    if not context.executing_eagerly():
       self.assertEqual(
           len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 0)
 
@@ -162,13 +162,13 @@ class DenseTest(test.TestCase):
     dense = core_layers.Dense(2, activation=nn_ops.relu, name='dense1')
     inputs = random_ops.random_uniform((5, 3), seed=1)
     outputs = dense(inputs)
-    if context.in_graph_mode():
+    if not context.executing_eagerly():
       self.assertEqual(outputs.op.name, 'dense1/Relu')
 
     dense = core_layers.Dense(2, name='dense2')
     inputs = random_ops.random_uniform((5, 3), seed=1)
     outputs = dense(inputs)
-    if context.in_graph_mode():
+    if not context.executing_eagerly():
       self.assertEqual(outputs.op.name, 'dense2/BiasAdd')
 
   def testActivityRegularizer(self):
@@ -374,7 +374,7 @@ class DropoutTest(test.TestCase):
     dp = core_layers.Dropout(0.5)
     inputs = array_ops.ones((5, 3))
     dropped = dp.apply(inputs, training=True)
-    if context.in_graph_mode():
+    if not context.executing_eagerly():
       self.evaluate(variables.global_variables_initializer())
     np_output = self.evaluate(dropped)
     self.assertAlmostEqual(0., np_output.min())
diff --git a/tensorflow/python/layers/normalization.py b/tensorflow/python/layers/normalization.py
index d83292b809..c23d755a8e 100644
--- a/tensorflow/python/layers/normalization.py
+++ b/tensorflow/python/layers/normalization.py
@@ -338,8 +338,9 @@ class BatchNormalization(base.Layer):
           return var
 
         with ops.device(None):
-          device = ((lambda _: self.moving_mean.device)
-                    if context.in_graph_mode() else self.moving_mean.device)
+          device = (
+              self.moving_mean.device if context.executing_eagerly() else
+              (lambda _: self.moving_mean.device))
           with ops.device(device):
             self.renorm_mean = _renorm_variable('renorm_mean', param_shape)
             self.renorm_mean_weight = _renorm_variable('renorm_mean_weight', ())
@@ -347,8 +348,9 @@ class BatchNormalization(base.Layer):
           # renorm_stddev_weight. This allows us to (1) mix the average
           # stddev with the minibatch stddev early in training, and (2) compute
           # the unbiased average stddev by dividing renorm_stddev by the weight.
-          device = ((lambda _: self.moving_variance.device)
-                    if context.in_graph_mode() else self.moving_variance.device)
+          device = (
+              self.moving_variance.device if context.executing_eagerly() else
+              (lambda _: self.moving_variance.device))
           with ops.device(device):
             self.renorm_stddev = _renorm_variable('renorm_stddev', param_shape)
             self.renorm_stddev_weight = _renorm_variable(
@@ -420,7 +422,7 @@ class BatchNormalization(base.Layer):
                                                 one_minus_decay)
       variance_update = self._assign_moving_average(self.moving_variance,
                                                     variance, one_minus_decay)
-      if context.in_graph_mode():
+      if not context.executing_eagerly():
         # Note that in Eager mode, the updates are already executed when running
         # assign_moving_averages. So we do not need to put them into
         # collections.
@@ -493,7 +495,7 @@ class BatchNormalization(base.Layer):
     return (r, d, new_mean, new_variance)
 
   def call(self, inputs, training=False):
-    in_eager_mode = context.in_eager_mode()
+    in_eager_mode = context.executing_eagerly()
     if self.virtual_batch_size is not None:
       # Virtual batches (aka ghost batches) can be simulated by reshaping the
       # Tensor and reusing the existing batch norm implementation
@@ -610,7 +612,7 @@ class BatchNormalization(base.Layer):
           training,
           lambda: _do_update(self.moving_variance, new_variance),
           lambda: self.moving_variance)
-      if context.in_graph_mode():
+      if not context.executing_eagerly():
         self.add_update(mean_update, inputs=inputs)
         self.add_update(variance_update, inputs=inputs)