Merge pull request #41483 from tomerk/cherrypicks_DC0YA

Raise an error when some but not all values passed to the first layer…

tensorflow/python/keras/engine/base_layer.py

@@ -921,7 +921,7 @@ class Layer(module.Module, version_utils.LayerVersionSelector):
     # >> inputs = tf.keras.Input(10)
     # >> outputs = MyLayer()(inputs)  # Functional construction mode.
     # >> model = tf.keras.Model(inputs, outputs)
-    if _in_functional_construction_mode(inputs, args, kwargs, input_list):
+    if _in_functional_construction_mode(self, inputs, args, kwargs, input_list):
       return self._functional_construction_call(inputs, args, kwargs,
                                                 input_list)
 
@@ -3205,7 +3205,7 @@ class AddMetric(Layer):
     return config
 
 
-def _in_functional_construction_mode(inputs, args, kwargs, input_list):  # pylint: disable=unused-argument
+def _in_functional_construction_mode(layer, inputs, args, kwargs, input_list):  # pylint: disable=unused-argument
   """Check the arguments to see if we are constructing a functional model."""
   if keras_tensor.keras_tensors_enabled():
     # We are constructing a functional model if any of the inputs
@@ -3215,7 +3215,20 @@ def _in_functional_construction_mode(inputs, args, kwargs, input_list):  # pylin
         for tensor in nest.flatten([inputs, args, kwargs]))
   else:
     if context.executing_eagerly():
-      return all(tf_utils.is_symbolic_tensor(t) for t in input_list)
+      all_inputs_symbolic = all(
+          tf_utils.is_symbolic_tensor(t) for t in input_list)
+      if (base_layer_utils.is_subclassed(layer) and
+          any(tf_utils.is_symbolic_tensor(t) for t in nest.flatten(
+              [inputs, args, kwargs])) and not all_inputs_symbolic):
+        raise ValueError('It appears you are trying to construct a '
+                         'functional model, but not all of the inputs in '
+                         'the first positional argument of your layer call '
+                         'are symbolic tensors. '
+                         '(Input objects, or the output of another layer) '
+                         'Functional models cannot correctly track custom '
+                         'layers unless all values in the first call argument '
+                         'are symbolic.')
+      return all_inputs_symbolic
     else:
       return (base_layer_utils.is_in_keras_graph() or
               all(hasattr(t, '_keras_history') for t in input_list))

tensorflow/python/keras/engine/functional_test.py

@@ -932,6 +932,72 @@ class NetworkConstructionTest(keras_parameterized.TestCase):
     # Check that second input was correctly added to first.
     self.assertEqual(history.history['loss'][0], 0.0)
 
+  @combinations.generate(combinations.times(
+      combinations.keras_mode_combinations(mode='eager'),
+      combinations.combine(use_keras_tensors=False)))
+  def test_only_some_in_first_arg_derived_from_keras_layer(self):
+    class MyAddAll(layers.Layer):
+
+      def call(self, inputs):
+        x = inputs[0]
+        for inp in inputs[1:]:
+          if inp is not None:
+            x = x + inp
+        return x
+
+    input1 = input_layer_lib.Input(10)
+    input2 = input_layer_lib.Input(10)
+    layer = MyAddAll()
+
+    with self.assertRaisesRegexp(ValueError, 'construct a functional'):
+      layer([0.0, input1, None, input2, None])
+
+  @combinations.generate(combinations.times(
+      combinations.keras_mode_combinations(mode='eager'),
+      combinations.combine(use_keras_tensors=True)))
+  def test_only_some_in_first_arg_derived_from_keras_layer_keras_tensors(self):
+    # This functionality is unsupported in v1 graphs
+
+    class MyAddAll(layers.Layer):
+
+      def call(self, inputs):
+        x = inputs[0]
+        for inp in inputs[1:]:
+          if inp is not None:
+            x = x + inp
+        return x
+
+    input1 = input_layer_lib.Input(10)
+    input2 = input_layer_lib.Input(10)
+    layer = MyAddAll()
+    outputs = layer([0.0, input1, None, input2, None])
+    model = training_lib.Model([input1, input2], outputs)
+    self.assertIn(layer, model.layers)
+    model.compile(
+        'sgd',
+        'mse',
+        run_eagerly=testing_utils.should_run_eagerly())
+    history = model.fit(
+        x=[3 * np.ones((10, 10)), 7 * np.ones((10, 10))],
+        y=10 * np.ones((10, 10)),
+        batch_size=2)
+    # Check that second input was correctly added to first.
+    self.assertEqual(history.history['loss'][0], 0.0)
+
+    # Check serialization.
+    model = training_lib.Model.from_config(
+        model.get_config(), custom_objects={'MyAddAll': MyAddAll})
+    model.compile(
+        'sgd',
+        'mse',
+        run_eagerly=testing_utils.should_run_eagerly())
+    history = model.fit(
+        x=[3 * np.ones((10, 10)), 7 * np.ones((10, 10))],
+        y=10 * np.ones((10, 10)),
+        batch_size=2)
+    # Check that second input was correctly added to first.
+    self.assertEqual(history.history['loss'][0], 0.0)
+
   @combinations.generate(combinations.keras_mode_combinations())
   def test_call_kwarg_derived_from_keras_layer(self):
 
@@ -1070,7 +1136,8 @@ class NetworkConstructionTest(keras_parameterized.TestCase):
     input2 = input_layer_lib.Input(10)
     input3 = input_layer_lib.Input(10)
 
-    outputs = AddAll()(
+    layer = AddAll()
+    outputs = layer(
         [input1, 4 * array_ops.ones((1, 10))],
         x3={
             'a': input2,
@@ -1078,6 +1145,7 @@ class NetworkConstructionTest(keras_parameterized.TestCase):
             'c': 5 * array_ops.ones((1, 10))
         })
     model = training_lib.Model([input1, input2, input3], outputs)
+    self.assertIn(layer, model.layers)
     model.compile(
         'sgd',
         'mse',