Fix issue when a Layer's first argument isn't called "inputs".

PiperOrigin-RevId: 290563724
Change-Id: I55a5da8a4624dfc330c89e9ce5302501137b82cb

tensorflow/python/keras/engine/base_layer.py

@@ -626,13 +626,12 @@ class Layer(module.Module):
     # carry over the input mask
     return mask
 
-  def __call__(self, inputs, *args, **kwargs):
+  def __call__(self, *args, **kwargs):
     """Wraps `call`, applying pre- and post-processing steps.
 
     Arguments:
-      inputs: input tensor(s).
-      *args: additional positional arguments to be passed to `self.call`.
-      **kwargs: additional keyword arguments to be passed to `self.call`.
+      *args: Positional arguments to be passed to `self.call`.
+      **kwargs: Keyword arguments to be passed to `self.call`.
 
     Returns:
       Output tensor(s).
@@ -655,6 +654,17 @@ class Layer(module.Module):
     if not hasattr(self, '_thread_local'):
       raise RuntimeError(
           'You must call `super().__init__()` in the layer constructor.')
+
+    # Grab the first positional or keyword argument.
+    if args:
+      inputs = args[0]
+      args = args[1:]
+    elif self._call_fn_args[0] in kwargs:
+      inputs = kwargs.pop(self._call_fn_args[0])
+    else:
+      raise ValueError(
+          'The first argument to `Layer.call` must always be passed.')
+
     call_context = base_layer_utils.call_context()
     input_list = nest.flatten(inputs)
 

tensorflow/python/keras/engine/base_layer_test.py

@@ -593,6 +593,43 @@ class BaseLayerTest(keras_parameterized.TestCase):
     with self.assertRaisesRegexp(RuntimeError, 'You must call `super()'):
       layer(np.random.random((10, 2)))
 
+  @test_util.run_in_graph_and_eager_modes
+  def test_first_arg_not_called_inputs(self):
+    x, y = array_ops.ones((10, 1)), array_ops.ones((10, 1))
+
+    class ArgLayer(keras.layers.Layer):
+
+      def call(self, x, y):
+        return x + y
+
+    layer = ArgLayer()
+    out = self.evaluate(layer(x=x, y=y))
+    self.assertAllClose(out, 2 * np.ones((10, 1)))
+
+    class KwargLayer(keras.layers.Layer):
+
+      def call(self, x=None, y=None):
+        return x + y
+
+    layer = KwargLayer()
+    out = self.evaluate(layer(x=x, y=y))
+    self.assertAllClose(out, 2 * np.ones((10, 1)))
+
+    with self.assertRaisesRegexp(ValueError, 'must always be passed'):
+      layer(y=y)
+
+    class TFFunctionLayer(keras.layers.Layer):
+
+      @def_function.function
+      def call(self, x, y=None):
+        if y is None:
+          return x
+        return x + y
+
+    layer = TFFunctionLayer()
+    out = self.evaluate(layer(x=x, y=y))
+    self.assertAllClose(out, 2 * np.ones((10, 1)))
+
 
 class SymbolicSupportTest(test.TestCase):
 

tensorflow/python/keras/saving/saving_utils.py

@@ -147,7 +147,7 @@ def trace_model_call(model, input_signature=None):
 
     with base_layer_utils.call_context().enter(
         model, inputs=inputs, build_graph=False, training=False, saving=True):
-      outputs_list = nest.flatten(model(inputs=inputs, training=False))
+      outputs_list = nest.flatten(model(inputs, training=False))
 
     try:
       output_names = model.output_names
@@ -211,8 +211,8 @@ def model_metadata(model, include_optimizer=True, require_config=True):
         metadata['training_config']['optimizer_config'] = optimizer_config
       except AttributeError:
         pass  # If the model has an optimizer, but not all of the attributes
-              # loss, _compile_metrics, etc., then it was not compiled using
-              # model.compile. In this case, do not save the training config.
+        # loss, _compile_metrics, etc., then it was not compiled using
+        # model.compile. In this case, do not save the training config.
   return metadata