Extend Keras Lambda layers to work with functions of any signature rather than only functions that take one argument.

Any *args and **kwargs passed when calling the lambda layer will be forwarded directly to the underlying lambda.

PiperOrigin-RevId: 311789009
Change-Id: Ic072d2252038330cc944d7f565f14806753d7436

tensorflow/python/keras/layers/core.py

@@ -53,7 +53,7 @@ from tensorflow.python.ops import variable_scope
 from tensorflow.python.platform import tf_logging
 from tensorflow.python.training.tracking import base as trackable
 from tensorflow.python.util import nest
-from tensorflow.python.util import tf_decorator
+from tensorflow.python.util import tf_inspect
 from tensorflow.python.util.tf_export import keras_export
 
 
@@ -738,8 +738,7 @@ class Lambda(Layer):
   models. `Lambda` layers are best suited for simple operations or
   quick experimentation. For more advanced use cases, follow
   [this guide](https://www.tensorflow.org/guide/keras/custom_layers_and_models)
-  for subclassing `tf.keras.layers.Layer`. (Do not subclass
+  for subclassing `tf.keras.layers.Layer`.
-  `tf.keras.layers.Lamba`.)
 
   The main reason to subclass `tf.keras.layers.Layer` instead of using a
   `Lambda` layer is saving and inspecting a Model. `Lambda` layers
@@ -799,7 +798,8 @@ class Lambda(Layer):
     computation, but anything more complex should use a subclass Layer instead.
 
   Arguments:
-    function: The function to evaluate when the layer is called.
+    function: The function to be evaluated. Takes input tensor as first
+      argument.
     output_shape: Expected output shape from function. This argument can be
       inferred if not explicitly provided. Can be a tuple or function. If a
       tuple, it only specifies the first dimension onward;
@@ -812,8 +812,8 @@ class Lambda(Layer):
     mask: Either None (indicating no masking) or a callable with the same
       signature as the `compute_mask` layer method, or a tensor that will be
       returned as output mask regardless of what the input is.
-    arguments: Optional dictionary of keyword arguments to pass by default to
+    arguments: Optional dictionary of keyword arguments to be passed to the
-      the function when those arguments are not passed to the layer call.
+      function.
   Input shape: Arbitrary. Use the keyword argument input_shape (tuple of
     integers, does not include the samples axis) when using this layer as the
     first layer in a model.
@@ -823,16 +823,11 @@ class Lambda(Layer):
   @trackable.no_automatic_dependency_tracking
   def __init__(self, function, output_shape=None, mask=None, arguments=None,
                **kwargs):
+    super(Lambda, self).__init__(**kwargs)
+
     self.arguments = arguments or {}
     self.function = function
 
-    # Decorate the function to produce this layer's call method
-    def _call_wrapper(*args, **kwargs):
-      return self._call_wrapper(*args, **kwargs)
-    self.call = tf_decorator.make_decorator(function, _call_wrapper)
-
-    super(Lambda, self).__init__(**kwargs)
-
     if mask is not None:
       self.supports_masking = True
     self.mask = mask
@@ -841,8 +836,9 @@ class Lambda(Layer):
     # Warning on every invocation will be quite irksome in Eager mode.
     self._already_warned = False
 
-    self._expects_training_arg = 'training' in self._call_fn_args
+    function_args = tf_inspect.getfullargspec(function).args
-    self._expects_mask_arg = 'mask' in self._call_fn_args
+    self._fn_expects_training_arg = 'training' in function_args
+    self._fn_expects_mask_arg = 'mask' in function_args
 
   @tf_utils.shape_type_conversion
   def compute_output_shape(self, input_shape):
@@ -873,22 +869,23 @@ class Lambda(Layer):
     output_shapes = tf_utils.convert_shapes(self._output_shape, to_tuples=False)
     return nest.map_structure(_add_batch, output_shapes)
 
-  def _call_wrapper(self, *args, **kwargs):
+  def call(self, inputs, mask=None, training=None):
     # We must copy for thread safety, but it only needs to be a shallow copy.
-    call_kwargs = {k: v for k, v in self.arguments.items()}
+    kwargs = {k: v for k, v in self.arguments.items()}
-
+    if self._fn_expects_mask_arg:
-    # override default kwargs with the args passed to the layer call
+      kwargs['mask'] = mask
-    call_kwargs.update(kwargs)
+    if self._fn_expects_training_arg:
+      kwargs['training'] = training
 
     created_variables = []
-    def _variable_creator(next_creator, **creator_kwargs):
+    def _variable_creator(next_creator, **kwargs):
-      var = next_creator(**creator_kwargs)
+      var = next_creator(**kwargs)
       created_variables.append(var)
       return var
 
     with backprop.GradientTape(watch_accessed_variables=True) as tape,\
         variable_scope.variable_creator_scope(_variable_creator):
-      result = self.function(*args, **call_kwargs)
+      result = self.function(inputs, **kwargs)
     self._check_variables(created_variables, tape.watched_variables())
     return result
 

tensorflow/python/keras/layers/core_test.py

@@ -139,26 +139,6 @@ class LambdaLayerTest(keras_parameterized.TestCase):
     out = ld([x1, x2])
     self.assertAllEqual(out.shape, [3, 2])
 
-  def test_lambda_multiple_args(self):
-    ld = keras.layers.Lambda(lambda x, y: x[0] + y)
-    x1 = np.ones([3, 2], np.float32)
-    x2 = np.ones([3, 5], np.float32)
-
-    expected_result = x1 * 2
-    self.assertAllEqual(ld([x1, x2], x1), expected_result)
-    self.assertAllEqual(ld([x1, x2], y=x1), expected_result)
-    self.assertAllEqual(ld(x=[x1, x2], y=x1), expected_result)
-
-  def test_lambda_constructor_args_and_multiple_args(self):
-    x1 = np.ones([3, 2], np.float32)
-    x2 = np.ones([3, 5], np.float32)
-    ld = keras.layers.Lambda(lambda x, y: x[0] + y, arguments={'y': x1*2})
-
-    self.assertAllEqual(ld([x1, x2]), x1 * 3)
-    self.assertAllEqual(ld([x1, x2], y=x1), x1 * 2)
-    self.assertAllEqual(ld(x=[x1, x2]), x1 * 3)
-    self.assertAllEqual(ld(x=[x1, x2], y=x1), x1 * 2)
-
   def test_lambda_output_shape(self):
     l = keras.layers.Lambda(lambda x: x + 1, output_shape=(1, 1))
     l(keras.backend.variable(np.ones((1, 1))))

tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-lambda.pbtxt

@@ -145,7 +145,7 @@ tf_class {
   }
   member_method {
     name: "call"
-    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=kwargs, defaults=None"
+    argspec: "args=[\'self\', \'inputs\', \'mask\', \'training\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
   }
   member_method {
     name: "compute_mask"

tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-lambda.pbtxt

@@ -145,7 +145,7 @@ tf_class {
   }
   member_method {
     name: "call"
-    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=kwargs, defaults=None"
+    argspec: "args=[\'self\', \'inputs\', \'mask\', \'training\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
   }
   member_method {
     name: "compute_mask"