Updates Keras layer `__call__` to always set `training`, with the following priority order:

# Training mode for `Layer.__call__` is set via (in order of priority):
    # (1) The `training` argument passed to this `Layer.__call__`, if it is not None
    # (2) The training mode of an outer `Layer.__call__`.
    # (3) The default mode set by `tf.keras.backed.set_learning_phase` (if set).
    # (4) Any non-None default value for `training` specified in the `call`
    #  signature
    # (5) False (treating the layer as if it's in inference)

Previously (4) and (5) were missing, leading to crashes for layers that do not provide a default argument for `training`.

Note that (4) is fragile to reflection issues, and may get confused by decorators.

PiperOrigin-RevId: 317709904
Change-Id: I58039a4d9e5106bcb27f4cfbf65e6762f1b40807

tensorflow/python/keras/engine/base_layer.py

@@ -943,10 +943,14 @@ class Layer(module.Module, version_utils.LayerVersionSelector):
       kwargs['mask'] = input_masks
 
     # Training mode for `Layer.call` is set via (in order of priority):
-    # (1) The `training` argument passed to this `Layer.call`.
+    # (1) The `training` argument passed to this `Layer.call`, if it is not None
     # (2) The training mode of an outer `Layer.call`.
-    # (3) The default mode set by `tf.keras.backed.set_learning_phase` (if set).
-    training_mode = self._set_training_mode(args, kwargs, call_context)
+    # (3) The default mode set by `tf.keras.backend.set_learning_phase` (if set)
+    # (4) Any non-None default value for `training` specified in the call
+    #  signature
+    # (5) False (treating the layer as if it's in inference)
+    args, kwargs, training_mode = self._set_training_mode(
+        args, kwargs, call_context)
 
     # Losses are cleared for all sublayers on the outermost `Layer.call`.
     # Losses are not cleared on inner `Layer.call`s, because sublayers can be
@@ -1020,7 +1024,7 @@ class Layer(module.Module, version_utils.LayerVersionSelector):
     # propagate `training` value from this layer's calling layer.
     training_value = None
     training_arg_passed_by_framework = False
-    # Priority 1: `training` was explicitly passed.
+    # Priority 1: `training` was explicitly passed a non-None value.
     if self._call_arg_was_passed('training', args, kwargs):
       training_value = self._get_call_arg_value('training', args, kwargs)
       if not self._expects_training_arg:
@@ -1030,17 +1034,23 @@ class Layer(module.Module, version_utils.LayerVersionSelector):
       # Priority 2: `training` was passed to a parent layer.
       if call_context.training is not None:
         training_value = call_context.training
-      # Priority 3a: `learning_phase()` has been set.
+      # Priority 3: `learning_phase()` has been set.
       elif backend.global_learning_phase_is_set():
         training_value = backend.learning_phase()
-
-      if self._expects_training_arg and training_value is not None:
         # Force the training_value to be bool type which matches to the contract
         # for layer/model call args.
         if tensor_util.is_tensor(training_value):
           training_value = math_ops.cast(training_value, dtypes.bool)
         else:
           training_value = bool(training_value)
+      # Priority 4: trace layer with the default training argument specified
+      # in the `call` signature (or in inference mode if the `call` signature
+      # specifies no non-None default).
+      else:
+        training_value = self._default_training_arg
+      # In cases (2), (3), (4) the training argument is passed automatically
+      # by the framework, and will not be hard-coded into the model.
+      if self._expects_training_arg:
         args, kwargs = self._set_call_arg_value('training', training_value,
                                                 args, kwargs)
         training_arg_passed_by_framework = True
@@ -1150,6 +1160,8 @@ class Layer(module.Module, version_utils.LayerVersionSelector):
       # (1) `training` was passed to this `Layer.call`.
       if self._call_arg_was_passed('training', args, kwargs):
         training_mode = self._get_call_arg_value('training', args, kwargs)
+      # If no `training` arg was passed, or `None` was explicitly passed,
+      # the framework will make a decision about the training mode is.
       if training_mode is None:
         call_ctx_training = call_context.training
         # (2) `training` mode is inferred from an outer `Layer.call`.
@@ -1165,10 +1177,15 @@ class Layer(module.Module, version_utils.LayerVersionSelector):
             training_mode = math_ops.cast(training_mode, dtypes.bool)
           else:
             training_mode = bool(training_mode)
+        # (4) We default to using `call`'s default value for `training`,
+        # or treating the layer as if it is in inference if no non-None default
+        # is specified in the `call` signature.
+        else:
+          training_mode = self._default_training_arg
 
-        # For case (2) or (3), `training` arg is passed by framework.
-        if training_mode is not None:
-          kwargs['training'] = training_mode
+        # For case (2), (3), (4) `training` arg is passed by framework.
+        args, kwargs = self._set_call_arg_value('training', training_mode, args,
+                                                kwargs)
     else:
       if 'training' in kwargs:
         # `training` was passed to this `Layer` but is not needed for
@@ -1178,7 +1195,7 @@ class Layer(module.Module, version_utils.LayerVersionSelector):
         # Grab the current `training` mode from any outer `Layer.call`.
         training_mode = call_context.training
 
-    return training_mode
+    return args, kwargs, training_mode
 
   def _autographed_call(self):
     # Wrapping `call` function in autograph to allow for dynamic control
@@ -2529,7 +2546,7 @@ class Layer(module.Module, version_utils.LayerVersionSelector):
       if len(args) > arg_pos:
         args = list(args)
         args[arg_pos] = new_value
-        return args, kwargs
+        return tuple(args), kwargs
     if new_value is None and pop_kwarg_if_none:
       kwargs.pop(arg_name, None)
     else:
@@ -2873,6 +2890,10 @@ class Layer(module.Module, version_utils.LayerVersionSelector):
     call_fn_args = self._call_fn_args
     self._expects_training_arg = ('training' in call_fn_args or
                                   self._call_accepts_kwargs)
+    # The default training arg will be any (non-None) default specified in the
+    # method signature, or `False` if no non-None default is specified.
+    self._default_training_arg = self._call_fn_arg_defaults.get(
+        'training') or False
     self._expects_mask_arg = ('mask' in call_fn_args or
                               self._call_accepts_kwargs)
 
@@ -2892,6 +2913,19 @@ class Layer(module.Module, version_utils.LayerVersionSelector):
       return all_args[1:]
     return all_args
 
+  @property
+  @tracking.cached_per_instance
+  def _call_fn_arg_defaults(self):
+    call_fn_args = self._call_fn_args
+    call_fn_defaults = self._call_full_argspec.defaults or []
+    defaults = dict()
+
+    # The call arg defaults are an n-tuple of the last n elements of the args
+    # list. (n = # of elements that have a default argument)
+    for i in range(-1 * len(call_fn_defaults), 0):
+      defaults[call_fn_args[i]] = call_fn_defaults[i]
+    return defaults
+
   @property
   @tracking.cached_per_instance
   def _call_fn_arg_positions(self):

tensorflow/python/keras/engine/base_layer_test.py

@@ -629,6 +629,96 @@ class BaseLayerTest(keras_parameterized.TestCase):
     self.assertTrue(layer.built)
     self.assertEqual([None, 3], layer._build_input_shape.as_list())
 
+  @combinations.generate(combinations.combine(mode=['eager']))
+  def custom_layer_training_arg(self):
+    class CustomLayerNoTrainingArg(base_layer.Layer):
+
+      def __init__(self, nested_layer=None):
+        self._nested_layer = nested_layer or array_ops.identity
+
+      def call(self, inputs):
+        return self._nested_layer(inputs)
+
+    class CustomLayerDefaultTrainingMissing(base_layer.Layer):
+
+      def __init__(self, nested_layer=None):
+        self._nested_layer = nested_layer or array_ops.identity
+
+      def call(self, inputs, training):
+        if training:
+          return self._nested_layer(inputs)
+        else:
+          return self._nested_layer(inputs) * 0.5
+
+    class CustomLayerDefaultTrainingFalse(base_layer.Layer):
+
+      def __init__(self, nested_layer=None):
+        self._nested_layer = nested_layer or array_ops.identity
+
+      def call(self, inputs, training=False):
+        if training:
+          return self._nested_layer(inputs)
+        else:
+          return self._nested_layer(inputs) * 0.5
+
+    class CustomLayerDefaultTrainingTrue(base_layer.Layer):
+
+      def __init__(self, nested_layer=None):
+        self._nested_layer = nested_layer or array_ops.identity
+
+      def call(self, inputs, training=True):
+        if training:
+          return self._nested_layer(inputs)
+        else:
+          return self._nested_layer(inputs) * 0.5
+
+    x = array_ops.ones(shape=(1, 1))
+
+    # If the layer signature doesn't specify a default training arg,
+    # run it in inference mode when to training arg is passed
+    # to __call__
+    layer = CustomLayerDefaultTrainingMissing()
+    self.assertAllEqual(layer(x), x * 0.5)
+    self.assertAllEqual(layer(x, training=False), x * 0.5)
+    self.assertAllEqual(layer(x, training=True), x)
+
+    # If the layer signature specifies `False` as the default training arg,
+    # run it in inference mode when no training arg is passed
+    # to __call__
+    layer = CustomLayerDefaultTrainingFalse()
+    self.assertAllEqual(layer(x), x * 0.5)
+    self.assertAllEqual(layer(x, training=False), x * 0.5)
+    self.assertAllEqual(layer(x, training=True), x)
+
+    # If the layer signature specifies `True` as the default training arg,
+    # explicitly run it in training mode when no training arg is passed
+    # to __call__
+    layer = CustomLayerDefaultTrainingTrue()
+    self.assertAllEqual(layer(x), x)
+    self.assertAllEqual(layer(x, training=False), x * 0.5)
+    self.assertAllEqual(layer(x, training=True), x)
+
+    # Outer layers/models should set the training context implicitly for all
+    # nested layers, respecting whatever mode the outer layer was run with.
+    layer = CustomLayerDefaultTrainingTrue(CustomLayerDefaultTrainingFalse())
+    self.assertAllEqual(layer(x), x)
+    self.assertAllEqual(layer(x, training=False), x * 0.25)
+    self.assertAllEqual(layer(x, training=True), x)
+
+    layer = CustomLayerDefaultTrainingFalse(CustomLayerDefaultTrainingTrue())
+    self.assertAllEqual(layer(x), x * 0.25)
+    self.assertAllEqual(layer(x, training=False), x * 0.25)
+    self.assertAllEqual(layer(x, training=True), x)
+
+    # If the outer layer `call` doesn't take a training argument at all,
+    # it'll set the nested scope as inference when no training arg is passed in.
+    # If a training arg is passed in it won't use it directly in `call`, but
+    # it will set the nested training mode.
+    layer = CustomLayerNoTrainingArg(CustomLayerDefaultTrainingTrue())
+    self.assertAllEqual(layer(x), x * 0.5)
+    self.assertAllEqual(layer(x, training=False), x * 0.5)
+    self.assertAllEqual(layer(x, training=True), x)
+
   def test_activity_regularizer_string(self):
 
     class MyLayer(base_layer.Layer):
@@ -1387,6 +1477,7 @@ class DTypeTest(keras_parameterized.TestCase):
     class IdentityLayerWithArgs(base_layer.Layer):
 
       def call(self, inputs, *args, **kwargs):
+        kwargs.pop('training', None)
         return nest.flatten([inputs, args, kwargs])
 
     layer = IdentityLayerWithArgs(dtype='float64')

tensorflow/python/keras/engine/functional_test.py

@@ -2036,43 +2036,73 @@ class CacheCorrectnessTest(keras_parameterized.TestCase):
 
   def test_training_passed_during_construction(self):
 
+    def _call(inputs, training):
+      if training is None:
+        return inputs * -1.0
+      elif training:
+        return inputs
+      else:
+        return inputs * 0.0
+
     class MyLayer(base_layer.Layer):
 
-      def call(self, x, training=None):
-        if training is None:
-          return x * -1.0
-        elif training:
-          return x
-        else:
-          return x * 0.0
+      def call(self, inputs, training=True):
+        return _call(inputs, training)
 
     my_layer = MyLayer()
     x = np.ones((1, 10))
 
+    # Hard-coded `true` value passed during construction is respected.
     inputs = input_layer_lib.Input(10)
     outputs = my_layer(inputs, training=True)
     network = functional.Functional(inputs, outputs)
+    self.assertAllEqual(network(x, training=True), _call(x, True))
+    self.assertAllEqual(network(x, training=False), _call(x, True))
+    self.assertAllEqual(network(x), _call(x, True))
 
-    # Hard-coded value passed during construction is respected.
-    self.assertAllEqual(network(x, training=False), x)
-
+    # Hard-coded `false` value passed during construction is respected.
     inputs = input_layer_lib.Input(10)
     outputs = my_layer(inputs, training=False)
     network = functional.Functional(inputs, outputs)
+    self.assertAllEqual(network(x, training=True), _call(x, False))
+    self.assertAllEqual(network(x, training=False), _call(x, False))
+    self.assertAllEqual(network(x), _call(x, False))
 
-    network(x, training=True)
-    # Hard-coded value passed during construction is respected.
-    self.assertAllEqual(network(x, training=True), x * 0.0)
+    if context.executing_eagerly():
+      # In v2, construction still works when no `training` is specified
+      # When no value passed during construction, it uses the runtime value.
+      inputs = input_layer_lib.Input(10)
+      outputs = my_layer(inputs)
+      network = functional.Functional(inputs, outputs)
+      self.assertAllEqual(network(x, training=True), _call(x, True))
+      self.assertAllEqual(network(x, training=False), _call(x, False))
+      self.assertAllEqual(network(x), _call(x, False))
 
+    # `None` value passed positionally during construction is ignored at runtime
+    inputs = input_layer_lib.Input(10)
+    outputs = my_layer(inputs, None)
+    network = functional.Functional(inputs, outputs)
+    self.assertAllEqual(network(x, training=True), _call(x, True))
+    self.assertAllEqual(network(x, training=False), _call(x, False))
+    if context.executing_eagerly():
+      self.assertAllEqual(network(x), _call(x, False))
+    else:
+      # in v1 training would have defaulted to using the `None` inside the layer
+      # if training is not passed at runtime
+      self.assertAllEqual(network(x), _call(x, None))
+
+    # `None` value passed as kwarg during construction is ignored at runtime.
     inputs = input_layer_lib.Input(10)
     outputs = my_layer(inputs, training=None)
     network = functional.Functional(inputs, outputs)
-
-    # `None` value passed during construction is overridden.
-    self.assertAllEqual(network(x, training=True), x)
-    # `None` value passed during construction is overridden.
-    self.assertAllEqual(network(x, training=False), x * 0.0)
-
+    self.assertAllEqual(network(x, training=True), _call(x, True))
+    self.assertAllEqual(network(x, training=False), _call(x, False))
+    if context.executing_eagerly():
+      self.assertAllEqual(network(x), _call(x, False))
+    else:
+      # in v1 training would have defaulted to using the `None` inside the layer
+      # if training is not passed at runtime
+      self.assertAllEqual(network(x), _call(x, None))
 
 if __name__ == '__main__':
   test.main()

tensorflow/python/keras/layers/rnn_cell_wrapper_v2.py

@@ -27,6 +27,7 @@ from __future__ import print_function
 
 from tensorflow.python.keras.layers import recurrent
 from tensorflow.python.ops import rnn_cell_wrapper_impl
+from tensorflow.python.util import tf_inspect
 from tensorflow.python.util.tf_export import tf_export
 
 
@@ -41,6 +42,10 @@ class _RNNCellWrapperV2(recurrent.AbstractRNNCell):
   def __init__(self, cell, *args, **kwargs):
     super(_RNNCellWrapperV2, self).__init__(*args, **kwargs)
     self.cell = cell
+    cell_call_spec = tf_inspect.getfullargspec(cell.call)
+    self._expects_training_arg = ("training" in cell_call_spec.args) or (
+        cell_call_spec.varkw is not None
+    )
 
   def call(self, inputs, state, **kwargs):
     """Runs the RNN cell step computation.

tensorflow/python/keras/tests/custom_training_loop_test.py

@@ -186,7 +186,7 @@ class CustomTrainingLoopTest(keras_parameterized.TestCase):
 
     def train_step(x):
       no_learning_phase_out = model(x)
-      self.assertIsNone(model.layer.training)
+      self.assertFalse(model.layer.training)
       with keras.backend.learning_phase_scope(0):
         inf_learning_phase_out = model(x)
       self.assertEqual(model.layer.training, 0)