Remove Layer._supports_ragged_inputs property.

This property made it more difficult to create a Layer that supports
RaggedTensors, since by default every user-created Layer class was assumed to
not work with RaggedTensors.

Instead, an error message is added to common built-in Layer subclasses that don't
support RaggedTensors.

PiperOrigin-RevId: 309315394
Change-Id: Id587d99cfaa4890c41aee49ec437f96108b4fbc7

tensorflow/python/keras/engine/base_layer.py

@@ -313,7 +313,6 @@ class Layer(module.Module, version_utils.LayerVersionSelector):
     # Provides information about which inputs are compatible with the layer.
     self._input_spec = None
     self.supports_masking = False
-    self._supports_ragged_inputs = False
 
     self._init_set_name(name)
     self._activity_regularizer = kwargs.pop('activity_regularizer', None)
@@ -905,12 +904,6 @@ class Layer(module.Module, version_utils.LayerVersionSelector):
         # are casted, not before.
         input_spec.assert_input_compatibility(self.input_spec, inputs,
                                               self.name)
-        if (any(isinstance(x, ragged_tensor.RaggedTensor) for x in input_list)
-            and not self._supports_ragged_inputs):
-          raise ValueError('Layer %s does not support RaggedTensors as input. '
-                           'Inputs received: %s. You can try converting your '
-                           'input to an uniform tensor.' % (self.name, inputs))
-
         graph = backend.get_graph()
         with graph.as_default(), backend.name_scope(self._name_scope()):
           # Build layer if applicable (if the `build` method has been

tensorflow/python/keras/engine/base_layer_test.py

@@ -1454,7 +1454,6 @@ class DTypeTest(keras_parameterized.TestCase):
         row_splits=array_ops.constant([0, 2, 2, 3], dtype='int64'))
 
     layer = IdentityLayer(dtype='float16')
-    layer._supports_ragged_inputs = True
 
     for x in sparse, ragged:
       self.assertEqual(x.dtype, 'float32')
@@ -1462,19 +1461,6 @@ class DTypeTest(keras_parameterized.TestCase):
       self.assertEqual(y.dtype, 'float16')
       self.assertEqual(type(x), type(y))
 
-  def test_supports_ragged_inputs_attribute_error(self):
-    with self.assertRaisesRegexp(ValueError,
-                                 'does not support RaggedTensors'):
-      ragged = ragged_tensor.RaggedTensor.from_row_splits(
-          values=array_ops.constant([1., 2., 3.], dtype='float32'),
-          row_splits=array_ops.constant([0, 2, 2, 3], dtype='int64'))
-      model = sequential.Sequential([
-          input_layer.InputLayer(input_shape=(None,), ragged=True),
-          IdentityLayer()
-      ])
-      model.compile(rmsprop.RMSprop(0.001), loss='mse')
-      model.train_on_batch(ragged)
-
   @testing_utils.enable_v2_dtype_behavior
   def test_passing_non_tensor(self):
     layer = IdentityLayer()

tensorflow/python/keras/engine/base_layer_utils.py

@@ -34,6 +34,7 @@ from tensorflow.python.ops import control_flow_v2_func_graphs
 from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import init_ops_v2
 from tensorflow.python.ops import variables as tf_variables
+from tensorflow.python.ops.ragged import ragged_tensor
 from tensorflow.python.training.tracking import base as tracking
 from tensorflow.python.util import nest
 from tensorflow.python.util import tf_contextlib
@@ -792,6 +793,14 @@ class TrackableWeightHandler(object):
     backend.get_session().run(self._assign_op, feed_dict)
 
 
+def no_ragged_support(inputs, layer_name):
+  input_list = nest.flatten(inputs)
+  if any(isinstance(x, ragged_tensor.RaggedTensor) for x in input_list):
+    raise ValueError('Layer %s does not support RaggedTensors as input. '
+                     'Inputs received: %s. You can try converting your '
+                     'input to an uniform tensor.' % (layer_name, inputs))
+
+
 # TODO(kathywu): This is a temporary hack. When a network of layers is revived
 # from SavedModel, only the top-level layer will have losses. This causes issues
 # in eager mode because the child layers may have graph losses

tensorflow/python/keras/engine/base_layer_v1.py

@@ -182,7 +182,6 @@ class Layer(base_layer.Layer):
     # Provides information about which inputs are compatible with the layer.
     self._input_spec = None
     self.supports_masking = False
-    self._supports_ragged_inputs = False
 
     self._init_set_name(name)
     self._activity_regularizer = kwargs.pop('activity_regularizer', None)
@@ -746,12 +745,6 @@ class Layer(base_layer.Layer):
         # are casted, not before.
         input_spec.assert_input_compatibility(self.input_spec, inputs,
                                               self.name)
-        if (any(isinstance(x, ragged_tensor.RaggedTensor) for x in input_list)
-            and self._supports_ragged_inputs is False):  # pylint: disable=g-bool-id-comparison
-          raise ValueError('Layer %s does not support RaggedTensors as input. '
-                           'Inputs received: %s. You can try converting your '
-                           'input to an uniform tensor.' % (self.name, inputs))
-
         graph = backend.get_graph()
         with graph.as_default(), backend.name_scope(self._name_scope()):
           # Build layer if applicable (if the `build` method has been

tensorflow/python/keras/engine/input_layer.py

@@ -132,7 +132,6 @@ class InputLayer(base_layer.Layer):
     self.ragged = ragged
     self.batch_size = batch_size
     self.supports_masking = True
-    self._supports_ragged_inputs = True
 
     if isinstance(input_shape, tensor_shape.TensorShape):
       input_shape = tuple(input_shape.as_list())

tensorflow/python/keras/engine/network.py

@@ -51,7 +51,6 @@ from tensorflow.python.keras.utils.io_utils import ask_to_proceed_with_overwrite
 from tensorflow.python.keras.utils.io_utils import path_to_string
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
-from tensorflow.python.ops.ragged import ragged_tensor
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.training import checkpoint_management
 from tensorflow.python.training import py_checkpoint_reader
@@ -426,10 +425,6 @@ class Network(base_layer.Layer):
     self._is_graph_network = False
     self.inputs = None
     self.outputs = None
-    # Since we don't know whether the subclass model support ragged inputs,
-    # we leave it as True, otherwise the layer will raise error when a ragged
-    # tensor is called as input.
-    self._supports_ragged_inputs = True
 
   @property
   @trackable_layer_utils.cache_recursive_attribute('dynamic')
@@ -1398,8 +1393,6 @@ class Network(base_layer.Layer):
                         'Note that input tensors are '
                         'instantiated via `tensor = tf.keras.Input(shape)`.\n'
                         'The tensor that caused the issue was: ' + str(x.name))
-      if isinstance(x, ragged_tensor.RaggedTensor):
-        self._supports_ragged_inputs = True
 
     # Check compatibility of batch sizes of Input Layers.
     input_batch_sizes = [

tensorflow/python/keras/engine/network_test.py

@@ -967,14 +967,9 @@ class NetworkConstructionTest(keras_parameterized.TestCase):
   @combinations.generate(combinations.keras_mode_combinations())
   def test_composite_call_kwarg_derived_from_keras_layer(self):
 
-    # Create a test layer that accepts composite tensor inputs (note the
+    # Create a test layer that accepts composite tensor inputs.
-    # 'supports_ragged_inputs = True' in the init method.)
     class MaybeAdd(layers.Layer):
 
-      def __init__(self, **kwargs):
-        super(MaybeAdd, self).__init__(**kwargs)
-        self._supports_ragged_inputs = True
-
       def call(self, x1, x2=None):
         # We need to convert this to a tensor for loss calculations -
         # losses don't play nicely with ragged tensors yet.

tensorflow/python/keras/layers/core.py

@@ -37,6 +37,7 @@ from tensorflow.python.keras import backend as K
 from tensorflow.python.keras import constraints
 from tensorflow.python.keras import initializers
 from tensorflow.python.keras import regularizers
+from tensorflow.python.keras.engine import base_layer_utils
 from tensorflow.python.keras.engine.base_layer import Layer
 from tensorflow.python.keras.engine.input_spec import InputSpec
 from tensorflow.python.keras.utils import conv_utils
@@ -830,7 +831,6 @@ class Lambda(Layer):
     if mask is not None:
       self.supports_masking = True
     self.mask = mask
-    self._supports_ragged_inputs = True
     self._output_shape = output_shape
 
     # Warning on every invocation will be quite irksome in Eager mode.
@@ -1177,6 +1177,7 @@ class Dense(Layer):
     self.built = True
 
   def call(self, inputs):
+    base_layer_utils.no_ragged_support(inputs, self.name)
     rank = inputs.shape.rank
     if rank is not None and rank > 2:
       # Broadcasting is required for the inputs.

tensorflow/python/keras/layers/embeddings.py

@@ -121,7 +121,6 @@ class Embedding(Layer):
     self.mask_zero = mask_zero
     self.supports_masking = mask_zero
     self.input_length = input_length
-    self._supports_ragged_inputs = True
 
   @tf_utils.shape_type_conversion
   def build(self, input_shape):

tensorflow/python/keras/layers/merge.py

@@ -21,6 +21,7 @@ from __future__ import division
 from __future__ import print_function
 
 from tensorflow.python.keras import backend as K
+from tensorflow.python.keras.engine import base_layer_utils
 from tensorflow.python.keras.engine.base_layer import Layer
 from tensorflow.python.keras.utils import tf_utils
 from tensorflow.python.ops import array_ops
@@ -43,7 +44,6 @@ class _Merge(Layer):
     """
     super(_Merge, self).__init__(**kwargs)
     self.supports_masking = True
-    self._supports_ragged_inputs = True
 
   def _merge_function(self, inputs):
     raise NotImplementedError
@@ -651,7 +651,6 @@ class Dot(_Merge):
     self.normalize = normalize
     self.supports_masking = True
     self._reshape_required = False
-    self._supports_ragged_inputs = False
 
   @tf_utils.shape_type_conversion
   def build(self, input_shape):
@@ -677,6 +676,7 @@ class Dot(_Merge):
                        'Chosen axes: %s, %s' % (axes[0], axes[1]))
 
   def _merge_function(self, inputs):
+    base_layer_utils.no_ragged_support(inputs, self.name)
     if len(inputs) != 2:
       raise ValueError('A `Dot` layer should be called on exactly 2 inputs')
     x1 = inputs[0]

tensorflow/python/keras/layers/pooling.py

@@ -714,7 +714,6 @@ class GlobalPooling1D(Layer):
     super(GlobalPooling1D, self).__init__(**kwargs)
     self.input_spec = InputSpec(ndim=3)
     self.data_format = conv_utils.normalize_data_format(data_format)
-    self._supports_ragged_inputs = True
 
   def compute_output_shape(self, input_shape):
     input_shape = tensor_shape.TensorShape(input_shape).as_list()
@@ -849,7 +848,6 @@ class GlobalPooling2D(Layer):
     super(GlobalPooling2D, self).__init__(**kwargs)
     self.data_format = conv_utils.normalize_data_format(data_format)
     self.input_spec = InputSpec(ndim=4)
-    self._supports_ragged_inputs = True
 
   def compute_output_shape(self, input_shape):
     input_shape = tensor_shape.TensorShape(input_shape).as_list()
@@ -957,7 +955,6 @@ class GlobalPooling3D(Layer):
     super(GlobalPooling3D, self).__init__(**kwargs)
     self.data_format = conv_utils.normalize_data_format(data_format)
     self.input_spec = InputSpec(ndim=5)
-    self._supports_ragged_inputs = True
 
   def compute_output_shape(self, input_shape):
     input_shape = tensor_shape.TensorShape(input_shape).as_list()

tensorflow/python/keras/layers/preprocessing/categorical_crossing.py

@@ -161,7 +161,6 @@ class CategoryCrossing(Layer):
       self._depth_tuple = depth
     elif depth is not None:
       self._depth_tuple = tuple([i for i in range(1, depth + 1)])
-    self._supports_ragged_inputs = True
 
   def partial_crossing(self, partial_inputs, ragged_out, sparse_out):
     """Gets the crossed output from a partial list/tuple of inputs."""

tensorflow/python/keras/layers/preprocessing/categorical_encoding.py

@@ -102,9 +102,6 @@ class CategoricalEncoding(base_preprocessing_layer.CombinerPreprocessingLayer):
     self._sparse = sparse
     self._called = False
 
-    # This layer supports RaggedTensor inputs.
-    self._supports_ragged_inputs = True
-
     # We are adding these here instead of in build() since they do not depend
     # on the input shape at all.
     if max_tokens is None:

tensorflow/python/keras/layers/preprocessing/discretization.py

@@ -56,7 +56,6 @@ class Discretization(Layer):
 
   def __init__(self, bins, output_mode=INTEGER, **kwargs):
     super(Discretization, self).__init__(**kwargs)
-    self._supports_ragged_inputs = True
     self.bins = bins
     self.output_mode = output_mode
 

tensorflow/python/keras/layers/preprocessing/hashing.py

@@ -90,7 +90,6 @@ class Hashing(Layer):
     super(Hashing, self).__init__(name=name, **kwargs)
     self.num_bins = num_bins
     self.salt = salt
-    self._supports_ragged_inputs = True
 
   def call(self, inputs):
     # Converts integer inputs to string.

tensorflow/python/keras/layers/preprocessing/index_lookup.py

@@ -150,9 +150,6 @@ class IndexLookup(base_preprocessing_layer.CombinerPreprocessingLayer):
     super(IndexLookup, self).__init__(
         combiner=_IndexLookupCombiner(self.max_tokens), **kwargs)
 
-    # This layer supports RaggedTensor inputs.
-    self._supports_ragged_inputs = True
-
     # If the layer's input type is int32, we can only output int32 values -
     # MutableHashTable doesn't allow us to map int32->int64.
     if self.dtype == dtypes.int32:

tensorflow/python/keras/layers/preprocessing/reduction.py

@@ -75,7 +75,6 @@ class Reduction(Layer):
     # We temporarily turn off autocasting, as it does not apply to named call
     # kwargs.
     super(Reduction, self).__init__(**kwargs)
-    self._supports_ragged_inputs = True
 
   def call(self, inputs, weights=None):
     # If we are not weighting the inputs we can immediately reduce the data

tensorflow/python/keras/layers/preprocessing/text_vectorization.py

@@ -302,7 +302,6 @@ class TextVectorization(CombinerPreprocessingLayer):
         combiner=_TextVectorizationCombiner(
             self._max_vocab_size, compute_idf=output_mode == TFIDF),
         **kwargs)
-    self._supports_ragged_inputs = True
 
     reserve_zero = output_mode in [None, INT]
     self._index_lookup_layer = self._get_index_lookup_class()(

tensorflow/python/keras/layers/recurrent.py

@@ -438,7 +438,6 @@ class RNN(Layer):
     self._states = None
     self.constants_spec = None
     self._num_constants = 0
-    self._supports_ragged_inputs = True
 
     if stateful:
       if ds_context.has_strategy():

tensorflow/python/keras/layers/wrappers.py

@@ -125,7 +125,6 @@ class TimeDistributed(Wrapper):
               input=layer))
     super(TimeDistributed, self).__init__(layer, **kwargs)
     self.supports_masking = True
-    self._supports_ragged_inputs = True
 
     # It is safe to use the fast, reshape-based approach with all of our
     # built-in Layers.
@@ -449,7 +448,6 @@ class Bidirectional(Wrapper):
     self._trainable = True
     self._num_constants = 0
     self.input_spec = layer.input_spec
-    self._supports_ragged_inputs = True
 
   def _verify_layer_config(self):
     """Ensure the forward and backward layers have valid common property."""

tensorflow/python/keras/utils/composite_tensor_support_test.py

@@ -55,7 +55,6 @@ class ToDense(Layer):
   def __init__(self, default_value, **kwargs):
     super(ToDense, self).__init__(**kwargs)
     self._default_value = default_value
-    self._supports_ragged_inputs = True
 
   def call(self, inputs):
     if isinstance(inputs, dict):  # Dicts are no longer flattened.
@@ -83,7 +82,6 @@ class ToRagged(Layer):
     super(ToRagged, self).__init__(**kwargs)
     self._padding = padding
     self._ragged_rank = ragged_rank
-    self._supports_ragged_inputs = True
 
   def call(self, inputs):
     return ragged_tensor.RaggedTensor.from_tensor(