Automated g4 rollback of changelist 200783477

PiperOrigin-RevId: 201204573

2 files changed, 4 insertions, 68 deletions

diff --git a/tensorflow/python/layers/base.py b/tensorflow/python/layers/base.py
index abbe9d0c56..b8969a41ab 100644
--- a/tensorflow/python/layers/base.py
+++ b/tensorflow/python/layers/base.py
@@ -43,15 +43,13 @@ class Layer(base_layer.Layer):
   Arguments:
     trainable: Boolean, whether the layer's variables should be trainable.
     name: String name of the layer.
-    dtype: Default dtype of the layer's weights and computations (default of
-      `None` means use the type of the first input). If not None, inputs will be
-      casted to this dtype.
+    dtype: Default dtype of the layer's weights (default of `None` means use the
+      type of the first input).
 
   Read-only properties:
     name: The name of the layer (string).
-    dtype: Default dtype of the layer's weights and computations. (default of
-      `None` means use the type of the first input). If not None, inputs will be
-      casted to this dtype.
+    dtype: Default dtype of the layer's weights (default of `None` means use the
+      type of the first input).
     trainable_variables: List of trainable variables.
     non_trainable_variables: List of non-trainable variables.
     variables: List of all variables of this layer, trainable and
diff --git a/tensorflow/python/layers/base_test.py b/tensorflow/python/layers/base_test.py
index ad44328aab..fcacc8d603 100644
--- a/tensorflow/python/layers/base_test.py
+++ b/tensorflow/python/layers/base_test.py
@@ -25,8 +25,6 @@ from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import test_util
-from tensorflow.python.keras import backend
-from tensorflow.python.keras.engine import base_layer as keras_base_layer
 from tensorflow.python.layers import base as base_layers
 from tensorflow.python.layers import core as core_layers
 from tensorflow.python.ops import array_ops
@@ -591,65 +589,5 @@ class BaseLayerTest(test.TestCase):
         ValueError, 'Input graph and Layer graph are not the same'):
       layer.apply(constant_op.constant([[1.]]))
 
-  @test_util.run_in_graph_and_eager_modes()
-  def testOnlyCastInputsWhenDtypeSpecified(self):
-
-    class MyKerasLayer(keras_base_layer.Layer):
-
-      def call(self, inputs):
-        self.x = inputs[0]
-        self.y = inputs[1]
-        return self.x + 1, self.y + 2
-
-    # Inherit from both the Keras Layer and base_layers.Layer to ensure we
-    # still get the base_layers.Layer behavior when directly inheriting from
-    # the Keras Layer.
-    class MyTFLayer(MyKerasLayer, base_layers.Layer):
-      pass
-
-    # Test inputs are casted.
-    input1 = array_ops.constant(1.0, dtype=dtypes.float64)
-    input2 = array_ops.constant(1.0, dtype=dtypes.float32)
-    layer = MyTFLayer(dtype=dtypes.float16)
-    output1, output2 = layer([input1, input2])
-    self.assertEqual(output1.dtype, dtypes.float16)
-    self.assertEqual(output2.dtype, dtypes.float16)
-
-    # Test inputs are not casted.
-    input1 = array_ops.constant(1.0, dtype=dtypes.float64)
-    input2 = array_ops.constant(1.0, dtype=dtypes.float32)
-    layer = MyTFLayer()
-    output1, output2 = layer([input1, input2])
-    self.assertEqual(output1.dtype, dtypes.float64)
-    self.assertEqual(output2.dtype, dtypes.float32)
-
-  @test_util.run_in_graph_and_eager_modes()
-  def testVariablesDefaultToFloat32(self):
-
-    class MyKerasLayer(keras_base_layer.Layer):
-
-      def build(self, input_shape):
-        self.x = self.add_weight('x', ())
-
-      def call(self, inputs):
-        return inputs + self.x
-
-    # Inherit from both the Keras Layer and base_layers.Layer to ensure we
-    # still get the base_layers.Layer behavior when directly inheriting from
-    # the Keras Layer.
-    class MyTFLayer(MyKerasLayer, base_layers.Layer):
-      pass
-
-    try:
-      # The behavior of Keras Layers is to default to floatx. Ensure that this
-      # behavior is overridden to instead default to float32.
-      backend.set_floatx('float16')
-      layer = MyTFLayer()
-      layer.build(())
-      self.assertEqual(layer.dtype, None)
-      self.assertEqual(layer.x.dtype.base_dtype, dtypes.float32)
-    finally:
-      backend.set_floatx('float32')
-
 if __name__ == '__main__':
   test.main()