remove mixed precision

official/nlp/modeling/layers/on_device_embedding_test.py

@@ -214,28 +214,5 @@ class OnDeviceEmbeddingTest(keras_parameterized.TestCase):
     output = model.predict(input_data)
     self.assertEqual(tf.float32, output.dtype)
 
-  def test_use_scale_layer_invocation_with_mixed_precision(self):
-    vocab_size = 31
-    embedding_width = 27
-    policy = tf.keras.mixed_precision.experimental.Policy("mixed_float16")
-    test_layer = on_device_embedding.OnDeviceEmbedding(
-        vocab_size=vocab_size, embedding_width=embedding_width,
-        dtype=policy, use_scale=True)
-    # Create a 2-dimensional input (the first dimension is implicit).
-    sequence_length = 23
-    input_tensor = tf.keras.Input(shape=(sequence_length), dtype=tf.int32)
-    output_tensor = test_layer(input_tensor)
-
-    # Create a model from the test layer.
-    model = tf.keras.Model(input_tensor, output_tensor)
-
-    # Invoke the model on test data. We can't validate the output data itself
-    # (the NN is too complex) but this will rule out structural runtime errors.
-    batch_size = 3
-    input_data = np.random.randint(
-        vocab_size, size=(batch_size, sequence_length))
-    output = model.predict(input_data)
-    self.assertEqual(tf.float16, output.dtype)
-
 if __name__ == "__main__":
   tf.test.main()