diff --git a/tensorflow/python/keras/engine/base_layer.py b/tensorflow/python/keras/engine/base_layer.py
index 2fc669be2c8..46c913256a5 100644
--- a/tensorflow/python/keras/engine/base_layer.py
+++ b/tensorflow/python/keras/engine/base_layer.py
@@ -2591,6 +2591,9 @@ class AddLoss(Layer):
   """
 
   def __init__(self, unconditional, **kwargs):
+    # Pass autocast=False, as there is no reason to cast loss to a different
+    # dtype.
+    kwargs['autocast'] = False
     super(AddLoss, self).__init__(**kwargs)
     self.unconditional = unconditional
 
diff --git a/tensorflow/python/keras/mixed_precision/experimental/keras_test.py b/tensorflow/python/keras/mixed_precision/experimental/keras_test.py
index a64e0f68149..784d7b304dd 100644
--- a/tensorflow/python/keras/mixed_precision/experimental/keras_test.py
+++ b/tensorflow/python/keras/mixed_precision/experimental/keras_test.py
@@ -764,6 +764,17 @@ class KerasModelTest(keras_parameterized.TestCase):
                                    'optimizer" must be an instance of '):
         model.compile(optimizers.SGD(1.), 'mse')
 
+  @test_util.run_in_graph_and_eager_modes
+  @testing_utils.enable_v2_dtype_behavior
+  def test_functional_model_loss_dtype(self):
+    with policy.policy_scope('float16'):
+      x = layers.Input(shape=(1,))
+      y = AddLayer()(x)
+      model = models.Model(x, y)
+      model.add_loss(math_ops.cast(y, 'float32'))
+      # The loss should not be casted to the policy's dtype.
+      self.assertEqual(model.losses[0].dtype, 'float32')
+
   @parameterized.named_parameters(
       {
           'testcase_name': 'base',