Fix save model issue for stateless ConvLSTM2D layer.

The root cause is that ConvLSTM2D.state is a tuple rather than a list. When converting the state for save_model, the tuple is not converted to trackable objects since the states are (None, None). On the other hand, save_model requires all objects to be trackable when saving.

We didn't hit this issue for keras.LSTM since its state is a list, rather than tuple. The list is auto convert to ListWrapper since list itself is mutable.

This should fix https://github.com/tensorflow/tensorflow/issues/40328 and partly https://github.com/tensorflow/tensorflow/issues/38220

PiperOrigin-RevId: 317131403
Change-Id: I202d4dbdb29accc7a047d5f5a2fef08d24d05c7c

tensorflow/python/keras/saving/saved_model/layer_serialization.py

@@ -159,6 +159,12 @@ class RNNSavedModelSaver(LayerSavedModelSaver):
     objects, functions = (
         super(RNNSavedModelSaver, self)._get_serialized_attributes_internal(
             serialization_cache))
-
-    objects['states'] = data_structures.wrap_or_unwrap(self.obj.states)
+    states = data_structures.wrap_or_unwrap(self.obj.states)
+    # Force the tuple into TupleWrapper which is a trackable object. The
+    # save/load code requires all the objects to be trackable.
+    # Tuple is not converted to TupleWrapper by data_structures.wrap_or_unwrap()
+    # if it doesn't contains any trackable objects.
+    if isinstance(states, tuple):
+      states = data_structures._TupleWrapper(states)  # pylint: disable=protected-access
+    objects['states'] = states
     return objects, functions

tensorflow/python/keras/saving/saved_model/saved_model_test.py

@@ -773,6 +773,26 @@ class TestModelSavingAndLoadingV2(keras_parameterized.TestCase):
     self.assertAllClose(layer.states, loaded_layer.states)
     self.assertAllClose(model(input_arr), loaded(input_arr))
 
+  def testSaveStatelessConvLSTM2D(self):
+    data_format = 'channels_first'
+    batch, timesteps, channels, rows, cols = 12, 10, 8, 4, 4
+    input_arr = np.ones(
+        (batch, timesteps, channels, rows, cols)).astype('float32')
+    layer = keras.layers.ConvLSTM2D(
+        filters=16, kernel_size=(1, 1), data_format=data_format)
+    x = keras.Input(batch_shape=(batch, timesteps, channels, rows, cols))
+    y = layer(x)
+    model = keras.Model(x, y)
+
+    predict_1 = model(input_arr)
+    saved_model_dir = self._save_model_dir()
+    tf_save.save(model, saved_model_dir)
+    del model
+
+    loaded = keras_load.load(saved_model_dir)
+    predict_2 = loaded(input_arr)
+    self.assertAllClose(predict_1, predict_2)
+
   def testSaveWithRaggedInputs(self):
 
     class EmbeddingMerger(keras.layers.Layer):