Handle compatibility issue & use cloudpickle

conftest.py

@@ -15,6 +15,9 @@
 import pytest  # noqa: E402
 
 from keras.src.backend import backend  # noqa: E402
+from keras.src.saving.object_registration import (  # noqa: E402
+    get_custom_objects,
+)
 
 
 def pytest_configure(config):
@@ -32,3 +35,9 @@ def pytest_collection_modifyitems(config, items):
     for item in items:
         if "requires_trainable_backend" in item.keywords:
             item.add_marker(requires_trainable_backend)
+
+
+# Ensure each test is run in isolation regarding the custom objects dict
+@pytest.fixture(autouse=True)
+def reset_custom_objects_global_dictionary(request):
+    get_custom_objects().clear()

keras/src/models/model_test.py

@@ -1,5 +1,7 @@
 import pickle
+import sys
 
+import cloudpickle
 import numpy as np
 import pytest
 from absl.testing import parameterized
@@ -14,26 +16,6 @@
 from keras.src.saving.object_registration import register_keras_serializable
 
 
-@pytest.fixture
-def my_custom_dense():
-    @register_keras_serializable(package="MyLayers", name="CustomDense")
-    class CustomDense(layers.Layer):
-        def __init__(self, units, **kwargs):
-            super().__init__(**kwargs)
-            self.units = units
-            self.dense = layers.Dense(units)
-
-        def call(self, x):
-            return self.dense(x)
-
-        def get_config(self):
-            config = super().get_config()
-            config.update({"units": self.units})
-            return config
-
-    return CustomDense
-
-
 def _get_model():
     input_a = Input(shape=(3,), batch_size=2, name="input_a")
     input_b = Input(shape=(3,), batch_size=2, name="input_b")
@@ -89,11 +71,15 @@ def _get_model_multi_outputs_dict():
     return model
 
 
-def _get_model_custom_layer():
-    x = Input(shape=(3,), name="input_a")
-    output_a = my_custom_dense()(10, name="output_a")(x)
-    model = Model(x, output_a)
-    return model
+@pytest.fixture
+def fake_main_module(request, monkeypatch):
+    original_main = sys.modules["__main__"]
+
+    def restore_main_module():
+        sys.modules["__main__"] = original_main
+
+    request.addfinalizer(restore_main_module)
+    sys.modules["__main__"] = sys.modules[__name__]
 
 
 @pytest.mark.requires_trainable_backend
@@ -155,7 +141,6 @@ def call(self, x):
         ("single_list_output_2", _get_model_single_output_list),
         ("single_list_output_3", _get_model_single_output_list),
         ("single_list_output_4", _get_model_single_output_list),
-        ("custom_layer", _get_model_custom_layer),
     )
     def test_functional_pickling(self, model_fn):
         model = model_fn()
@@ -170,6 +155,45 @@ def test_functional_pickling(self, model_fn):
 
         self.assertAllClose(np.array(pred_reloaded), np.array(pred))
 
+    # Fake the __main__ module because cloudpickle only serializes
+    # functions & classes if they are defined in the __main__ module.
+    @pytest.mark.usefixtures("fake_main_module")
+    def test_functional_pickling_custom_layer(self):
+        @register_keras_serializable()
+        class CustomDense(layers.Layer):
+            def __init__(self, units, **kwargs):
+                super().__init__(**kwargs)
+                self.units = units
+                self.dense = layers.Dense(units)
+
+            def call(self, x):
+                return self.dense(x)
+
+            def get_config(self):
+                config = super().get_config()
+                config.update({"units": self.units})
+                return config
+
+        x = Input(shape=(3,), name="input_a")
+        output_a = CustomDense(10, name="output_a")(x)
+        model = Model(x, output_a)
+
+        self.assertIsInstance(model, Functional)
+        model.compile()
+        x = np.random.rand(8, 3)
+
+        dumped_pickle = cloudpickle.dumps(model)
+
+        # Verify that we can load the dumped pickle even if the custom object
+        # is not available in the loading environment.
+        del CustomDense
+        reloaded_pickle = cloudpickle.loads(dumped_pickle)
+
+        pred_reloaded = reloaded_pickle.predict(x)
+        pred = model.predict(x)
+
+        self.assertAllClose(np.array(pred_reloaded), np.array(pred))
+
     @parameterized.named_parameters(
         ("single_output_1", _get_model_single_output, None),
         ("single_output_2", _get_model_single_output, "list"),

keras/src/saving/keras_saveable.py

@@ -1,5 +1,4 @@
 import io
-import pickle
 
 from keras.src.saving.object_registration import get_custom_objects
 
@@ -17,28 +16,24 @@ def _obj_type(self):
         )
 
     @classmethod
-    def _unpickle_model(cls, model_buf, *args):
+    def _unpickle_model(cls, data):
         import keras.src.saving.saving_lib as saving_lib
 
         # pickle is not safe regardless of what you do.
 
-        if len(args) == 0:
-            return saving_lib._load_model_from_fileobj(
-                model_buf,
-                custom_objects=None,
-                compile=True,
-                safe_mode=False,
-            )
+        if "custom_objects_buf" in data.keys():
+            import pickle
 
+            custom_objects = pickle.load(data["custom_objects_buf"])
         else:
-            custom_objects_buf = args[0]
-            custom_objects = pickle.load(custom_objects_buf)
-            return saving_lib._load_model_from_fileobj(
-                model_buf,
-                custom_objects=custom_objects,
-                compile=True,
-                safe_mode=False,
-            )
+            custom_objects = None
+
+        return saving_lib._load_model_from_fileobj(
+            data["model_buf"],
+            custom_objects=custom_objects,
+            compile=True,
+            safe_mode=False,
+        )
 
     def __reduce__(self):
         """__reduce__ is used to customize the behavior of `pickle.pickle()`.
@@ -48,14 +43,23 @@ def __reduce__(self):
         keras saving library."""
         import keras.src.saving.saving_lib as saving_lib
 
+        data = {}
+
         model_buf = io.BytesIO()
         saving_lib._save_model_to_fileobj(self, model_buf, "h5")
+        data["model_buf"] = model_buf
+
+        try:
+            import cloudpickle
 
-        custom_objects_buf = io.BytesIO()
-        pickle.dump(get_custom_objects(), custom_objects_buf)
-        custom_objects_buf.seek(0)
+            custom_objects_buf = io.BytesIO()
+            cloudpickle.dump(get_custom_objects(), custom_objects_buf)
+            custom_objects_buf.seek(0)
+            data["custom_objects_buf"] = custom_objects_buf
+        except ImportError:
+            pass
 
         return (
             self._unpickle_model,
-            (model_buf, custom_objects_buf),
+            (data,),
         )