Fix saving TF custom models (#7291)

* Fix #7277

* Apply style

* Add a full training pipeline test

* Apply style

Author: jplu

src/transformers/modeling_tf_utils.py

@@ -85,20 +85,20 @@ def keras_serializable(cls):
 
     @functools.wraps(initializer)
     def wrapped_init(self, *args, **kwargs):
-        transformers_config = kwargs.pop("transformers_config", None)
-        config = args[0] if args and isinstance(args[0], PretrainedConfig) else kwargs.get("config", None)
-        if config is not None and transformers_config is not None:
-            raise ValueError("Must pass either `config` or `transformers_config`, not both")
-        elif config is not None:
-            # normal layer construction, call with unchanged args (config is already in there)
-            initializer(self, *args, **kwargs)
-        elif transformers_config is not None:
-            # Keras deserialization, convert dict to config
-            config = config_class.from_dict(transformers_config)
+        config = args[0] if args and isinstance(args[0], PretrainedConfig) else kwargs.pop("config", None)
+
+        if isinstance(config, dict):
+            config = config_class.from_dict(config)
             initializer(self, config, *args, **kwargs)
+        elif isinstance(config, PretrainedConfig):
+            if len(args) > 0:
+                initializer(self, *args, **kwargs)
+            else:
+                initializer(self, config, *args, **kwargs)
         else:
-            raise ValueError("Must pass either `config` (PretrainedConfig) or `transformers_config` (dict)")
-        self._transformers_config = config
+            raise ValueError("Must pass either `config` (PretrainedConfig) or `config` (dict)")
+
+        self._config = config
         self._kwargs = kwargs
 
     cls.__init__ = wrapped_init
@@ -109,7 +109,7 @@ def wrapped_init(self, *args, **kwargs):
 
         def get_config(self):
             cfg = super(cls, self).get_config()
-            cfg["transformers_config"] = self._transformers_config.to_dict()
+            cfg["config"] = self._config.to_dict()
             cfg.update(self._kwargs)
             return cfg
 

tests/test_modeling_tf_common.py

@@ -354,6 +354,69 @@ def test_pt_tf_model_equivalence(self):
             max_diff = np.amax(np.abs(tfo - pto))
             self.assertLessEqual(max_diff, 4e-2)
 
+    def test_train_pipeline_custom_model(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        tf_main_layer_classes = set(
+            module_member
+            for model_class in self.all_model_classes
+            for module in (import_module(model_class.__module__),)
+            for module_member_name in dir(module)
+            if module_member_name.endswith("MainLayer")
+            for module_member in (getattr(module, module_member_name),)
+            if isinstance(module_member, type)
+            and tf.keras.layers.Layer in module_member.__bases__
+            and getattr(module_member, "_keras_serializable", False)
+        )
+
+        for main_layer_class in tf_main_layer_classes:
+            # T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
+            if "T5" in main_layer_class.__name__:
+                # Take the same values than in TFT5ModelTester for this shared layer
+                shared = TFSharedEmbeddings(self.model_tester.vocab_size, self.model_tester.hidden_size, name="shared")
+                config.use_cache = False
+                main_layer = main_layer_class(config, embed_tokens=shared)
+                del inputs_dict["use_cache"]
+            else:
+                main_layer = main_layer_class(config)
+
+            symbolic_inputs = {
+                name: tf.keras.Input(tensor.shape[1:], dtype=tensor.dtype) for name, tensor in inputs_dict.items()
+            }
+
+            if hasattr(self.model_tester, "num_labels"):
+                num_labels = self.model_tester.num_labels
+            else:
+                num_labels = 2
+
+            X = tf.data.Dataset.from_tensor_slices(
+                (inputs_dict, np.random.randint(0, num_labels, (self.model_tester.batch_size, 1)))
+            ).batch(1)
+
+            hidden_states = main_layer(symbolic_inputs)[0]
+            outputs = tf.keras.layers.Dense(num_labels, activation="softmax", name="outputs")(hidden_states)
+            model = tf.keras.models.Model(inputs=symbolic_inputs, outputs=[outputs])
+
+            model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["acc"])
+            model.fit(X, epochs=1)
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                filepath = os.path.join(tmpdirname, "keras_model.h5")
+                model.save(filepath)
+                if "T5" in main_layer_class.__name__:
+                    model = tf.keras.models.load_model(
+                        filepath,
+                        custom_objects={
+                            main_layer_class.__name__: main_layer_class,
+                            "TFSharedEmbeddings": TFSharedEmbeddings,
+                        },
+                    )
+                else:
+                    model = tf.keras.models.load_model(
+                        filepath, custom_objects={main_layer_class.__name__: main_layer_class}
+                    )
+                assert isinstance(model, tf.keras.Model)
+                model(inputs_dict)
+
     def test_compile_tf_model(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
 

tests/test_modeling_tf_funnel.py

@@ -327,7 +327,7 @@ def prepare_config_and_inputs_for_common(self):
 
 
 @require_tf
-class FunnelModelTest(TFModelTesterMixin, unittest.TestCase):
+class TFFunnelModelTest(TFModelTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             TFFunnelModel,