Correct missing keys + test (#3143)

src/transformers/modeling_utils.py

@@ -539,6 +539,15 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
                 model_to_load = getattr(model, cls.base_model_prefix)
 
             load(model_to_load, prefix=start_prefix)
+
+            if model.__class__.__name__ != model_to_load.__class__.__name__:
+                base_model_state_dict = model_to_load.state_dict().keys()
+                head_model_state_dict_without_base_prefix = [
+                    key.split(cls.base_model_prefix + ".")[-1] for key in model.state_dict().keys()
+                ]
+
+                missing_keys.extend(head_model_state_dict_without_base_prefix - base_model_state_dict)
+
             if len(missing_keys) > 0:
                 logger.info(
                     "Weights of {} not initialized from pretrained model: {}".format(

tests/test_modeling_common.py

@@ -526,6 +526,21 @@ class ModelTesterMixin:
             x = model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(x, torch.nn.Linear))
 
+    def test_correct_missing_keys(self):
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            base_model_prefix = model.base_model_prefix
+
+            if hasattr(model, base_model_prefix):
+                with tempfile.TemporaryDirectory() as temp_dir_name:
+                    model.base_model.save_pretrained(temp_dir_name)
+                    model, loading_info = model_class.from_pretrained(temp_dir_name, output_loading_info=True)
+
+                    with self.subTest(msg="Missing keys for {}".format(model.__class__.__name__)):
+                        self.assertGreater(len(loading_info["missing_keys"]), 0)
+
     def test_tie_model_weights(self):
         if not self.test_torchscript:
             return