Fix from_pretrained with default base_model_prefix (#15814)

src/transformers/modeling_utils.py

@@ -1580,8 +1580,12 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
         loaded_keys = list(state_dict.keys())
         prefix = model.base_model_prefix
 
-        has_prefix_module = any(s.startswith(prefix) for s in loaded_keys)
-        expects_prefix_module = any(s.startswith(prefix) for s in expected_keys)
+        if len(prefix) > 0:
+            has_prefix_module = any(s.startswith(prefix) for s in loaded_keys)
+            expects_prefix_module = any(s.startswith(prefix) for s in expected_keys)
+        else:
+            has_prefix_module = False
+            expects_prefix_module = False
 
         # key re-naming operations are never done on the keys
         # that are loaded, but always on the keys of the newly initialized model
@@ -1669,9 +1673,9 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
         # Make sure we are able to load base models as well as derived models (with heads)
         start_prefix = ""
         model_to_load = model
-        if not hasattr(model, cls.base_model_prefix) and has_prefix_module:
+        if len(cls.base_model_prefix) > 0 and not hasattr(model, cls.base_model_prefix) and has_prefix_module:
             start_prefix = cls.base_model_prefix + "."
-        if hasattr(model, cls.base_model_prefix) and not has_prefix_module:
+        if len(cls.base_model_prefix) > 0 and hasattr(model, cls.base_model_prefix) and not has_prefix_module:
             model_to_load = getattr(model, cls.base_model_prefix)
             if any(key in expected_keys_not_prefixed for key in loaded_keys):
                 raise ValueError(

tests/test_modeling_common.py

@@ -2105,7 +2105,10 @@ class ModelUtilsTest(TestCasePlus):
         with tempfile.TemporaryDirectory() as tmp_dir:
             model.save_pretrained(tmp_dir)
 
-            model = NoSuperInitModel.from_pretrained(tmp_dir)
+            new_model = NoSuperInitModel.from_pretrained(tmp_dir)
+
+        for p1, p2 in zip(model.parameters(), new_model.parameters()):
+            self.assertTrue(torch.equal(p1, p2))
 
 
 @require_torch

utils/test_module/custom_modeling.py

@@ -7,7 +7,6 @@ from .custom_configuration import CustomConfig, NoSuperInitConfig
 
 class CustomModel(PreTrainedModel):
     config_class = CustomConfig
-    base_model_prefix = "custom"
 
     def __init__(self, config):
         super().__init__(config)
@@ -22,7 +21,6 @@ class CustomModel(PreTrainedModel):
 
 class NoSuperInitModel(PreTrainedModel):
     config_class = NoSuperInitConfig
-    base_model_prefix = "custom"
 
     def __init__(self, config):
         super().__init__(config)