Fix from_pretrained API with config and state_dict (#21542)

src/transformers/modeling_utils.py

@@ -2770,7 +2770,10 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
                         del state_dict[checkpoint_key]
             return mismatched_keys
 
-        folder = os.path.sep.join(resolved_archive_file[0].split(os.path.sep)[:-1])
+        if resolved_archive_file is not None:
+            folder = os.path.sep.join(resolved_archive_file[0].split(os.path.sep)[:-1])
+        else:
+            folder = None
         if device_map is not None and is_safetensors:
             param_device_map = expand_device_map(device_map, original_loaded_keys)
 

tests/test_modeling_common.py

@@ -2749,6 +2749,15 @@ class ModelUtilsTest(TestCasePlus):
             BertModel.from_pretrained(TINY_T5)
         self.assertTrue("You are using a model of type t5 to instantiate a model of type bert" in cl.out)
 
+    def test_model_from_pretrained_no_checkpoint(self):
+        config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert")
+        model = BertModel(config)
+        state_dict = model.state_dict()
+
+        new_model = BertModel.from_pretrained(pretrained_model_name_or_path=None, config=config, state_dict=state_dict)
+        for p1, p2 in zip(model.parameters(), new_model.parameters()):
+            self.assertTrue(torch.equal(p1, p2))
+
     @require_torch
     def test_model_from_config_torch_dtype(self):
         # test that the model can be instantiated with dtype of user's choice - as long as it's a