Make sure custom configs work with Transformers

src/transformers/configuration_utils.py

@@ -368,7 +368,7 @@ def __init__(self, **kwargs):
 
     @property
     def name_or_path(self) -> str:
-        return self._name_or_path
+        return getattr(self, "_name_or_path", None)
 
     @name_or_path.setter
     def name_or_path(self, value):

src/transformers/modeling_utils.py

@@ -621,10 +621,10 @@ def tie_weights(self):
         weights instead.
         """
         output_embeddings = self.get_output_embeddings()
-        if output_embeddings is not None and self.config.tie_word_embeddings:
+        if output_embeddings is not None and getattr(self.config, "tie_word_embeddings", True):
             self._tie_or_clone_weights(output_embeddings, self.get_input_embeddings())
 
-        if self.config.is_encoder_decoder and self.config.tie_encoder_decoder:
+        if getattr(self.config, "is_encoder_decoder", False) and getattr(self.config, "tie_encoder_decoder", False):
             if hasattr(self, self.base_model_prefix):
                 self = getattr(self, self.base_model_prefix)
             self._tie_encoder_decoder_weights(self.encoder, self.decoder, self.base_model_prefix)

tests/test_modeling_common.py

@@ -59,14 +59,14 @@
 
 sys.path.append(str(Path(__file__).parent.parent / "utils"))
 
-from test_module.custom_configuration import CustomConfig  # noqa E402
+from test_module.custom_configuration import CustomConfig, NoSuperInitConfig  # noqa E402
 
 
 if is_torch_available():
     import torch
     from torch import nn
 
-    from test_module.custom_modeling import CustomModel
+    from test_module.custom_modeling import CustomModel, NoSuperInitModel
     from transformers import (
         BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
         MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
@@ -2091,6 +2091,15 @@ def test_model_from_pretrained_torch_dtype(self):
         model = AutoModel.from_pretrained(TINY_T5, torch_dtype=torch.float16)
         self.assertEqual(model.dtype, torch.float16)
 
+    def test_no_super_init_config_and_model(self):
+        config = NoSuperInitConfig(attribute=32)
+        model = NoSuperInitModel(config)
+
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            model.save_pretrained(tmp_dir)
+
+            model = NoSuperInitModel.from_pretrained(tmp_dir)
+
 
 @require_torch
 @is_staging_test

utils/test_module/custom_configuration.py

@@ -7,3 +7,10 @@ class CustomConfig(PretrainedConfig):
     def __init__(self, attribute=1, **kwargs):
         self.attribute = attribute
         super().__init__(**kwargs)
+
+
+class NoSuperInitConfig(PretrainedConfig):
+    model_type = "custom"
+
+    def __init__(self, attribute=1, **kwargs):
+        self.attribute = attribute

utils/test_module/custom_modeling.py

@@ -2,7 +2,7 @@
 
 from transformers import PreTrainedModel
 
-from .custom_configuration import CustomConfig
+from .custom_configuration import CustomConfig, NoSuperInitConfig
 
 
 class CustomModel(PreTrainedModel):
@@ -18,3 +18,18 @@ def forward(self, x):
 
     def _init_weights(self, module):
         pass
+
+
+class NoSuperInitModel(PreTrainedModel):
+    config_class = NoSuperInitConfig
+    base_model_prefix = "custom"
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.linear = torch.nn.Linear(config.attribute, config.attribute)
+
+    def forward(self, x):
+        return self.linear(x)
+
+    def _init_weights(self, module):
+        pass