save config file

pytorch_transformers/modeling_utils.py

@@ -48,6 +48,17 @@ class PretrainedConfig(object):
         self.output_hidden_states = kwargs.pop('output_hidden_states', False)
         self.torchscript = kwargs.pop('torchscript', False)
 
+    def save_pretrained(self, save_directory):
+        """ Save a configuration file to a directory, so that it
+            can be re-loaded using the `from_pretrained(save_directory)` class method.
+        """
+        assert os.path.isdir(save_directory), "Saving path should be a directory where the model and configuration can be saved"
+
+        # If we save using the predefined names, we can load using `from_pretrained`
+        output_config_file = os.path.join(save_directory, CONFIG_NAME)
+
+        self.to_json_file(output_config_file)
+
     @classmethod
     def from_pretrained(cls, pretrained_model_name_or_path, *input, **kwargs):
         """
@@ -248,12 +259,13 @@ class PreTrainedModel(nn.Module):
         # Only save the model it-self if we are using distributed training
         model_to_save = self.module if hasattr(self, 'module') else self
 
+        # Save configuration file
+        model_to_save.config.save_pretrained(save_directory)
+
         # If we save using the predefined names, we can load using `from_pretrained`
         output_model_file = os.path.join(save_directory, WEIGHTS_NAME)
-        output_config_file = os.path.join(save_directory, CONFIG_NAME)
 
         torch.save(model_to_save.state_dict(), output_model_file)
-        model_to_save.config.to_json_file(output_config_file)
 
     @classmethod
     def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs):