unified tokenizer api and serialization + tests

pytorch_transformers/modeling_utils.py

@@ -169,6 +169,22 @@ class PreTrainedModel(nn.Module):
         model_to_prune = getattr(self, self.base_model_prefix, self)  # get the base model if needed
         model_to_prune._prune_heads(heads_to_prune)
 
+    def save_pretrained(self, save_directory):
+        """ Save a model with its 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"
+
+        # Only save the model it-self if we are using distributed training
+        model_to_save = self.module if hasattr(self, 'module') else self
+
+        # 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):
         """