save_pretrained: mkdir(exist_ok=True) (#5258)

* all save_pretrained methods mkdir if not os.path.exists

examples/bert-loses-patience/run_glue_with_pabee.py

@@ -226,8 +226,6 @@ def train(args, train_dataset, model, tokenizer):
                 if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
                     # Save model checkpoint
                     output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step))
-                    if not os.path.exists(output_dir):
-                        os.makedirs(output_dir)
                     model_to_save = (
                         model.module if hasattr(model, "module") else model
                     )  # Take care of distributed/parallel training
@@ -649,10 +647,6 @@ def main():
 
     # Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
     if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
-        # Create output directory if needed
-        if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
-            os.makedirs(args.output_dir)
-
         logger.info("Saving model checkpoint to %s", args.output_dir)
         # Save a trained model, configuration and tokenizer using `save_pretrained()`.
         # They can then be reloaded using `from_pretrained()`

examples/contrib/mm-imdb/run_mmimdb.py

@@ -521,10 +521,6 @@ def main():
 
     # Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
     if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
-        # Create output directory if needed
-        if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
-            os.makedirs(args.output_dir)
-
         logger.info("Saving model checkpoint to %s", args.output_dir)
         # Save a trained model, configuration and tokenizer using `save_pretrained()`.
         # They can then be reloaded using `from_pretrained()`

examples/contrib/run_swag.py

@@ -383,8 +383,6 @@ def train(args, train_dataset, model, tokenizer):
                 if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
                     # Save model checkpoint
                     output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step))
-                    if not os.path.exists(output_dir):
-                        os.makedirs(output_dir)
                     model_to_save = (
                         model.module if hasattr(model, "module") else model
                     )  # Take care of distributed/parallel training
@@ -651,10 +649,6 @@ def main():
 
     # Save the trained model and the tokenizer
     if args.local_rank == -1 or torch.distributed.get_rank() == 0:
-        # Create output directory if needed
-        if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
-            os.makedirs(args.output_dir)
-
         logger.info("Saving model checkpoint to %s", args.output_dir)
         # Save a trained model, configuration and tokenizer using `save_pretrained()`.
         # They can then be reloaded using `from_pretrained()`

examples/distillation/run_squad_w_distillation.py

@@ -809,10 +809,6 @@ def main():
 
     # Save the trained model and the tokenizer
     if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
-        # Create output directory if needed
-        if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
-            os.makedirs(args.output_dir)
-
         logger.info("Saving model checkpoint to %s", args.output_dir)
         # Save a trained model, configuration and tokenizer using `save_pretrained()`.
         # They can then be reloaded using `from_pretrained()`

examples/movement-pruning/masked_run_glue.py

@@ -875,10 +875,6 @@ def main():
 
     # Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
     if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
-        # Create output directory if needed
-        if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
-            os.makedirs(args.output_dir)
-
         logger.info("Saving model checkpoint to %s", args.output_dir)
         # Save a trained model, configuration and tokenizer using `save_pretrained()`.
         # They can then be reloaded using `from_pretrained()`

examples/movement-pruning/masked_run_squad.py

@@ -1059,10 +1059,6 @@ def main():
 
     # Save the trained model and the tokenizer
     if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
-        # Create output directory if needed
-        if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
-            os.makedirs(args.output_dir)
-
         logger.info("Saving model checkpoint to %s", args.output_dir)
         # Save a trained model, configuration and tokenizer using `save_pretrained()`.
         # They can then be reloaded using `from_pretrained()`

examples/question-answering/run_squad.py

@@ -240,8 +240,6 @@ def train(args, train_dataset, model, tokenizer):
                 # Save model checkpoint
                 if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
                     output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step))
-                    if not os.path.exists(output_dir):
-                        os.makedirs(output_dir)
                     # Take care of distributed/parallel training
                     model_to_save = model.module if hasattr(model, "module") else model
                     model_to_save.save_pretrained(output_dir)
@@ -768,10 +766,6 @@ def main():
 
     # Save the trained model and the tokenizer
     if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
-        # Create output directory if needed
-        if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
-            os.makedirs(args.output_dir)
-
         logger.info("Saving model checkpoint to %s", args.output_dir)
         # Save a trained model, configuration and tokenizer using `save_pretrained()`.
         # They can then be reloaded using `from_pretrained()`

examples/seq2seq/distillation.py

@@ -92,8 +92,6 @@ class BartSummarizationDistiller(SummarizationModule):
         student = BartForConditionalGeneration(student_cfg)
         student, _ = init_student(student, teacher)
         save_dir = self.output_dir.joinpath("student")
-        save_dir.mkdir(exist_ok=True)
-
         self.copy_to_student(d_layers_to_copy, e_layers_to_copy, hparams, student, teacher)
         student.save_pretrained(save_dir)
         hparams.model_name_or_path = str(save_dir)

examples/text-classification/run_xnli.py

@@ -573,10 +573,6 @@ def main():
 
     # Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
     if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
-        # Create output directory if needed
-        if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
-            os.makedirs(args.output_dir)
-
         logger.info("Saving model checkpoint to %s", args.output_dir)
         # Save a trained model, configuration and tokenizer using `save_pretrained()`.
         # They can then be reloaded using `from_pretrained()`

src/transformers/configuration_utils.py

@@ -132,10 +132,9 @@ class PretrainedConfig(object):
             save_directory (:obj:`string`):
                 Directory where the configuration JSON file will be saved.
         """
-        assert os.path.isdir(
-            save_directory
-        ), "Saving path should be a directory where the model and configuration can be saved"
-
+        if os.path.isfile(save_directory):
+            raise AssertionError("Provided path ({}) should be a directory, not a file".format(save_directory))
+        os.makedirs(save_directory, exist_ok=True)
         # If we save using the predefined names, we can load using `from_pretrained`
         output_config_file = os.path.join(save_directory, CONFIG_NAME)
 

src/transformers/convert_pytorch_checkpoint_to_tf2.py

@@ -240,7 +240,6 @@ def convert_all_pt_checkpoints_to_tf(
     remove_cached_files=False,
     only_convert_finetuned_models=False,
 ):
-    assert os.path.isdir(args.tf_dump_path), "--tf_dump_path should be a directory"
 
     if args_model_type is None:
         model_types = list(MODEL_CLASSES.keys())

src/transformers/modeling_tf_utils.py

@@ -315,9 +315,10 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
         """ Save a model and its configuration file to a directory, so that it
             can be re-loaded using the :func:`~transformers.PreTrainedModel.from_pretrained` class method.
         """
-        assert os.path.isdir(
-            save_directory
-        ), "Saving path should be a directory where the model and configuration can be saved"
+        if os.path.isfile(save_directory):
+            logger.error("Provided path ({}) should be a directory, not a file".format(save_directory))
+            return
+        os.makedirs(save_directory, exist_ok=True)
 
         # Save configuration file
         self.config.save_pretrained(save_directory)

src/transformers/modeling_utils.py

@@ -477,9 +477,10 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
             Arguments:
                 save_directory: directory to which to save.
         """
-        assert os.path.isdir(
-            save_directory
-        ), "Saving path should be a directory where the model and configuration can be saved"
+        if os.path.isfile(save_directory):
+            logger.error("Provided path ({}) should be a directory, not a file".format(save_directory))
+            return
+        os.makedirs(save_directory, exist_ok=True)
 
         # Only save the model itself if we are using distributed training
         model_to_save = self.module if hasattr(self, "module") else self

src/transformers/pipelines.py

@@ -405,9 +405,10 @@ class Pipeline(_ScikitCompat):
         """
         Save the pipeline's model and tokenizer to the specified save_directory
         """
-        if not os.path.isdir(save_directory):
-            logger.error("Provided path ({}) should be a directory".format(save_directory))
+        if os.path.isfile(save_directory):
+            logger.error("Provided path ({}) should be a directory, not a file".format(save_directory))
             return
+        os.makedirs(save_directory, exist_ok=True)
 
         self.model.save_pretrained(save_directory)
         self.tokenizer.save_pretrained(save_directory)

src/transformers/tokenization_utils_base.py

@@ -1343,9 +1343,10 @@ class PreTrainedTokenizerBase(SpecialTokensMixin):
             This method make sure the full tokenizer can then be re-loaded using the
             :func:`~transformers.PreTrainedTokenizer.from_pretrained` class method.
         """
-        if not os.path.isdir(save_directory):
-            logger.error("Saving directory ({}) should be a directory".format(save_directory))
+        if os.path.isfile(save_directory):
+            logger.error("Provided path ({}) should be a directory, not a file".format(save_directory))
             return
+        os.makedirs(save_directory, exist_ok=True)
 
         special_tokens_map_file = os.path.join(save_directory, SPECIAL_TOKENS_MAP_FILE)
         added_tokens_file = os.path.join(save_directory, ADDED_TOKENS_FILE)

templates/adding_a_new_example_script/run_xxx.py

@@ -653,10 +653,6 @@ def main():
 
     # Save the trained model and the tokenizer
     if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
-        # Create output directory if needed
-        if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
-            os.makedirs(args.output_dir)
-
         logger.info("Saving model checkpoint to %s", args.output_dir)
         # Save a trained model, configuration and tokenizer using `save_pretrained()`.
         # They can then be reloaded using `from_pretrained()`