Fix bug with rotating checkpoints (#28009)

* Fix bug

* Write test

* Keep back old modification for grad accum steps

* Whitespace...

* Whitespace again

* Race condition

* Wait for everyone

src/transformers/trainer.py

@@ -2379,8 +2379,13 @@ def _save_checkpoint(self, model, trial, metrics=None):
             self._push_from_checkpoint(staging_output_dir)
 
         # Place checkpoint in final location after all saving is finished.
+        # First wait for everyone to finish writing
+        self.args.distributed_state.wait_for_everyone()
+        # Then go through the rewriting process starting on process 0
         if staging_output_dir != output_dir:
-            os.rename(staging_output_dir, output_dir)
+            with self.args.main_process_first(desc="Renaming model checkpoint folder to true location"):
+                if os.path.exists(staging_output_dir):
+                    os.rename(staging_output_dir, output_dir)
 
         # Maybe delete some older checkpoints.
         if self.args.should_save:

tests/trainer/test_trainer_distributed.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from pathlib import Path
 from typing import Dict
 
 import numpy as np
@@ -236,6 +237,20 @@ def compute_metrics(p: EvalPrediction) -> Dict:
 
         trainer.args.eval_accumulation_steps = None
 
+    # Check that saving does indeed work with temp dir rotation
+    # If this fails, will see a FileNotFoundError
+    model = RegressionModel()
+    training_args.max_steps = 1
+    opt = torch.optim.Adam(model.parameters(), lr=1e-3)
+    sched = torch.optim.lr_scheduler.LambdaLR(opt, lambda x: 1)
+    trainer = Trainer(
+        model, training_args, optimizers=(opt, sched), data_collator=DummyDataCollator(), eval_dataset=dataset
+    )
+    trainer._save_checkpoint(model=None, trial=None)
+    # Check that the temp folder does not exist
+    assert not (Path(training_args.output_dir) / "tmp-checkpoint-0").exists()
+    assert (Path(training_args.output_dir) / "checkpoint-0").exists()
+
     # Check that `dispatch_batches=False` will work on a finite iterable dataset
 
     train_dataset = FiniteIterableDataset(label_names=["labels", "extra"], length=1)