try appending version to already saved ckpt_file

Author: rohitgr7

pytorch_lightning/callbacks/model_checkpoint.py

@@ -504,8 +504,24 @@ def _get_metric_interpolated_filepath_name(
     ) -> str:
         filepath = self.format_checkpoint_name(epoch, step, ckpt_name_metrics)
 
-        version_cnt = 0
-        while self._fs.exists(filepath) and filepath != del_filepath:
+        version_cnt = 1
+        old_ckpt_ver_0 = self.format_checkpoint_name(epoch, step, ckpt_name_metrics, ver=0)
+        while (
+            self._fs.exists(filepath)
+            or (self._fs.exists(old_ckpt_ver_0) and version_cnt == 1)
+        ):
+            if del_filepath == filepath:
+                return filepath
+
+            if del_filepath == old_ckpt_ver_0:
+                return old_ckpt_ver_0
+
+            if self._fs.exists(filepath):
+                self._fs.rename(filepath, old_ckpt_ver_0)
+                old_ckpt_score = self.best_k_models[filepath]
+                self.best_k_models.pop(filepath)
+                self.best_k_models[old_ckpt_ver_0] = old_ckpt_score
+
             filepath = self.format_checkpoint_name(epoch, step, ckpt_name_metrics, ver=version_cnt)
             version_cnt += 1
 
@@ -523,10 +539,6 @@ def _save_last_checkpoint(self, trainer, pl_module, ckpt_name_metrics):
         if not should_save_last:
             return
 
-        last_filepath = self._get_metric_interpolated_filepath_name(
-            ckpt_name_metrics, trainer.current_epoch, trainer.global_step
-        )
-
         # when user ALSO asked for the 'last.ckpt' change the name
         if self.save_last:
             last_filepath = self._format_checkpoint_name(
@@ -537,6 +549,10 @@ def _save_last_checkpoint(self, trainer, pl_module, ckpt_name_metrics):
                 prefix=self.prefix
             )
             last_filepath = os.path.join(self.dirpath, f"{last_filepath}.ckpt")
+        else:
+            last_filepath = self._get_metric_interpolated_filepath_name(
+                ckpt_name_metrics, trainer.current_epoch, trainer.global_step
+            )
 
         self._save_model(last_filepath, trainer, pl_module)
         if (

tests/checkpointing/test_model_checkpoint.py

@@ -993,7 +993,7 @@ def __init__(self, hparams):
     'save_top_k, expected',
     [
         (1, ['curr_epoch.ckpt']),
-        (2, ['curr_epoch.ckpt', 'curr_epoch-v0.ckpt']),
+        (2, ['curr_epoch-v0.ckpt', 'curr_epoch-v1.ckpt']),
     ]
 )
 def test_model_checkpoint_file_already_exists(tmpdir, max_epochs, save_top_k, expected):
@@ -1026,3 +1026,6 @@ def validation_epoch_end(self, outputs):
     trainer.fit(model)
     ckpt_files = os.listdir(tmpdir)
     assert set(ckpt_files) == set(expected)
+
+    expected_epoch_in_files = sorted([pl_load(os.path.join(tmpdir, f))['epoch'] - 1 for f in ckpt_files])
+    assert expected_epoch_in_files == sorted(list(range(max_epochs))[-save_top_k:])