Optimize non-empty directory warning check in model checkpoint callback (#9615)

* pt1 dir empty check

* clean imports

* bring back resolve mkdir?

* original doc

* warningcache

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* cp callback after resolve

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* move global_zero check outside warn fn

Co-authored-by: ananthsub <ananth.subramaniam@gmail.com>

* move global_zero check outside warn fn 2

Co-authored-by: ananthsub <ananth.subramaniam@gmail.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: ananthsub <ananth.subramaniam@gmail.com>

pytorch_lightning/callbacks/model_checkpoint.py

@@ -24,8 +24,7 @@ import re
 import time
 from copy import deepcopy
 from datetime import timedelta
-from pathlib import Path
-from typing import Any, Dict, Optional, Union
+from typing import Any, Dict, Optional
 from weakref import proxy
 
 import numpy as np
@@ -37,7 +36,7 @@ from pytorch_lightning.callbacks.base import Callback
 from pytorch_lightning.utilities import rank_zero_deprecation, rank_zero_info, rank_zero_warn
 from pytorch_lightning.utilities.cloud_io import get_filesystem
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
-from pytorch_lightning.utilities.types import _METRIC, STEP_OUTPUT
+from pytorch_lightning.utilities.types import _METRIC, _PATH, STEP_OUTPUT
 from pytorch_lightning.utilities.warnings import WarningCache
 
 log = logging.getLogger(__name__)
@@ -203,7 +202,7 @@ class ModelCheckpoint(Callback):
 
     def __init__(
         self,
-        dirpath: Optional[Union[str, Path]] = None,
+        dirpath: Optional[_PATH] = None,
         filename: Optional[str] = None,
         monitor: Optional[str] = None,
         verbose: bool = False,
@@ -267,6 +266,8 @@ class ModelCheckpoint(Callback):
     def on_pretrain_routine_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
         """When pretrain routine starts we build the ckpt dir on the fly."""
         self.__resolve_ckpt_dir(trainer)
+        if trainer.is_global_zero:
+            self.__warn_if_dir_not_empty(self.dirpath)
 
     def on_train_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
         self._last_time_checked = time.monotonic()
@@ -440,11 +441,8 @@ class ModelCheckpoint(Callback):
                     " will duplicate the last checkpoint saved."
                 )
 
-    def __init_ckpt_dir(self, dirpath: Optional[Union[str, Path]], filename: Optional[str]) -> None:
-        self._fs = get_filesystem(str(dirpath) if dirpath else "")
-
-        if self.save_top_k != 0 and dirpath is not None and self._fs.isdir(dirpath) and len(self._fs.ls(dirpath)) > 0:
-            rank_zero_warn(f"Checkpoint directory {dirpath} exists and is not empty.")
+    def __init_ckpt_dir(self, dirpath: Optional[_PATH], filename: Optional[str]) -> None:
+        self._fs = get_filesystem(dirpath if dirpath else "")
 
         if dirpath and self._fs.protocol == "file":
             dirpath = os.path.realpath(dirpath)
@@ -619,6 +617,10 @@ class ModelCheckpoint(Callback):
         if not trainer.fast_dev_run and trainer.should_rank_save_checkpoint:
             self._fs.makedirs(self.dirpath, exist_ok=True)
 
+    def __warn_if_dir_not_empty(self, dirpath: _PATH) -> None:
+        if self.save_top_k != 0 and self._fs.isdir(dirpath) and len(self._fs.ls(dirpath)) > 0:
+            rank_zero_warn(f"Checkpoint directory {dirpath} exists and is not empty.")
+
     def _validate_monitor_key(self, trainer: "pl.Trainer") -> None:
         metrics = trainer.callback_metrics
 
@@ -735,7 +737,7 @@ class ModelCheckpoint(Callback):
         if del_filepath is not None and filepath != del_filepath:
             trainer.training_type_plugin.remove_checkpoint(del_filepath)
 
-    def to_yaml(self, filepath: Optional[Union[str, Path]] = None) -> None:
+    def to_yaml(self, filepath: Optional[_PATH] = None) -> None:
         """Saves the `best_k_models` dict containing the checkpoint paths with the corresponding scores to a YAML
         file."""
         best_k = {k: v.item() for k, v in self.best_k_models.items()}
@@ -744,7 +746,7 @@ class ModelCheckpoint(Callback):
         with self._fs.open(filepath, "w") as fp:
             yaml.dump(best_k, fp)
 
-    def file_exists(self, filepath: Union[str, Path], trainer: "pl.Trainer") -> bool:
+    def file_exists(self, filepath: _PATH, trainer: "pl.Trainer") -> bool:
         """Checks if a file exists on rank 0 and broadcasts the result to all other ranks, preventing the internal
         state to diverge between ranks."""
         exists = self._fs.exists(filepath)