Fix usage of fs.listdir in CheckpointConnector

src/pytorch_lightning/CHANGELOG.md

@@ -50,6 +50,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Fixed an attribute error in `ColossalAIStrategy` at import time when `torch.distributed` is not available ([#15535](https://github.com/Lightning-AI/lightning/pull/15535))
 
+- Fixed an issue when calling `fs.listdir` with file URI instead of path in `CheckpointConnector` ([#15413](https://github.com/Lightning-AI/lightning/pull/15413))
+
 - Fixed an issue with the `BaseFinetuning` callback not setting the `track_running_stats` attribute for batch normaliztion layers ([#15063](https://github.com/Lightning-AI/lightning/pull/15063))
 
 

src/pytorch_lightning/trainer/connectors/checkpoint_connector.py

@@ -19,6 +19,8 @@
 from typing import Any, Dict, Optional
 
 import torch
+from fsspec.core import url_to_fs
+from fsspec.implementations.local import LocalFileSystem
 from torch import Tensor
 from torchmetrics import Metric
 
@@ -59,13 +61,16 @@ def __init__(self, trainer: "pl.Trainer", resume_from_checkpoint: Optional[_PATH
     @property
     def _hpc_resume_path(self) -> Optional[str]:
         dir_path_hpc = self.trainer.default_root_dir
-        fs = get_filesystem(dir_path_hpc)
-        if not fs.isdir(dir_path_hpc):
-            return None
         dir_path_hpc = str(dir_path_hpc)
+        fs, path = url_to_fs(dir_path_hpc)
+        if not fs.isdir(path):
+            return None
         max_version = self.__max_ckpt_version_in_folder(dir_path_hpc, "hpc_ckpt_")
         if max_version is not None:
-            return os.path.join(dir_path_hpc, f"hpc_ckpt_{max_version}.ckpt")
+            if isinstance(fs, LocalFileSystem):
+                return os.path.join(dir_path_hpc, f"hpc_ckpt_{max_version}.ckpt")
+            else:
+                return dir_path_hpc + fs.sep + f"hpc_ckpt_{max_version}.ckpt"
 
     def resume_start(self, checkpoint_path: Optional[_PATH] = None) -> None:
         """Attempts to pre-load the checkpoint file to memory, with the source path determined in this priority:
@@ -565,12 +570,12 @@ def __max_ckpt_version_in_folder(dir_path: _PATH, name_key: str = "ckpt_") -> Op
         """
 
         # check directory existence
-        fs = get_filesystem(dir_path)
+        fs, uri = url_to_fs(str(dir_path))
         if not fs.exists(dir_path):
             return None
 
         # check corresponding file existence
-        files = [os.path.basename(f["name"]) for f in fs.listdir(dir_path)]
+        files = [os.path.basename(f["name"]) for f in fs.listdir(uri)]
         files = [x for x in files if name_key in x]
         if len(files) == 0:
             return None

tests/tests_pytorch/trainer/connectors/test_checkpoint_connector.py

@@ -102,6 +102,30 @@ def test_hpc_max_ckpt_version(tmpdir):
     )
 
 
+def test_ckpt_for_fsspec():
+    """Test that the CheckpointConnector is able to write to fsspec file systems."""
+
+    model = BoringModel()
+    # hardcoding dir since `tmpdir` can be windows path
+    trainer = Trainer(
+        default_root_dir="memory://test_ckpt_for_fsspec", limit_train_batches=1, limit_val_batches=1, max_epochs=1
+    )
+    trainer.fit(model)
+    trainer.save_checkpoint("memory://test_ckpt_for_fsspec/hpc_ckpt.ckpt")
+    trainer.save_checkpoint("memory://test_ckpt_for_fsspec/hpc_ckpt_0.ckpt")
+    trainer.save_checkpoint("memory://test_ckpt_for_fsspec/hpc_ckpt_3.ckpt")
+    trainer.save_checkpoint("memory://test_ckpt_for_fsspec/hpc_ckpt_33.ckpt")
+
+    assert trainer._checkpoint_connector._hpc_resume_path == "memory://test_ckpt_for_fsspec/hpc_ckpt_33.ckpt"
+    assert (
+        trainer._checkpoint_connector._CheckpointConnector__max_ckpt_version_in_folder("memory://test_ckpt_for_fsspec")
+        == 33
+    )
+    assert (
+        trainer._checkpoint_connector._CheckpointConnector__max_ckpt_version_in_folder("memory://not_existing") is None
+    )
+
+
 def test_loops_restore(tmpdir):
     """Test that required loop state_dict is loaded correctly by checkpoint connector."""
     model = BoringModel()