Make asyncio checkpointing work if validate/fit is called more than once

src/lightning/pytorch/CHANGELOG.md

@@ -31,8 +31,12 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed `AsyncCheckpointIO` snapshots tensors to avoid race with parameter mutation ([#21079](https://github.com/Lightning-AI/pytorch-lightning/pull/21079))
 
 
+- Fixed `AsyncCheckpointIO` threadpool exception if calling fit or validate more than one ([#20952](https://github.com/Lightning-AI/pytorch-lightning/pull/20952))
+
+
 - Fixed learning rate not being correctly set after using `LearningRateFinder` callback ([#21068](https://github.com/Lightning-AI/pytorch-lightning/pull/21068))
 
+
 ---
 
 ## [2.5.3] - 2025-08-13

src/lightning/pytorch/plugins/io/async_plugin.py

@@ -13,15 +13,17 @@
 # limitations under the License.
 
 from concurrent.futures import ThreadPoolExecutor
-from typing import Any, Optional
+from typing import TYPE_CHECKING, Any, Optional
 
 import torch
 from lightning_utilities.core.apply_func import apply_to_collection
 from typing_extensions import override
 
-from lightning.fabric.plugins import CheckpointIO
 from lightning.pytorch.plugins.io.wrapper import _WrappingCheckpointIO
 
+if TYPE_CHECKING:
+    from lightning.fabric.plugins import CheckpointIO
+
 
 class AsyncCheckpointIO(_WrappingCheckpointIO):
     """``AsyncCheckpointIO`` enables saving the checkpoints asynchronously in a thread.
@@ -33,20 +35,30 @@ class AsyncCheckpointIO(_WrappingCheckpointIO):
 
     """
 
+    _executor: Optional[ThreadPoolExecutor]
+    _error: Optional[BaseException]
+
     def __init__(self, checkpoint_io: Optional["CheckpointIO"] = None) -> None:
         super().__init__(checkpoint_io)
+        self._executor = None
+        self._error = None
+
+    # CheckpointIO doesn't have a setup method so we have to do something like.
+    def _ensure_setup(self) -> None:
+        """Ensures that the executor is setup.
 
-        self._executor = ThreadPoolExecutor(max_workers=1)
-        self._error: Optional[BaseException] = None
+        We can't do setup in __init__ because if train or validate is called more than once, the teardown method deletes
+        the executor.
+
+        """
+        if self._executor is None:
+            self._executor = ThreadPoolExecutor(max_workers=1)
 
     @override
     def save_checkpoint(self, *args: Any, **kwargs: Any) -> None:
         """Uses the ``ThreadPoolExecutor`` to save the checkpoints using the base ``checkpoint_io``."""
 
-        # snapshot the checkpoint payload on the caller thread to avoid races with parameter mutation
-        def _clone_tensor(t: torch.Tensor) -> torch.Tensor:
-            # detach to avoid autograd history and clone to take a point-in-time copy
-            return t.detach().clone()
+        self._ensure_setup()
 
         # rebuild args/kwargs with a cloned checkpoint (supports positional or kw form)
         if "checkpoint" in kwargs:
@@ -61,6 +73,7 @@ def _save_checkpoint(*args: Any, **kwargs: Any) -> None:
             except BaseException as ex:
                 self._error = ex
 
+        assert self._executor is not None
         self._executor.submit(_save_checkpoint, *args, **kwargs)
 
         # if an error was raised between the previous time `save_checkpoint`` was called and now,
@@ -71,8 +84,17 @@ def _save_checkpoint(*args: Any, **kwargs: Any) -> None:
     @override
     def teardown(self) -> None:
         """This method is called to close the threads."""
-        self._executor.shutdown(wait=True)
+        if self._executor is not None:
+            self._executor.shutdown(wait=True)
+            self._executor = None
 
         # if an error was raised anytime in any of the `executor.submit` calls
         if self._error:
             raise self._error
+
+
+# snapshot the checkpoint payload on the caller thread to avoid races with parameter mutation
+def _clone_tensor(t: torch.Tensor) -> torch.Tensor:
+    """Clones a tensor on the caller thread."""
+    # detach to avoid autograd history and clone to take a point-in-time copy
+    return t.detach().clone()

tests/tests_pytorch/plugins/test_checkpoint_io_plugin.py

@@ -127,6 +127,10 @@ def on_fit_start(self):
         enable_progress_bar=False,
         enable_model_summary=False,
     )
+
+    # We add a validate step to test that async works when fit or validate is called multiple times.
+    trainer.validate(model)
+
     trainer.fit(model)
 
     assert checkpoint_plugin.save_checkpoint.call_count == 3