Delete TensorBoardLogger experiment before spawning the processes.

CHANGELOG.md

@@ -205,6 +205,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed `_compare_version` for python packages ([#10762](https://github.com/PyTorchLightning/pytorch-lightning/pull/10762))
 
 
+- Fixed TensorBoardLogger `SummaryWriter` not close before spawning the processes ([#10777](https://github.com/PyTorchLightning/pytorch-lightning/pull/10777))
+
+
 ## [1.5.2] - 2021-11-16
 
 ### Fixed

pytorch_lightning/plugins/training_type/ddp_spawn.py

@@ -25,6 +25,7 @@
 from torch.nn.parallel.distributed import DistributedDataParallel
 
 import pytorch_lightning as pl
+from pytorch_lightning.loggers import LoggerCollection, TensorBoardLogger
 from pytorch_lightning.overrides import LightningDistributedModule
 from pytorch_lightning.overrides.distributed import prepare_for_backward
 from pytorch_lightning.plugins.environments.cluster_environment import ClusterEnvironment
@@ -147,14 +148,17 @@ def get_mp_spawn_kwargs(self, trainer: Optional["pl.Trainer"] = None) -> Dict[st
         return {"nprocs": self.num_processes}
 
     def start_training(self, trainer: "pl.Trainer") -> None:
+        self._clean_logger(trainer)
         self.spawn(self.new_process, trainer, self.mp_queue, return_result=False)
         # reset optimizers, since main process is never used for training and thus does not have a valid optim state
         trainer.optimizers = []
 
     def start_evaluating(self, trainer: "pl.Trainer") -> None:
+        self._clean_logger(trainer)
         self.spawn(self.new_process, trainer, self.mp_queue, return_result=False)
 
     def start_predicting(self, trainer: "pl.Trainer") -> None:
+        self._clean_logger(trainer)
         self.spawn(self.new_process, trainer, self.mp_queue, return_result=False)
 
     def spawn(self, function: Callable, *args: Any, return_result: bool = True, **kwargs: Any) -> Optional[Any]:
@@ -415,3 +419,16 @@ def teardown(self) -> None:
             self.lightning_module.cpu()
             # clean up memory
             torch.cuda.empty_cache()
+
+    @staticmethod
+    def _clean_logger(trainer: "pl.Trainer") -> None:
+        loggers = trainer.logger._logger_iterable if isinstance(trainer.logger, LoggerCollection) else [trainer.logger]
+        for logger in loggers:
+            if isinstance(logger, TensorBoardLogger) and logger._experiment is not None:
+                rank_zero_warn(
+                    "When using `ddp_spawn`, the `TensorBoardLogger` experiment should be `None`. Setting it to `None`."
+                )
+                # the experiment class of `TensorBoard` holds a multiprocessing queue which can make ours hang.
+                # we want to make sure these are closed before we spawn our own threads.
+                # assuming nothing else references the experiment object, python should instantly `__del__` it.
+                logger._experiment = None

pytorch_lightning/plugins/training_type/tpu_spawn.py

@@ -258,10 +258,6 @@ def reduce(self, output, group: Optional[Any] = None, reduce_op: Optional[Union[
 
         return output
 
-    def _close_logger(self, trainer) -> None:
-        if trainer.logger is not None:
-            trainer.logger.finalize("success")
-
     def get_mp_spawn_kwargs(self, trainer: Optional["pl.Trainer"] = None) -> Dict[str, Any]:
         return {
             "nprocs": len(self.parallel_devices),
@@ -297,13 +293,8 @@ def start_training(self, trainer: "pl.Trainer") -> None:
         # todo: precision pluging is call in accelerator setup and should be moved
         if "XLA_USE_BF16" in os.environ:
             del os.environ["XLA_USE_BF16"]
-        self._close_logger(trainer)
         return super().start_training(trainer)
 
-    def start_evaluating(self, trainer: "pl.Trainer") -> None:
-        self._close_logger(trainer)
-        return super().start_evaluating(trainer)
-
     def training_step(self, *args, **kwargs):
         return self.model(*args, **kwargs)
 

tests/loggers/test_tensorboard.py

@@ -25,6 +25,7 @@
 
 from pytorch_lightning import Trainer
 from pytorch_lightning.loggers import TensorBoardLogger
+from pytorch_lightning.loggers.base import LoggerCollection
 from pytorch_lightning.utilities.imports import _compare_version
 from tests.helpers import BoringModel
 
@@ -332,3 +333,17 @@ def test_tensorboard_missing_folder_warning(tmpdir, caplog):
         assert logger.version == 0
 
     assert "Missing logger folder:" in caplog.text
+
+
+@pytest.mark.parametrize("use_list", [False, True])
+def test_tensorboard_ddp_spawn_cleanup(use_list, tmpdir):
+    tensorboard_logger = TensorBoardLogger(save_dir=tmpdir)
+    assert tensorboard_logger._experiment is None
+    tensorboard_logger.experiment  # this property access will create the experiment
+    assert tensorboard_logger._experiment is not None
+    logger = [tensorboard_logger] if use_list else tensorboard_logger
+    trainer = Trainer(strategy="ddp_spawn", devices=2, accelerator="auto", logger=logger)
+    trainer.training_type_plugin._clean_logger(trainer)
+    if use_list:
+        assert isinstance(trainer.logger, LoggerCollection)
+    assert tensorboard_logger._experiment is None