Better error message when trying to re-initialize CUDA in forked subprocess

src/lightning_lite/strategies/launchers/multiprocessing.py

@@ -24,7 +24,7 @@
 from lightning_lite.strategies.launchers.base import _Launcher
 from lightning_lite.strategies.strategy import Strategy
 from lightning_lite.utilities.apply_func import move_data_to_device
-from lightning_lite.utilities.imports import _TORCH_GREATER_EQUAL_1_11
+from lightning_lite.utilities.imports import _IS_INTERACTIVE, _TORCH_GREATER_EQUAL_1_11
 from lightning_lite.utilities.seed import _collect_rng_states, _set_rng_states
 
 
@@ -82,6 +82,9 @@ def launch(self, function: Callable, *args: Any, **kwargs: Any) -> Any:
             *args: Optional positional arguments to be passed to the given function.
             **kwargs: Optional keyword arguments to be passed to the given function.
         """
+        if self._start_method in ("fork", "forkserver"):
+            _check_bad_cuda_fork()
+
         # The default cluster environment in Lightning chooses a random free port number
         # This needs to be done in the main process here before starting processes to ensure each rank will connect
         # through the same port
@@ -166,3 +169,22 @@ def restore(self) -> None:
             torch.use_deterministic_algorithms(self.use_deterministic_algorithms)
         torch.backends.cudnn.benchmark = self.cudnn_benchmark
         _set_rng_states(self.rng_states)
+
+
+def _check_bad_cuda_fork() -> None:
+    """Checks whether it is safe to fork and initialize CUDA in the new processes, and raises an exception if not.
+
+    The error message replaces PyTorch's 'Cannot re-initialize CUDA in forked subprocess' with helpful advice for
+    Lightning users.
+    """
+    if not torch.cuda.is_initialized():
+        return
+
+    message = (
+        "Lightning can't create new processes if CUDA is already initialized. Did you manually call"
+        " `torch.cuda.*` functions, have moved the model to the device, or allocated memory on the GPU any"
+        " other way? Please remove any such calls, or change the selected strategy."
+    )
+    if _IS_INTERACTIVE:
+        message += " You will have to restart the Python kernel."
+    raise RuntimeError(message)

src/pytorch_lightning/CHANGELOG.md

@@ -47,6 +47,10 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 
 
+- Added a more descriptive error message when attempting to fork processes with pre-initialized CUDA context ([#14709](https://github.com/Lightning-AI/lightning/issues/14709))
+
+
+
 ### Changed
 
 - The `Trainer.{fit,validate,test,predict,tune}` methods now raise a useful error message if the input is not a `LightningModule` ([#13892](https://github.com/Lightning-AI/lightning/pull/13892))

src/pytorch_lightning/strategies/launchers/multiprocessing.py

@@ -27,6 +27,7 @@
 
 import pytorch_lightning as pl
 from lightning_lite.strategies.launchers.base import _Launcher
+from lightning_lite.strategies.launchers.multiprocessing import _check_bad_cuda_fork
 from lightning_lite.utilities import move_data_to_device
 from lightning_lite.utilities.seed import _collect_rng_states, _set_rng_states
 from lightning_lite.utilities.types import _PATH
@@ -90,6 +91,9 @@ def launch(self, function: Callable, *args: Any, trainer: Optional["pl.Trainer"]
             **kwargs: Optional keyword arguments to be passed to the given function.
         """
         self._check_torchdistx_support()
+        if self._start_method in ("fork", "forkserver"):
+            _check_bad_cuda_fork()
+
         # The default cluster environment in Lightning chooses a random free port number
         # This needs to be done in the main process here before starting processes to ensure each rank will connect
         # through the same port

tests/tests_lite/strategies/launchers/test_multiprocessing.py

@@ -84,3 +84,13 @@ def test_global_state_snapshot():
     assert torch.are_deterministic_algorithms_enabled()
     assert not torch.backends.cudnn.benchmark
     assert torch.initial_seed() == 123
+
+
+@pytest.mark.parametrize("start_method", ["fork", "forkserver"])
+@mock.patch("torch.cuda.is_initialized", return_value=True)
+@mock.patch("lightning_lite.strategies.launchers.multiprocessing.mp")
+def test_multiprocessing_launcher_check_for_bad_cuda_fork(mp_mock, _, start_method):
+    mp_mock.get_all_start_methods.return_value = [start_method]
+    launcher = _MultiProcessingLauncher(strategy=Mock(), start_method=start_method)
+    with pytest.raises(RuntimeError, match="Lightning can't create new processes if CUDA is already initialized"):
+        launcher.launch(function=Mock())