Merge branch 'master' into readme/pip-badge

docs/source-pytorch/upgrade/sections/1_8_regular.rst

@@ -11,7 +11,7 @@
      - `PR12804`_
 
    * - used ``Trainer.reset_train_val_dataloaders()``
-     - call ``Trainer.reset_train_dataloaders()`` and ``Trainer.reset_val_dataloaders()`` separately
+     - call ``Trainer.fit_loop.setup_data()``
      - `PR12184`_
 
    * - imported ``pl.core.lightning``

src/lightning/fabric/strategies/launchers/multiprocessing.py

@@ -15,6 +15,7 @@
 import os
 from dataclasses import dataclass
 from multiprocessing.queues import SimpleQueue
+from textwrap import dedent
 from typing import Any, Callable, Dict, Literal, Optional, TYPE_CHECKING
 
 import torch
@@ -91,6 +92,8 @@ def launch(self, function: Callable, *args: Any, **kwargs: Any) -> Any:
         """
         if self._start_method in ("fork", "forkserver"):
             _check_bad_cuda_fork()
+        if self._start_method == "spawn":
+            _check_missing_main_guard()
 
         # 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
@@ -216,3 +219,25 @@ def unshare(module: Module) -> Module:
         return module
 
     return apply_to_collection(data, function=unshare, dtype=Module)
+
+
+def _check_missing_main_guard() -> None:
+    """Raises an exception if the ``__name__ == "__main__"`` guard is missing."""
+    if not getattr(mp.current_process(), "_inheriting", False):
+        return
+    message = dedent(
+        """
+        Launching multiple processes with the 'spawn' start method requires that your script guards the main
+        function with an `if __name__ == \"__main__\"` clause. For example:
+
+        def main():
+            # Put your code here
+            ...
+
+        if __name__ == "__main__":
+            main()
+
+        Alternatively, you can run with `strategy="ddp"` to avoid this error.
+        """
+    )
+    raise RuntimeError(message)

src/lightning/pytorch/strategies/launchers/multiprocessing.py

@@ -27,7 +27,11 @@
 from torch import Tensor
 
 import lightning.pytorch as pl
-from lightning.fabric.strategies.launchers.multiprocessing import _check_bad_cuda_fork, _disable_module_memory_sharing
+from lightning.fabric.strategies.launchers.multiprocessing import (
+    _check_bad_cuda_fork,
+    _check_missing_main_guard,
+    _disable_module_memory_sharing,
+)
 from lightning.fabric.utilities import move_data_to_device
 from lightning.fabric.utilities.seed import _collect_rng_states, _set_rng_states
 from lightning.fabric.utilities.types import _PATH
@@ -99,6 +103,8 @@ def launch(self, function: Callable, *args: Any, trainer: Optional["pl.Trainer"]
         """
         if self._start_method in ("fork", "forkserver"):
             _check_bad_cuda_fork()
+        if self._start_method == "spawn":
+            _check_missing_main_guard()
 
         # 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

tests/tests_fabric/strategies/launchers/test_multiprocessing.py

@@ -36,7 +36,8 @@ def test_forking_on_unsupported_platform(_):
 
 @pytest.mark.parametrize("start_method", ["spawn", pytest.param("fork", marks=RunIf(standalone=True))])
 @mock.patch("lightning.fabric.strategies.launchers.multiprocessing.mp")
-def test_start_method(mp_mock, start_method):
+@mock.patch("lightning.fabric.strategies.launchers.multiprocessing._check_missing_main_guard")
+def test_start_method(_, mp_mock, start_method):
     mp_mock.get_all_start_methods.return_value = [start_method]
     launcher = _MultiProcessingLauncher(strategy=Mock(), start_method=start_method)
     launcher.launch(function=Mock())
@@ -51,7 +52,8 @@ def test_start_method(mp_mock, start_method):
 
 @pytest.mark.parametrize("start_method", ["spawn", pytest.param("fork", marks=RunIf(standalone=True))])
 @mock.patch("lightning.fabric.strategies.launchers.multiprocessing.mp")
-def test_restore_globals(mp_mock, start_method):
+@mock.patch("lightning.fabric.strategies.launchers.multiprocessing._check_missing_main_guard")
+def test_restore_globals(_, mp_mock, start_method):
     """Test that we pass the global state snapshot to the worker function only if we are starting with 'spawn'."""
     mp_mock.get_all_start_methods.return_value = [start_method]
     launcher = _MultiProcessingLauncher(strategy=Mock(), start_method=start_method)
@@ -94,3 +96,12 @@ def test_check_for_bad_cuda_fork(mp_mock, _, 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())
+
+
+def test_check_for_missing_main_guard():
+    launcher = _MultiProcessingLauncher(strategy=Mock(), start_method="spawn")
+    with mock.patch(
+        "lightning.fabric.strategies.launchers.multiprocessing.mp.current_process",
+        return_value=Mock(_inheriting=True),  # pretend that main is importing itself
+    ), pytest.raises(RuntimeError, match="requires that your script guards the main"):
+        launcher.launch(function=Mock())

tests/tests_pytorch/strategies/launchers/test_multiprocessing.py

@@ -203,3 +203,12 @@ def test_memory_sharing_disabled():
 
     trainer = Trainer(accelerator="cpu", devices=2, strategy="ddp_spawn", max_steps=0)
     trainer.fit(model)
+
+
+def test_check_for_missing_main_guard():
+    launcher = _MultiProcessingLauncher(strategy=Mock(), start_method="spawn")
+    with mock.patch(
+        "lightning.pytorch.strategies.launchers.multiprocessing.mp.current_process",
+        return_value=Mock(_inheriting=True),  # pretend that main is importing itself
+    ), pytest.raises(RuntimeError, match="requires that your script guards the main"):
+        launcher.launch(function=Mock())