Snapshot selected globals and restore them in spawned process

src/pytorch_lightning/CHANGELOG.md

@@ -396,6 +396,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed an issue that caused the learning rate finder to set the model's learning rate to None when no suggestion was possible ([#13845](https://github.com/Lightning-AI/lightning/pull/13845))
 
 
+- Fixed an issue causing deterministic algorighms and other globals to get reset in spawned processes ([#13921](https://github.com/Lightning-AI/lightning/pull/13921))
+
+
 - Fixed default `amp_level` for `DeepSpeedPrecisionPlugin` to `O2` ([#13897](https://github.com/PyTorchLightning/pytorch-lightning/pull/13897))
 
 

src/pytorch_lightning/strategies/ddp_spawn.py

@@ -50,7 +50,6 @@
 from pytorch_lightning.utilities.imports import _TORCH_GREATER_EQUAL_1_11
 from pytorch_lightning.utilities.optimizer import optimizers_to_device
 from pytorch_lightning.utilities.rank_zero import rank_zero_info, rank_zero_only
-from pytorch_lightning.utilities.seed import reset_seed
 from pytorch_lightning.utilities.types import PredictStep, STEP_OUTPUT, TestStep, ValidationStep
 
 log = logging.getLogger(__name__)
@@ -175,7 +174,6 @@ def set_world_ranks(self, process_idx: int = 0) -> None:
         rank_zero_only.rank = self.cluster_environment.global_rank()
 
     def _worker_setup(self, process_idx: int) -> None:
-        reset_seed()
         self.set_world_ranks(process_idx)
         rank_zero_only.rank = self.global_rank
         self._process_group_backend = self._get_process_group_backend()

src/pytorch_lightning/strategies/launchers/multiprocessing.py

@@ -13,11 +13,13 @@
 # limitations under the License.
 import os
 from collections import UserList
+from dataclasses import dataclass
 from multiprocessing.queues import SimpleQueue
-from typing import Any, Callable, NamedTuple, Optional
+from typing import Any, Callable, Dict, NamedTuple, Optional
 
 import numpy as np
 import torch
+import torch.backends.cudnn
 import torch.multiprocessing as mp
 from torch import Tensor
 from typing_extensions import Literal
@@ -27,7 +29,9 @@
 from pytorch_lightning.strategies.strategy import Strategy
 from pytorch_lightning.trainer.states import TrainerFn, TrainerState
 from pytorch_lightning.utilities.apply_func import apply_to_collection, move_data_to_device
+from pytorch_lightning.utilities.imports import _TORCH_GREATER_EQUAL_1_11
 from pytorch_lightning.utilities.rank_zero import rank_zero_debug
+from pytorch_lightning.utilities.seed import _collect_rng_states, _set_rng_states
 from pytorch_lightning.utilities.types import _PATH
 
 
@@ -89,9 +93,16 @@ def launch(self, function: Callable, *args: Any, trainer: Optional["pl.Trainer"]
         os.environ["MASTER_PORT"] = str(self._strategy.cluster_environment.main_port)
         context = mp.get_context(self._start_method)
         return_queue = context.SimpleQueue()
+
+        if self._start_method == "spawn":
+            global_states = _GlobalStateSnapshot.capture()
+            process_args = [trainer, function, args, kwargs, return_queue, global_states]
+        else:
+            process_args = [trainer, function, args, kwargs, return_queue]
+
         mp.start_processes(
             self._wrapping_function,
-            args=(trainer, function, args, kwargs, return_queue),
+            args=process_args,
             nprocs=self._strategy.num_processes,
             start_method=self._start_method,
         )
@@ -110,7 +121,10 @@ def _wrapping_function(
         args: Any,
         kwargs: Any,
         return_queue: SimpleQueue,
+        global_states: Optional["_GlobalStateSnapshot"] = None,
     ) -> None:
+        if global_states:
+            global_states.restore()
         self._strategy._worker_setup(process_idx)
         results = function(*args, **kwargs)
 
@@ -209,3 +223,50 @@ class _WorkerOutput(NamedTuple):
     trainer_state: TrainerState
     trainer_results: Any
     extra: _FakeQueue
+
+
+@dataclass
+class _GlobalStateSnapshot:
+    """Captures a hand-selected set of (global) variables in modules and provides a way to restore them.
+
+    It facilitates and encapsulates the transfer of globals like PyTorch's deterministic flags or random generator state
+    across process boundaries when launching processes with :func:`torch.multiprocessing.spawn`.
+
+    Example:
+
+        .. code-block:: python
+
+            # in main process
+            snapshot = _GlobalStateSnapshot.capture()
+
+            # in worker process
+            snapshot.restore()
+    """
+
+    use_deterministic_algorithms: bool
+    use_deterministic_algorithms_warn_only: bool
+    cudnn_benchmark: bool
+    rng_states: Dict[str, Any]
+
+    @classmethod
+    def capture(cls) -> "_GlobalStateSnapshot":
+        """Capture a few global states from torch, numpy, etc., that we want to restore in a spawned worker
+        process."""
+        warn_only = torch.is_deterministic_algorithms_warn_only_enabled() if _TORCH_GREATER_EQUAL_1_11 else False
+        return cls(
+            use_deterministic_algorithms=torch.are_deterministic_algorithms_enabled(),
+            use_deterministic_algorithms_warn_only=warn_only,
+            cudnn_benchmark=torch.backends.cudnn.benchmark,
+            rng_states=_collect_rng_states(),
+        )
+
+    def restore(self) -> None:
+        """Restores all globals to the values captured in the :meth:`capture` method."""
+        if _TORCH_GREATER_EQUAL_1_11:
+            torch.use_deterministic_algorithms(
+                self.use_deterministic_algorithms, warn_only=self.use_deterministic_algorithms_warn_only
+            )
+        else:
+            torch.use_deterministic_algorithms(self.use_deterministic_algorithms)
+        torch.backends.cudnn.benchmark = self.cudnn_benchmark
+        _set_rng_states(self.rng_states)

src/pytorch_lightning/strategies/launchers/xla.py

@@ -21,7 +21,12 @@
 from torch.multiprocessing import ProcessContext
 
 import pytorch_lightning as pl
-from pytorch_lightning.strategies.launchers.multiprocessing import _FakeQueue, _MultiProcessingLauncher, _WorkerOutput
+from pytorch_lightning.strategies.launchers.multiprocessing import (
+    _FakeQueue,
+    _GlobalStateSnapshot,
+    _MultiProcessingLauncher,
+    _WorkerOutput,
+)
 from pytorch_lightning.trainer.states import TrainerFn
 from pytorch_lightning.utilities import _TPU_AVAILABLE
 from pytorch_lightning.utilities.apply_func import move_data_to_device
@@ -96,6 +101,7 @@ def _wrapping_function(
         args: Any,
         kwargs: Any,
         return_queue: SimpleQueue,
+        global_states: Optional[_GlobalStateSnapshot] = None,
     ) -> None:
         self._strategy._worker_setup(process_idx)
         results = function(*args, **kwargs)

src/pytorch_lightning/strategies/tpu_spawn.py

@@ -37,7 +37,6 @@
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities.optimizer import optimizers_to_device
 from pytorch_lightning.utilities.rank_zero import rank_zero_only
-from pytorch_lightning.utilities.seed import reset_seed
 from pytorch_lightning.utilities.types import _PATH, STEP_OUTPUT
 
 if _TPU_AVAILABLE:
@@ -206,7 +205,6 @@ def reduce(self, output, group: Optional[Any] = None, reduce_op: Optional[Union[
 
     def _worker_setup(self, process_idx: int):
         self._launched = True
-        reset_seed()
         self.set_world_ranks(process_idx)
         rank_zero_only.rank = self.global_rank
 

tests/tests_pytorch/strategies/launchers/test_multiprocessing.py

@@ -15,19 +15,20 @@
 from unittest.mock import ANY, Mock
 
 import pytest
+import torch
 
-from pytorch_lightning.strategies.launchers.multiprocessing import _MultiProcessingLauncher
+from pytorch_lightning.strategies.launchers.multiprocessing import _GlobalStateSnapshot, _MultiProcessingLauncher
 
 
 @mock.patch("pytorch_lightning.strategies.launchers.multiprocessing.mp.get_all_start_methods", return_value=[])
-def test_spawn_launcher_forking_on_unsupported_platform(_):
+def test_multiprocessing_launcher_forking_on_unsupported_platform(_):
     with pytest.raises(ValueError, match="The start method 'fork' is not available on this platform"):
         _MultiProcessingLauncher(strategy=Mock(), start_method="fork")
 
 
 @pytest.mark.parametrize("start_method", ["spawn", "fork"])
 @mock.patch("pytorch_lightning.strategies.launchers.multiprocessing.mp")
-def test_spawn_launcher_start_method(mp_mock, start_method):
+def test_multiprocessing_launcher_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())
@@ -38,3 +39,39 @@ def test_spawn_launcher_start_method(mp_mock, start_method):
         nprocs=ANY,
         start_method=start_method,
     )
+
+
+@pytest.mark.parametrize("start_method", ["spawn", "fork"])
+@mock.patch("pytorch_lightning.strategies.launchers.multiprocessing.mp")
+def test_multiprocessing_launcher_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)
+    launcher.launch(function=Mock())
+    function_args = mp_mock.start_processes.call_args[1]["args"]
+    if start_method == "spawn":
+        assert len(function_args) == 6
+        assert isinstance(function_args[5], _GlobalStateSnapshot)
+    else:
+        assert len(function_args) == 5
+
+
+def test_global_state_snapshot():
+    """Test the capture() and restore() methods for the global state snapshot."""
+    torch.use_deterministic_algorithms(True)
+    torch.backends.cudnn.benchmark = False
+    torch.manual_seed(123)
+
+    # capture the state of globals
+    snapshot = _GlobalStateSnapshot.capture()
+
+    # simulate there is a process boundary and flags get reset here
+    torch.use_deterministic_algorithms(False)
+    torch.backends.cudnn.benchmark = True
+    torch.manual_seed(321)
+
+    # restore the state of globals
+    snapshot.restore()
+    assert torch.are_deterministic_algorithms_enabled()
+    assert not torch.backends.cudnn.benchmark
+    assert torch.initial_seed() == 123