Fix: Synchronize SIGTERM Handling in DDP to Prevent Deadlocks

src/lightning/pytorch/CHANGELOG.md

@@ -28,6 +28,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed logger_connector has edge case where step can be a float ([#20692](https://github.com/Lightning-AI/pytorch-lightning/issues/20692))
 
 
+- Fix: Synchronize SIGTERM Handling in DDP to Prevent Deadlocks ([#20825](https://github.com/Lightning-AI/pytorch-lightning/pull/20825))
+
+
 ---
 
 ## [2.5.1] - 2025-03-18

src/lightning/pytorch/loops/training_epoch_loop.py

@@ -11,11 +11,13 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import contextlib
 import math
 from collections import OrderedDict
 from dataclasses import dataclass
 from typing import Any, Optional, Union
 
+import torch
 from typing_extensions import override
 
 import lightning.pytorch as pl
@@ -249,6 +251,21 @@ def _on_before_fetch(self) -> None:
     def _on_after_fetch(self) -> None:
         self.trainer.profiler.stop(f"[{self.__class__.__name__}].train_dataloader_next")
 
+    def _broadcast_sigterm_tensor(self) -> None:
+        try:
+            sigterm_tensor = torch.tensor(
+                [1 if getattr(self.trainer, "received_sigterm", False) else 0],
+                device=self.trainer.strategy.root_device,
+            )
+            torch.distributed.broadcast(sigterm_tensor, src=0)
+        except Exception:
+            sigterm_tensor = torch.tensor([0], device=self.trainer.strategy.root_device)
+
+        if sigterm_tensor.item() == 1:
+            with contextlib.suppress(Exception):
+                torch.distributed.barrier()  # prevent deadlocks by syncing all ranks before exit
+            raise SIGTERMException()
+
     def advance(self, data_fetcher: _DataFetcher) -> None:
         """Runs a single training batch.
 
@@ -272,6 +289,13 @@ def advance(self, data_fetcher: _DataFetcher) -> None:
         # we are going to train first so the val loop does not need to restart
         self.val_loop.restarting = False
 
+        # =====================================================================
+
+        if torch.distributed.is_available() and torch.distributed.is_initialized() and self.trainer.world_size > 1:
+            self._broadcast_sigterm_tensor()
+
+        # =====================================================================
+
         if using_dataloader_iter := isinstance(data_fetcher, _DataLoaderIterDataFetcher):
             dataloader_iter = next(data_fetcher)
             # hook's batch_idx and dataloader_idx arguments correctness cannot be guaranteed in this setting

src/lightning/pytorch/trainer/connectors/callback_connector.py

@@ -106,8 +106,9 @@ def _configure_checkpoint_callbacks(self, enable_checkpointing: bool) -> None:
                 model_checkpoint = LitModelCheckpoint(model_registry=self.trainer._model_registry)
             else:
                 rank_zero_info(
-                    "Using default `ModelCheckpoint`. Consider installing `litmodels` package to enable"
-                    " `LitModelCheckpoint` for automatic upload to the Lightning model registry."
+                    "💡 Tip: For seamless cloud uploads and versioning,"
+                    " try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint,"
+                    " which syncs automatically with the Lightning model registry."
                 )
                 model_checkpoint = ModelCheckpoint()
             self.trainer.callbacks.append(model_checkpoint)

src/lightning/pytorch/trainer/connectors/signal_connector.py

@@ -7,6 +7,9 @@
 from types import FrameType
 from typing import Any, Callable, Union
 
+import torch
+import torch.distributed as dist
+
 import lightning.pytorch as pl
 from lightning.fabric.plugins.environments import SLURMEnvironment
 from lightning.fabric.utilities.imports import _IS_WINDOWS
@@ -104,12 +107,16 @@ def _slurm_sigusr_handler_fn(self, signum: _SIGNUM, _: FrameType) -> None:
 
     def _sigterm_notifier_fn(self, signum: _SIGNUM, _: FrameType) -> None:
         log.info(rank_prefixed_message(f"Received SIGTERM: {signum}", self.trainer.local_rank))
-        # subprocesses killing the parent process is not supported, only the parent (rank 0) does it
         if not self.received_sigterm:
-            # send the same signal to the subprocesses
             launcher = self.trainer.strategy.launcher
             if launcher is not None:
                 launcher.kill(signum)
+
+        # New broadcast logic
+        if dist.is_available() and dist.is_initialized() and self.trainer.world_size > 1:
+            sigterm_tensor = torch.tensor([1], device=self.trainer.strategy.root_device)
+            dist.broadcast(sigterm_tensor, src=0)
+
         self.received_sigterm = True
 
     def _sigterm_handler_fn(self, signum: _SIGNUM, _: FrameType) -> None:

tests/tests_pytorch/trainer/test_ddp_sigterm_handling.py

@@ -0,0 +1,80 @@
+import os
+import signal
+import time
+
+import pytest
+import torch
+import torch.multiprocessing as mp
+
+from lightning.pytorch import LightningModule, Trainer, seed_everything
+from lightning.pytorch.demos.boring_classes import BoringDataModule
+from lightning.pytorch.strategies.ddp import DDPStrategy
+from lightning.pytorch.utilities.exceptions import SIGTERMException
+
+# Skip the test if DDP or multiple devices are not available
+
+pytestmark = pytest.mark.skipif(
+    not torch.distributed.is_available() or torch.cuda.device_count() < 2,
+    reason="Requires torch.distributed and at least 2 CUDA devices",
+)
+
+
+class DummyModel(LightningModule):
+    def training_step(self, batch, batch_idx):
+        # Simulate SIGTERM in rank 0 at batch 2
+        if self.trainer.global_rank == 0 and batch_idx == 2:
+            time.sleep(3)  # Let other ranks proceed to the next batch
+            os.kill(os.getpid(), signal.SIGTERM)
+        return super().training_step(batch, batch_idx)
+
+
+def run_ddp_sigterm(rank, world_size, tmpdir):
+    os.environ["MASTER_ADDR"] = "localhost"
+    os.environ["MASTER_PORT"] = "12355"
+    os.environ["RANK"] = str(rank)
+    os.environ["WORLD_SIZE"] = str(world_size)
+
+    seed_everything(42)
+
+    torch.cuda.set_device(rank) if torch.cuda.is_available() else None
+
+    model = DummyModel()
+    datamodule = BoringDataModule()
+
+    trainer = Trainer(
+        accelerator="cuda" if torch.cuda.is_available() else "cpu",
+        strategy=DDPStrategy(find_unused_parameters=False),
+        devices=world_size,
+        num_nodes=1,
+        max_epochs=3,
+        default_root_dir=tmpdir,
+        enable_checkpointing=False,
+        enable_progress_bar=False,
+        enable_model_summary=False,
+        logger=False,
+    )
+
+    try:
+        trainer.fit(model, datamodule=datamodule)
+    except SIGTERMException:
+        # Test passed: SIGTERM was properly raised and caught
+        print(f"[Rank {rank}] Caught SIGTERMException successfully.")
+    except Exception as e:
+        pytest.fail(f"[Rank {rank}] Unexpected exception: {e}")
+
+
+def test_ddp_sigterm_handling(tmp_path):
+    world_size = 2
+    mp.spawn(run_ddp_sigterm, args=(world_size, tmp_path), nprocs=world_size, join=True)
+
+
+@pytest.mark.skipif(
+    not torch.distributed.is_available(),
+    reason="Requires torch.distributed",
+)
+@pytest.mark.skipif(
+    torch.cuda.is_available() and torch.cuda.device_count() < 2,
+    reason="Requires >=2 CUDA devices or use CPU",
+)
+def test_sigterm_handling_ddp(tmp_path):
+    test_ddp_sigterm_handling(tmp_path)