fix: AccumulateGrad stream mismatch warning when using DDP with Fabric & Trainer

src/lightning/fabric/CHANGELOG.md

@@ -20,7 +20,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Fixed
 
--
+- fixed AccumulateGrad stream mismatch warning when using DDP with Fabric ([#21746](https://github.com/Lightning-AI/pytorch-lightning/pull/21746))
+
 
 ---
 

src/lightning/fabric/strategies/ddp.py

@@ -124,8 +124,20 @@ def setup_environment(self) -> None:
     def setup_module(self, module: Module) -> DistributedDataParallel:
         """Wraps the model into a :class:`~torch.nn.parallel.distributed.DistributedDataParallel` module."""
         device_ids = self._determine_ddp_device_ids()
+        ctx: Union[torch.cuda.StreamContext, nullcontext] = nullcontext()
+
         # https://pytorch.org/docs/stable/notes/cuda.html#id5
-        ctx = torch.cuda.stream(torch.cuda.Stream()) if device_ids is not None else nullcontext()
+        if device_ids is not None:
+            capturing = torch.cuda.is_current_stream_capturing()
+            if capturing:
+                # DDP must be initialized on a side-stream for CUDA graph whole-network capture.
+                # The resulting AccumulateGrad stream mismatch is intentional in this case.
+                # See: https://pytorch.org/docs/stable/notes/cuda.html#id5
+                ctx = torch.cuda.stream(torch.cuda.Stream())
+                torch.autograd.graph.set_warn_on_accumulate_grad_stream_mismatch(False)
+            else:
+                # Default stream avoids AccumulateGrad stream mismatch warnings during normal training.
+                ctx = torch.cuda.stream(torch.cuda.default_stream())
         with ctx:
             return DistributedDataParallel(module=module, device_ids=device_ids, **self._ddp_kwargs)
 

src/lightning/pytorch/CHANGELOG.md

@@ -26,6 +26,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Fixed `SIGTERMException` producing a zero exit code instead of 143 (128 + SIGTERM) ([#21623](https://github.com/Lightning-AI/pytorch-lightning/issues/21623))
 
+- fixed AccumulateGrad stream mismatch warning when using DDP with Trainer ([#21746](https://github.com/Lightning-AI/pytorch-lightning/pull/21746))
+
 ---
 
 ## [2.6.4] - 2026-05-20

src/lightning/pytorch/strategies/ddp.py

@@ -189,9 +189,21 @@ def setup(self, trainer: "pl.Trainer") -> None:
     def _setup_model(self, model: Module) -> DistributedDataParallel:
         """Wraps the model into a :class:`~torch.nn.parallel.distributed.DistributedDataParallel` module."""
         device_ids = self.determine_ddp_device_ids()
+        ctx: Union[torch.cuda.StreamContext, nullcontext] = nullcontext()
+
         log.debug(f"setting up DDP model with device ids: {device_ids}, kwargs: {self._ddp_kwargs}")
         # https://pytorch.org/docs/stable/notes/cuda.html#id5
-        ctx = torch.cuda.stream(torch.cuda.Stream()) if device_ids is not None else nullcontext()
+        if device_ids is not None:
+            capturing = torch.cuda.is_current_stream_capturing()
+            if capturing:
+                # DDP must be initialized on a side-stream for CUDA graph whole-network capture.
+                # The resulting AccumulateGrad stream mismatch is intentional in this case.
+                # See: https://pytorch.org/docs/stable/notes/cuda.html#id5
+                ctx = torch.cuda.stream(torch.cuda.Stream())
+                torch.autograd.graph.set_warn_on_accumulate_grad_stream_mismatch(False)
+            else:
+                # Default stream avoids AccumulateGrad stream mismatch warnings during normal training.
+                ctx = torch.cuda.stream(torch.cuda.default_stream())
         with ctx:
             return DistributedDataParallel(module=model, device_ids=device_ids, **self._ddp_kwargs)
 

tests/tests_fabric/strategies/test_ddp.py

@@ -146,6 +146,8 @@ def test_module_init_context(precision, expected_dtype):
 @mock.patch.dict(os.environ, {"LOCAL_RANK": "0"})
 @mock.patch("lightning.fabric.strategies.ddp.DistributedDataParallel")
 @mock.patch("torch.cuda.Stream")
+@mock.patch("torch.cuda.default_stream")
+@mock.patch("torch.cuda.is_current_stream_capturing", return_value=False)
 @mock.patch("torch.cuda.stream")
 def test_setup_with_cuda_stream(cuda_stream_mock, *_):
     model = torch.nn.Linear(2, 2)