Handle edge case in Fabric.setup() when model has no parameters (#1…

…7441)

(cherry picked from commit 0631fa0)

src/lightning/fabric/CHANGELOG.md

@@ -17,6 +17,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed an issue with `LightningModule.*_step` methods bypassing the DDP/FSDP wrapper ([#17424](https://github.com/Lightning-AI/lightning/pull/17424))
 
 
+- Fixed device handling in `Fabric.setup()` when the model has no parameters ([#17441](https://github.com/Lightning-AI/lightning/pull/17441))
+
+
 ## [2.0.1] - 2023-03-30
 
 ### Changed

src/lightning/fabric/fabric.py

@@ -204,7 +204,7 @@ def setup(
         module = _FabricModule(module, self._precision, original_module=original_module)
 
         # Update the _DeviceDtypeModuleMixin's device parameter
-        module.to(self.device if move_to_device else next(module.parameters()).device)
+        module.to(self.device if move_to_device else next(module.parameters(), torch.tensor(0)).device)
 
         optimizers = [_FabricOptimizer(optimizer=optimizer, strategy=self._strategy) for optimizer in optimizers]
 
@@ -248,7 +248,7 @@ def setup_module(self, module: nn.Module, move_to_device: bool = True) -> _Fabri
 
         if not isinstance(self._strategy, FSDPStrategy):
             # Update the _DeviceDtypeModuleMixin's device parameter
-            module.to(self.device if move_to_device else next(module.parameters()).device)
+            module.to(self.device if move_to_device else next(module.parameters(), torch.tensor(0)).device)
 
         if hasattr(original_module, "_fabric"):  # this is probably a LightningModule
             original_module._fabric = self  # type: ignore[assignment]
@@ -741,7 +741,7 @@ def _run_with_setup(self, run_function: Callable, *args: Any, **kwargs: Any) ->
             return run_function(*args, **kwargs)
 
     def _move_model_to_device(self, model: nn.Module, optimizers: List[Optimizer]) -> nn.Module:
-        initial_device = next(model.parameters()).device
+        initial_device = next(model.parameters(), torch.tensor(0)).device
         if any(param.device != initial_device for param in model.parameters()):
             rank_zero_warn(
                 "The model passed to `Fabric.setup()` has parameters on different devices. Since `move_to_device=True`,"

tests/tests_fabric/test_fabric.py

@@ -137,6 +137,11 @@ def test_setup_module_move_to_device(setup_method, move_to_device, accelerator,
     assert fabric_model.device == expected_device
     assert fabric.device == target_device
 
+    # edge case: model has no parameters
+    model = nn.Sequential()
+    fabric_model = setup_method(model, move_to_device=move_to_device)
+    assert fabric_model.device == target_device if move_to_device else torch.device("cpu")
+
 
 @RunIf(min_cuda_gpus=1)
 @pytest.mark.parametrize("move_to_device", [True, False])