Revert removal of empty-parameters check for configure_optimizers()…

… with FSDP (#18785)

src/lightning/pytorch/strategies/fsdp.py

@@ -327,7 +327,18 @@ def setup(self, trainer: "pl.Trainer") -> None:
     def setup_optimizers(self, trainer: "pl.Trainer") -> None:
         if self.kwargs.get("use_orig_params"):
             return super().setup_optimizers(trainer)
-        if any(not _optimizer_has_flat_params(optimizer) for optimizer in self.optimizers):
+
+        invalid_params_error = False
+        try:
+            # In PyTorch < 2.0, or if `use_orig_params=False` the user needs to do access
+            # `self.trainer.model.parameters()` in configure_optimizers()
+            super().setup_optimizers(trainer)
+        except ValueError as ex:
+            if "optimizer got an empty parameter list" not in str(ex):
+                raise
+            invalid_params_error = True
+
+        if invalid_params_error or any(not _optimizer_has_flat_params(optimizer) for optimizer in self.optimizers):
             # We avoid this limitation in PyTorch >= 2.0 by setting `use_orig_params=True`
             raise ValueError(
                 "The optimizer does not seem to reference any FSDP parameters. HINT: Make sure to create the"

tests/tests_pytorch/strategies/test_fsdp.py

@@ -359,16 +359,22 @@ def test_fsdp_checkpoint_multi_gpus(tmpdir, model, strategy, strategy_cfg):
 
 
 @RunIf(min_cuda_gpus=1, skip_windows=True, standalone=True)
-def test_invalid_parameters_in_optimizer():
+@pytest.mark.parametrize("use_orig_params", [None, False, True])
+def test_invalid_parameters_in_optimizer(use_orig_params):
+    fsdp_kwargs = {}
+    if _TORCH_GREATER_EQUAL_2_0 and use_orig_params is not None:
+        fsdp_kwargs = {"use_orig_params": use_orig_params}
+
     trainer = Trainer(
-        strategy="fsdp",
+        strategy=FSDPStrategy(**fsdp_kwargs),
         accelerator="cuda",
         devices=1,
         fast_dev_run=1,
     )
+
     error_context = (
         nullcontext()
-        if _TORCH_GREATER_EQUAL_2_0
+        if _TORCH_GREATER_EQUAL_2_0 and (_TORCH_GREATER_EQUAL_2_1 or use_orig_params is not False)
         else pytest.raises(ValueError, match="The optimizer does not seem to reference any FSDP parameters")
     )
 
@@ -385,6 +391,12 @@ def configure_optimizers(self):
             layer = torch.nn.Linear(4, 5)
             return torch.optim.Adam(layer.parameters(), lr=1e-2)
 
+    error_context = (
+        nullcontext()
+        if _TORCH_GREATER_EQUAL_2_0 and use_orig_params is not False
+        else pytest.raises(ValueError, match="The optimizer does not seem to reference any FSDP parameters")
+    )
+
     model = NoFlatParametersModel()
     with error_context:
         trainer.fit(model)

tests/tests_pytorch/trainer/optimization/test_manual_optimization.py

@@ -388,6 +388,7 @@ def on_before_optimizer_step(self, optimizer, *_):
 
 def test_step_with_optimizer_closure(tmpdir):
     """Tests that `step` works with optimizer_closure."""
+    seed_everything(1)
 
     class TestModel(BoringModel):
         _losses = []