correctly get parameters

Signed-off-by: Masaki Kozuki <mkozuki@nvidia.com>

thunder/distributed/__init__.py

@@ -143,6 +143,10 @@ def _sync_grads(module: torch.nn.Module) -> None:
                 tdist.distributed_c10d.all_reduce(g, group=process_group)
         cm.wait()
     elif getattr(module, "use_fsdp", False):
+        from typing import cast
+        from thunder.core.module import ThunderModule
+
+        module = cast(ThunderModule, module)
 
         def prep_shard(
             g: torch.Tensor,
@@ -154,7 +158,16 @@ def prep_shard(
 
         rank: int = tdist.distributed_c10d.get_rank(process_group)
         world_size: int = tdist.distributed_c10d.get_world_size(process_group)
-        params_with_grad = tuple(filter(lambda p: hasattr(p, "_thunder_fsdp_unsharded_grad"), module.parameters()))
+
+        params_of_orig_module = list(module._model.get_parameter(name) for name, _ in module.named_parameters())
+        params_of_fsdp_module = list(module.get_parameter(name) for name, _ in module.named_parameters())
+
+        params_with_grad = []
+        params_to_attach_grad = []
+        for o, f in zip(params_of_orig_module, params_of_fsdp_module):
+            if hasattr(o, "_thunder_fsdp_unsharded_grad"):
+                params_with_grad.append(o)
+                params_to_attach_grad.append(f)
         if not params_with_grad:
             return
         unsharded_grads = [p._thunder_fsdp_unsharded_grad for p in params_with_grad]
@@ -165,9 +178,9 @@ def prep_shard(
                     s, u, op=tdist.distributed_c10d.ReduceOp.AVG, group=process_group
                 )
         cm.wait()
-        for p, g in zip(params_with_grad, sharded_grads):
+        for orig, p, g in zip(params_with_grad, params_to_attach_grad, sharded_grads):
             p.grad = g
-            del p._thunder_fsdp_unsharded_grad
+            del orig._thunder_fsdp_unsharded_grad
     else:
         import warnings