diff --git a/torchtitan/experiments/simple_fsdp/simple_fsdp.py b/torchtitan/experiments/simple_fsdp/simple_fsdp.py
index 6597c45f9d..de2196298e 100644
--- a/torchtitan/experiments/simple_fsdp/simple_fsdp.py
+++ b/torchtitan/experiments/simple_fsdp/simple_fsdp.py
@@ -347,3 +347,38 @@ def data_parallel(
             ),
         )
     return model
+
+
+def _maybe_register_placement_as_opaque():
+    # The `PlacementVariable` was removed from TorchDynamo in
+    # https://github.com/pytorch/pytorch/pull/171482, so now TorchDynamo
+    # requires an opaque type, registration for placements. That includes
+    # derived classes like `_ScaledPartial`. According to the notes in:
+    # https://github.com/pytorch/pytorch/blob/main/torch/_library/opaque_object.py
+    # Opaque objects are the way TorchDynamo allows custom operators to accept
+    # a user-defined "black box" object as an input. Users can register their
+    # custom classes as `register_opaque_type(MyClass, typ=..., members=...)`,
+    # where `typ` is either "reference" or "value", and `members` is a dictionary
+    # mapping member names (attributes, properties, or methods) to their MemberType,
+    # which controls how they are handled during torch.compile tracing.
+
+    from torch._dynamo import variables
+
+    if not hasattr("PlacementVariable", variables):
+        from torch._library.opaque_object import MemberType, register_opaque_type
+
+        allowed_members = {
+            "reduce_op": MemberType.USE_REAL,
+            "is_shard": MemberType.USE_REAL,
+            "is_partial": MemberType.USE_REAL,
+            "is_replicate": MemberType.USE_REAL,
+            "__eq__": MemberType.USE_REAL,
+        }
+        register_opaque_type(
+            _ScaledPartial,
+            typ="value",
+            members=allowed_members,
+        )
+
+
+_maybe_register_placement_as_opaque()