Mark weights unbacked

Summary: This is to avoid recompilations caused by the shape changes of `_weights` in KJT.

Differential Revision: D66342695

Author: Microve

Diff for: torchrec/pt2/utils.py

@@ -75,12 +75,16 @@ def kjt_for_pt2_tracing(
 
     values = kjt.values().long()
     torch._dynamo.decorators.mark_unbacked(values, 0)
+    weights = kjt.weights_or_none()
+    if weights is not None:
+        weights = weights.float()
+        torch._dynamo.decorators.mark_unbacked(weights, 0)
 
     return KeyedJaggedTensor(
         keys=kjt.keys(),
         values=values,
         lengths=lengths,
-        weights=kjt.weights_or_none(),
+        weights=weights,
         stride=stride if not is_vb else None,
         stride_per_key_per_rank=kjt.stride_per_key_per_rank() if is_vb else None,
         inverse_indices=inverse_indices,