Update Executorch ops registration for rms_norm

Summary: Allows use of `aten::rms_norm` in place of `cadence::rms_norm` custom op, which is no longer needed.

Differential Revision: D72485973

Author: Vysarat

backends/cadence/aot/ops_registrations.py

@@ -293,6 +293,15 @@
     "attention_mask.out(Tensor input, Tensor start, Tensor stop, *, Tensor(a!) out) -> Tensor(a!)"
 )
 
+# Custom ops in aten namespace. RMSNorm is usually decomposed, so having
+# an out-variant is non-standard
+
+lib_aten = Library("aten", "FRAGMENT")
+
+lib_aten.define(
+    "rms_norm.out(Tensor input, SymInt[] normalized_shape, Tensor? weight=None, float? eps=None, *, Tensor(a!) out) -> Tensor(a!)"
+)
+
 
 @register_fake("cadence::quantize_per_tensor")
 def quantize_per_tensor_meta(
@@ -619,15 +628,6 @@ def linalg_vector_norm_meta(
     return X.new_empty([], dtype=X.dtype)
 
 
-@register_fake("cadence::rms_norm")
-def rms_norm_meta(
-    X: torch.Tensor,
-    eps: float,
-    weight: torch.Tensor,
-) -> torch.Tensor:
-    return X.new_empty(X.shape, dtype=X.dtype)
-
-
 @register_fake("cadence::requantize")
 def requantize_meta(
     input: torch.Tensor,