Add RmsNormNopQuantizer and Pattern

backends/cadence/aot/quantizer/patterns.py

@@ -721,3 +721,18 @@ def __init__(self, args, meta):
 
     def replacement_op(self) -> OpOverload:
         return torch.ops.cadence.quantized_w8a32_gru.default
+
+
+class _RmsNormPattern(QuantizationPattern):
+    """Pattern that preserves rms_norm from decomposition without matching anything."""
+
+    def partition_types(self) -> list[torch._ops.OpOverload]:
+        return [torch.ops.aten.rms_norm.default]
+
+    def get_anchors(
+        self, gm: torch.fx.GraphModule, fused_partition: List[fx.GraphModule]
+    ) -> Tuple[PartitionAnchors, fx.Node]:
+        return PartitionAnchors(empty=True), None  # pyre-ignore[7]
+
+    def replacement_op(self) -> torch._ops.OpOverload:
+        return torch.ops.aten.rms_norm.default

backends/cadence/aot/quantizer/quantizer.py

@@ -11,6 +11,7 @@
 
 import torch
 from executorch.backends.cadence.aot.quantizer.patterns import (
+    _RmsNormPattern,
     AddmmPattern,
     AddPattern,
     BmmPattern,
@@ -37,9 +38,7 @@
     is_annotated,
     no_outside_users,
 )
-
 from torch import fx
-
 from torchao.quantization.pt2e import HistogramObserver, MinMaxObserver
 from torchao.quantization.pt2e.quantizer import (
     ComposableQuantizer,
@@ -285,6 +284,15 @@ def __init__(
         super().__init__([])
 
 
+class CadenceRmsNormNopQuantizer(CadenceQuantizer):
+    """
+    Nop quantizer that preserves rms_norm from decomposition.
+    """
+
+    def __init__(self) -> None:
+        super().__init__([CadenceAtenQuantizer(_RmsNormPattern(), qconfig_A8W8)])
+
+
 class CadenceWithLayerNormQuantizer(CadenceQuantizer):
     """
     Quantizer including layer norm