diff --git a/python/tvm/relax/backend/contrib/cutlass.py b/python/tvm/relax/backend/contrib/cutlass.py
index de7eb54b991b..6f47b44d1766 100644
--- a/python/tvm/relax/backend/contrib/cutlass.py
+++ b/python/tvm/relax/backend/contrib/cutlass.py
@@ -464,21 +464,12 @@ def layer_norm_pattern():
     ]
 
 
-def _check_rms_norm(ctx: PatternCheckContext) -> bool:
-    rms_norm = ctx.annotated_expr["rms_norm"]
-    if "rms_norm" not in rms_norm.args[0].name_hint:
-        return False
-
-    return True
-
-
 def rms_norm_pattern():
     """Create a RMS norm pattern for CUTLASS."""
     return [
         (
             "cutlass.rms_norm",
             *make_rms_norm_pattern(),
-            _check_rms_norm,
         ),
     ]
 
diff --git a/python/tvm/relax/backend/patterns.py b/python/tvm/relax/backend/patterns.py
index 23de175b24f6..e7bf3d501211 100644
--- a/python/tvm/relax/backend/patterns.py
+++ b/python/tvm/relax/backend/patterns.py
@@ -330,10 +330,8 @@ def make_rms_norm_pattern():
     """Create a layer norm pattern."""
     inp = wildcard()
     weight = wildcard()
-    gv = GlobalVarPattern()
-    out = is_op("relax.call_tir")(gv, TuplePattern([inp, weight]))
-    annotations = {"gv": gv, "inp": inp, "rms_norm": out}
-    return out, annotations
+    out = is_op("relax.nn.rms_norm")(inp, weight)
+    return out, {}
 
 
 def make_attention_rewrite_pattern(
diff --git a/python/tvm/relax/op/nn/nn.py b/python/tvm/relax/op/nn/nn.py
index 5adf38d7d642..71fc8661dec4 100644
--- a/python/tvm/relax/op/nn/nn.py
+++ b/python/tvm/relax/op/nn/nn.py
@@ -1061,7 +1061,7 @@ def rms_norm(
 
     .. math::
 
-        out = \frac{data}{\sqrt{mean(data, axis)+\epsilon}} * weight + bias
+        out = \frac{data}{\sqrt{mean(data, axis)+\epsilon}} * weight
 
     Parameters
     ----------
@@ -1071,9 +1071,6 @@ def rms_norm(
     weight : relax.Expr
         The scale factor.
 
-    bias : relax.Expr
-        The offset factor.
-
     axes : Union[int, List[int]]
         The axes that along which the normalization is applied.
 
diff --git a/tests/python/relax/test_transform_fuse_ops_by_pattern.py b/tests/python/relax/test_transform_fuse_ops_by_pattern.py
index bd434864a081..e093e6022e0a 100644
--- a/tests/python/relax/test_transform_fuse_ops_by_pattern.py
+++ b/tests/python/relax/test_transform_fuse_ops_by_pattern.py
@@ -56,9 +56,9 @@ def main(
     ) -> R.Tensor((1, 64, 56, 56), dtype="float32"):
         cls = Conv2dReLU_composite_annotated
         with R.dataflow():
-            gv: R.Tensor(
-                (1, 64, 56, 56), dtype="float32"
-            ) = cls.fused_relax_nn_conv2d_relax_nn_relu_dnnl(data, weight1)
+            gv: R.Tensor((1, 64, 56, 56), dtype="float32") = (
+                cls.fused_relax_nn_conv2d_relax_nn_relu_dnnl(data, weight1)
+            )
             R.output(gv)
         return gv
 
@@ -120,12 +120,12 @@ def main(
     ) -> R.Tensor((1, 64, 54, 54), dtype="float32"):
         cls = Conv2dReLUx2Partitioned
         with R.dataflow():
-            lv: R.Tensor(
-                (1, 64, 56, 56), dtype="float32"
-            ) = cls.fused_relax_nn_conv2d_relax_nn_relu(data, weight1)
-            gv: R.Tensor(
-                (1, 64, 54, 54), dtype="float32"
-            ) = cls.fused_relax_nn_conv2d_relax_nn_relu1(lv, weight2)
+            lv: R.Tensor((1, 64, 56, 56), dtype="float32") = (
+                cls.fused_relax_nn_conv2d_relax_nn_relu(data, weight1)
+            )
+            gv: R.Tensor((1, 64, 54, 54), dtype="float32") = (
+                cls.fused_relax_nn_conv2d_relax_nn_relu1(lv, weight2)
+            )
             R.output(gv)
         return gv
 
@@ -235,9 +235,9 @@ def main(
             lv: R.Tensor((1, 64, 56, 56), dtype="float32") = cls.fused_relax_nn_conv2d(
                 data, weight1
             )
-            gv: R.Tensor(
-                (1, 64, 54, 54), dtype="float32"
-            ) = cls.fused_relax_nn_conv2d_relax_nn_relu(lv, weight2)
+            gv: R.Tensor((1, 64, 54, 54), dtype="float32") = (
+                cls.fused_relax_nn_conv2d_relax_nn_relu(lv, weight2)
+            )
             R.output(gv)
         return gv
 
@@ -1024,6 +1024,21 @@ def main(
     assert "fused_relax_matmul_relax_add_relax_add_cutlass" in func_names
 
 
+def test_rms_norm():
+    @tvm.script.ir_module
+    class RMSNorm:
+        @R.function
+        def main(data: R.Tensor((1, 64, 3), "float16"), weight: R.Tensor((3,), "float16")):
+            with R.dataflow():
+                out = R.nn.rms_norm(data, weight)
+                R.output(out)
+            return out
+
+    mod = partition_for_cutlass(RMSNorm)
+    func_names = [name.name_hint for (name, _) in mod.functions.items()]
+    assert "fused_relax_nn_rms_norm_cutlass" in func_names
+
+
 def test_intermediate_var_to_var_binding():
     """test the intermediate binding y1 will break the fusion"""