[CustomOp] Register RMSNorm instead of overwrite forward_oot (#2284)

### What this PR does / why we need it?
Use function CustomOp.register_oot to achieve the customop registery
```
from vllm.model_executor.custom_op import CustomOp
CustomOp.register_oot(_decorated_op_cls=AscendRMSNorm, name="RMSNorm")
```

### Does this PR introduce _any_ user-facing change?
N/A

### How was this patch tested?
CI passed with new added/existing test.

- vLLM version: v0.10.0
- vLLM main:
afa5b7ca0b

---------

Signed-off-by: Icey <1790571317@qq.com>

vllm_ascend/ops/layernorm.py

@@ -20,8 +20,6 @@ from typing import Optional, Tuple, Union
 import torch
 from vllm.model_executor.layers.layernorm import RMSNorm
 
-from vllm_ascend.utils import is_310p
-
 
 class AddRMSNormW8A8Quant(RMSNorm):
     # Fuse AddRmsNorm and W8A8 quantization ops together
@@ -60,27 +58,28 @@ class AddRMSNormW8A8Quant(RMSNorm):
         return x
 
 
-def forward_oot(
-    self,
-    x: torch.Tensor,
-    residual: Optional[torch.Tensor] = None,
-) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
-    import torch_npu
+class AscendRMSNorm(RMSNorm):
 
-    if residual is not None:
-        if is_310p():
-            orig_dtype = residual.dtype
-            x = x + residual.to(x.dtype)
-            residual = x.to(orig_dtype)
-            x, _ = torch_npu.npu_rms_norm(x, self.weight,
-                                          self.variance_epsilon)
-        else:
-            x, _, residual = torch_npu.npu_add_rms_norm(
-                x, residual, self.weight, self.variance_epsilon)
-        return x, residual
+    def forward_oot(
+        self,
+        x: torch.Tensor,
+        residual: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        import torch_npu
 
-    x, residual = torch_npu.npu_rms_norm(x, self.weight, self.variance_epsilon)
-    return x
+        from vllm_ascend.utils import is_310p
+        if residual is not None:
+            if is_310p():
+                orig_dtype = residual.dtype
+                x = x + residual.to(x.dtype)
+                residual = x.to(orig_dtype)
+                x, _ = torch_npu.npu_rms_norm(x, self.weight,
+                                              self.variance_epsilon)
+            else:
+                x, _, residual = torch_npu.npu_add_rms_norm(
+                    x, residual, self.weight, self.variance_epsilon)
+            return x, residual
 
-
-RMSNorm.forward_oot = forward_oot
+        x, residual = torch_npu.npu_rms_norm(x, self.weight,
+                                             self.variance_epsilon)
+        return x