[main] Use AddRmsNormQuant ops in the custom model to optimize Qwen3's performance (#1806)

### What this PR does / why we need it?
Optimizes the performance of the Qwen3 quantization model by registering
a custom model and adding the AddRmsNormQuant operation. Subsequent PRs
will focus on performance optimizations based on this custom model.

### Does this PR introduce _any_ user-facing change?
No.

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

- vLLM version: v0.9.2
- vLLM main:
8d0a01a5f2

Signed-off-by: rjg-lyh <1318825571@qq.com>

vllm_ascend/ops/layernorm.py

@@ -23,6 +23,43 @@ 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
+
+    def __init__(
+        self,
+        hidden_size: int,
+        layer: torch.nn.Module,
+        eps: float = 1e-6,
+        var_hidden_size: Optional[int] = None,
+        has_weight: bool = True,
+        dtype: Optional[torch.dtype] = None,
+    ) -> None:
+        super().__init__(hidden_size, eps, var_hidden_size, has_weight, dtype)
+        self.layer = layer
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        residual: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, tuple[torch.Tensor, torch.Tensor]]:
+        import torch_npu
+
+        if residual is not None:
+            x, _, residual = torch_npu.npu_add_rms_norm_quant(
+                x,
+                residual,
+                self.weight,
+                self.layer.aclnn_input_scale,
+                self.layer.aclnn_input_offset,
+                epsilon=self.variance_epsilon)
+            return x, residual
+
+        x, residual = torch_npu.npu_rms_norm(x, self.weight,
+                                             self.variance_epsilon)
+        return x
+
+
 def forward_oot(
     self,
     x: torch.Tensor,