[Feat.][310P] addrmsnorm for 300I DUO (#6704)

### What this PR does / why we need it?
This PR integrates the `npu_add_rms_norm` fused kernel for RMSNorm
operations with residual connections on 310P devices. This change
optimizes the computation by replacing a two-step process (manual
residual addition followed by RMSNorm) with a single, more efficient
fused operation. This is needed to improve the performance of models
utilizing RMSNorm with residual connections on the 310P architecture.

Fixes #

### Does this PR introduce _any_ user-facing change?
No, this PR introduces an internal optimization and does not change any
user-facing APIs or behaviors.

### How was this patch tested?
This patch was tested with updated unit tests
(`test_RMSNorm_forward_310p`) that mock the `npu_add_rms_norm` operation
to verify the correctness of the fused kernel integration.

---------

Signed-off-by: Tflowers-0129 <2906339855@qq.com>

vllm_ascend/_310p/ops/layernorm.py

@@ -11,13 +11,9 @@ class AscendRMSNorm310(AscendRMSNorm):
         residual: torch.Tensor | None = None,
     ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
         if residual is not None:
-            if x is None or x.numel() == 0 or x.shape[-1] == 0:
-                x = residual
-            else:
-                x = x + residual
-
-            residual = x
-            x, _ = torch_npu.npu_rms_norm(x, self.weight, self.variance_epsilon)
+            x, _, residual = torch_npu.npu_add_rms_norm(x, residual, self.weight, self.variance_epsilon)
+            if self.bias is not None:
+                x.add_(self.bias)
             return x, residual
 
         x, _ = torch_npu.npu_rms_norm(x, self.weight, self.variance_epsilon)