[Perf] Optimize bias handling in AscendRMSNorm (#7226)

### What this PR does / why we need it?
This PR optimizes bias handling in `AscendRMSNorm` without changing the
intended
functional behavior.

In the current implementation, bias may be initialized for
`AscendRMSNorm` based
on configuration-level detection, even though some norm layers never
actually
load a bias weight. This can cause the inference path to enter the bias
branch
and execute an unnecessary `add_` operator.

To improve this, this PR introduces a loader-based flag to record
whether the
bias has actually been loaded. The bias addition is then executed only
when the
bias is truly present.

This optimization reduces redundant computation in inference and makes
the bias
application logic better aligned with the actual model weights.

- vLLM version: v0.17.0
- vLLM main:
4034c3d32e

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

vllm_ascend/ops/layernorm.py

@@ -37,11 +37,28 @@ class AscendRMSNorm(RMSNorm):
         super().__init__(hidden_size, eps, var_hidden_size, has_weight, dtype)
         vllm_config = get_current_vllm_config()
         self.bias = None
+        self.bias_loaded = False
+
         # quantization with anti_method m4 will generate none-zero norm bias
         if vllm_config.quant_config is not None and any(
             "norm.bias" in name for name in vllm_config.quant_config.quant_description
         ):
             self.bias = torch.nn.Parameter(torch.zeros(hidden_size), requires_grad=False)
+            self.bias.weight_loader = self._bias_weight_loader
+
+    def _bias_weight_loader(self, param: torch.nn.Parameter, loaded_weight: torch.Tensor) -> None:
+        if param.numel() == 1 and loaded_weight.numel() == 1:
+            # Sometimes scalar values aren't considered tensors with shapes
+            # so if both param and loaded_weight are a scalar,
+            # "broadcast" instead of copy
+            param.data.fill_(loaded_weight.item())
+        else:
+            assert param.size() == loaded_weight.size(), (
+                f"Attempted to load weight ({loaded_weight.size()}) into parameter ({param.size()})"
+            )
+
+            param.data.copy_(loaded_weight)
+        self.bias_loaded = True
 
     def forward_oot(
         self,
@@ -62,7 +79,7 @@ class AscendRMSNorm(RMSNorm):
             return x, residual
 
         x, residual = torch_npu.npu_rms_norm(x, self.weight, self.variance_epsilon)
-        if self.bias is not None:
+        if self.bias_loaded:
             x.add_(self.bias)
 
         weight_prefetch_method = get_weight_prefetch_method()