[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/quantization/w8a8.py

@@ -91,10 +91,12 @@ class AscendW8A8LinearMethod:
         bias: Optional[torch.Tensor] = None,
         tp_rank: Optional[int] = 0,
     ) -> torch.Tensor:
-        original_dtype = x.dtype
-        if original_dtype != torch.int8:
-            x = quant_per_tensor(x, layer.aclnn_input_scale,
-                                 layer.aclnn_input_offset)
+        if x.dtype != torch.int8:
+            x = quant_per_tensor(
+                x,
+                layer.aclnn_input_scale_reciprocal,
+                layer.aclnn_input_offset,
+            )
         quant_bias = layer.quant_bias if tp_rank == 0 else None
         if is_310p():
             # On 300I Duo platform, we need transpose again if
@@ -104,7 +106,7 @@ class AscendW8A8LinearMethod:
                 layer.weight.data.transpose(1, 0),
                 layer.deq_scale,
                 bias=quant_bias,
-                output_dtype=original_dtype,
+                output_dtype=layer.params_dtype,
             )
         else:
             output = torch_npu.npu_quant_matmul(
@@ -112,13 +114,16 @@ class AscendW8A8LinearMethod:
                 layer.weight,
                 layer.deq_scale,
                 bias=quant_bias,
-                output_dtype=original_dtype,
+                output_dtype=layer.params_dtype,
             )
         return output
 
     def process_weights_after_loading(self, layer):
         expanding_factor = layer.weight.data.shape[1]
-        layer.aclnn_input_scale = 1 / torch.nn.Parameter(
+        layer.aclnn_input_scale = torch.nn.Parameter(
+            layer.input_scale.data.repeat(expanding_factor),
+            requires_grad=False)
+        layer.aclnn_input_scale_reciprocal = 1 / torch.nn.Parameter(
             layer.input_scale.data.repeat(expanding_factor),
             requires_grad=False)
         layer.aclnn_input_offset = torch.nn.Parameter(