[Feat]Qwen3 Moe supports npu_add_rms_norm_quant op by default, update op with bias, resolve conflict with weight prefetch (#3465)

### What this PR does / why we need it?
1.qwen3 moe uses add_rms_norm_quant op instead of 'add_rms_norm op and
quant op' during quantization scene.
2.torch_npu.add_rms_norm_quant op fixed accuracy while model weights is
quantized by anti_method m4, m4 quantization is asymmetric outlier
suppression method, it will generate none-zero norm bias,
add_rms_norm_quant op updated to add this parameter to calculate.
3. add torch-npu check

### Does this PR introduce _any_ user-facing change?
new feature works if torch_npu version >= torch_npu-2.7.1.dev20250919

### How was this patch tested?
1.no special parameters to set, no new envs to set. new feature works if
torch_npu version >= torch_npu-2.7.1.dev20250919
2.use qwen3 moe quantization model to test ,such as
Qwen3-235B-A22B-W8A8, Qwen3-30B-A3B-W8A8,
Qwen3-235B-A22B-Instruct-2507-m4 (anti_method m4)

- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0

---------

Signed-off-by: h30027576 <huangdong51@huawei.com>

vllm_ascend/ascend_forward_context.py

@@ -11,7 +11,7 @@ from vllm.forward_context import (BatchDescriptor, get_forward_context,
                                   set_forward_context)
 
 import vllm_ascend.envs as envs_ascend
-from vllm_ascend.utils import enable_sp, is_moe_model
+from vllm_ascend.utils import enable_sp, is_moe_model, version_check
 
 if TYPE_CHECKING:
     from vllm_ascend.ops.weight_prefetch import WeightPrefetchMethod
@@ -160,13 +160,18 @@ def set_ascend_forward_context(
         # this optim now just support dense models due to the specific operators used.
         # Once the necessary conditions are met, support for MOE models will also be added.
         from vllm_ascend.quantization.quant_config import AscendQuantConfig
+        model_type_scope = ["llama", "qwen2", "qwen3"]
+        if version_check():
+            model_type_scope.append("qwen3_moe")
         addrmsnorm_quant_fusion_enabled = isinstance(vllm_config.quant_config, AscendQuantConfig) and \
-            vllm_config.model_config.hf_config.model_type in ["llama", "qwen2", "qwen3"] and \
+            vllm_config.model_config.hf_config.model_type in model_type_scope and \
             forward_context.layer_idx is not None
         if addrmsnorm_quant_fusion_enabled:
             forward_context.model_instance = model_instance
             forward_context.num_hidden_layers = vllm_config.model_config.hf_config.num_hidden_layers
             forward_context.fusion_linear = "gate_up_dense" if forward_context.layer_idx == 0 else "qkv_dense"
+            if vllm_config.model_config.hf_config.model_type == "qwen3_moe":
+                forward_context.fusion_linear = "gate_moe" if forward_context.layer_idx == 0 else "qkv_moe"
         forward_context.addrmsnorm_quant_fusion_enabled = addrmsnorm_quant_fusion_enabled
 
         if num_tokens is None and attn_metadata is not None: