[main] Fuse GroupedMatmul, Swiglu and DynamicQuant in W8A8_DYNAMIC quantized MoE layers (#2275)

### What this PR does / why we need it?

Fuse `GroupedMatmul`, `Swiglu` and `DynamicQuant` into one fusion
operation `GroupedMatmulSwigluQuant`.

1. extract common functions in `w4a8_dynamic.py` and `w8a8_dynamic.py`
2. if in supported occasion, use fusion operation
`npu_grouped_matmul_swiglu_quant`

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

Tested on W8A8 quantized Qwen3-235B-A22B model with `bs=16`

1. `tp=8`, `dp=1`, `moe_tp=8`, `moe_ep=1`, TPOP increased 21.54%, Output
Token Throughput increased 27.35%
<img width="3443" height="211" alt="image"
src="https://github.com/user-attachments/assets/a1a9c14d-2310-41be-9a03-36125dabae6e"
/>

3. `tp=8`, `dp=1`, `moe_tp=1`, `moe_ep=8`, TPOP increased 17.38%, Output
Token Throughput increased 6.86%
<img width="3443" height="211" alt="image"
src="https://github.com/user-attachments/assets/1ce92e92-720d-40c0-8b4d-c493e5cb10a6"
/>


- vLLM version: v0.10.1.1
- vLLM main:
6997a25ac6

---------

Signed-off-by: Ruri <33858552+zhoux77899@users.noreply.github.com>
Signed-off-by: zhoux77899 <zhouxiang100@huawei.com>

vllm_ascend/ops/fused_moe.py

@@ -70,7 +70,8 @@ def unified_fused_experts_eager(hidden_states: torch.Tensor,
                                 shared_dequant_scale: Optional[Any] = None,
                                 mc2_mask: Optional[torch.Tensor] = None,
                                 apply_router_weight_on_input: bool = False,
-                                with_quant: bool = False):
+                                with_quant: bool = False,
+                                fusion_mlp: bool = False):
     token_dispatcher = get_forward_context().token_dispatcher
 
     results = token_dispatcher.token_dispatch(
@@ -100,7 +101,8 @@ def unified_fused_experts_eager(hidden_states: torch.Tensor,
         w1_scale_bias=w1_scale_bias,
         w2_scale_bias=w2_scale_bias,
         topk_scales=results.get("topk_scales"),
-        with_quant=with_quant)
+        with_quant=with_quant,
+        fusion=fusion_mlp)
     final_hidden_states = token_dispatcher.token_combine(expert_output)
     return final_hidden_states