[Feature]Use DispatchGmmCombineDecode operator to replace MC2(Optional) (#5040)

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

This PR adds model-side integration for the previously introduced
experimental AscendC fused operator DispatchGmmCombineDecode, used in
MoE decoding.

The operator implementation itself was added in a prior PR[#4139
](https://github.com/vllm-project/vllm-ascend/pull/4139).
This change only adapts the model execution path to optionally use the
fused operator.

When the environment variable VLLM_ASCEND_ENABLE_FUSED_MC2=2 is set, the
original MC2 path composed of multiple operators (A8W8 dispatch → GMM →
SwiGLU → GMM → combine) might be replaced by the single fused operator
DispatchGmmCombineDecode.

By default, the existing multi-operator MC2 implementation is preserved.

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

### How was this patch tested?

- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c

Signed-off-by: wangqiankun <wangqiankun13@huawei.com>

vllm_ascend/quantization/w8a8_dynamic.py

@@ -231,6 +231,10 @@ class AscendW8A8DynamicFusedMoEMethod:
         topk_weights = topk_weights.to(self.in_dtype)
 
         moe_comm_method = get_forward_context().moe_comm_method
+        # When VLLM_ASCEND_ENABLE_FUSED_MC2 == 2, use dispatch_gmm_combine_decode, need fp32 scale
+        w2_weight_scale_fp32_flag = (
+            get_forward_context().moe_comm_type == MoECommType.FUSED_MC2
+            and envs_ascend.VLLM_ASCEND_ENABLE_FUSED_MC2 == 2)
         if self.dynamic_eplb:
             w1 = layer.w13_weight_list
             w1_scale = layer.w13_weight_scale_fp32_list
@@ -240,7 +244,10 @@ class AscendW8A8DynamicFusedMoEMethod:
             w1 = [layer.w13_weight]
             w1_scale = [layer.w13_weight_scale_fp32]
             w2 = [layer.w2_weight]
-            w2_scale = [layer.w2_weight_scale]
+            w2_scale = [
+                layer.w2_weight_scale_fp32
+                if w2_weight_scale_fp32_flag else layer.w2_weight_scale
+            ]
 
         fused_scale_flag = (get_forward_context().moe_comm_type
                             == MoECommType.FUSED_MC2
@@ -279,6 +286,8 @@ class AscendW8A8DynamicFusedMoEMethod:
             layer.w13_weight_offset.data.shape[0], -1)
         layer.w2_weight_scale.data = layer.w2_weight_scale.data.view(
             layer.w2_weight_scale.data.shape[0], -1)
+        layer.w2_weight_scale_fp32 = layer.w2_weight_scale.data.to(
+            torch.float32)
         layer.w2_weight_offset.data = layer.w2_weight_offset.data.view(
             layer.w2_weight_offset.data.shape[0], -1)