[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/ops/fused_moe/fused_moe.py

@@ -345,7 +345,7 @@ class AscendFusedMoE(FusedMoE):
                 shared_out = fc3_context.shared_experts(hidden_states)
                 # NOTE: This is exactly the opposite of `maybe_all_reduce_tensor_model_parallel`
                 moe_comm_type = forward_context.moe_comm_type
-                if moe_comm_type in {MoECommType.ALLTOALL, MoECommType.MC2} \
+                if moe_comm_type in {MoECommType.ALLTOALL, MoECommType.MC2, MoECommType.FUSED_MC2} \
                         and not shared_expert_dp_enabled():
                     shared_out = tensor_model_parallel_all_reduce(shared_out)
                 set_flash_common3_context(shared_out=shared_out)

vllm_ascend/ops/fused_moe/moe_comm_method.py

@@ -291,9 +291,9 @@ class FusedMC2CommImpl(MoECommMethod):
         assert not (
             w1_scale is None or w2_scale is None
         ), "w1_scale and w2_scale cannot be None for FusedMC2CommImpl."
-        out = torch.empty_like(hidden_states)
 
         if envs_ascend.VLLM_ASCEND_ENABLE_FUSED_MC2 == 1:
+            out = torch.empty_like(hidden_states)
             torch.ops._C_ascend.dispatch_ffn_combine(
                 x=hidden_states,
                 weight1=w1[0],
@@ -307,7 +307,21 @@ class FusedMC2CommImpl(MoECommMethod):
                 out=out,
             )
         elif envs_ascend.VLLM_ASCEND_ENABLE_FUSED_MC2 == 2:
-            raise NotImplementedError()
+            assert expert_map is not None, "expert_map cannot be None."
+            out, _ = torch.ops._C_ascend.dispatch_gmm_combine_decode(
+                x=hidden_states,
+                expert_ids=topk_ids,
+                gmm1_permuted_weight=w1[0],
+                gmm1_permuted_weight_scale=w1_scale[0],
+                gmm2_weight=w2[0],
+                gmm2_weight_scale=w2_scale[0],
+                expert_smooth_scales=None,
+                expert_scales=topk_weights.to(torch.float32),
+                group_ep=self.token_dispatcher.moe_all_to_all_group_name,
+                ep_rank_size=self.token_dispatcher.ep_world_size,
+                ep_rank_id=self.token_dispatcher.ep_rank_id,
+                moe_expert_num=len(expert_map),
+                global_bs=self.token_dispatcher.fused_global_bs)
         else:
             raise ValueError(
                 f"Wrong value of {envs_ascend.VLLM_ASCEND_ENABLE_FUSED_MC2=}")

vllm_ascend/ops/fused_moe/token_dispatcher.py

@@ -125,6 +125,7 @@ class TokenDispatcherWithMC2(MoETokenDispatcher):
             max_num_tokens = min(max_num_reqs * uniform_decode_query_len, 512)
         num_tokens_per_tp_rank = (max_num_tokens + tp_size - 1) // tp_size
         self.global_bs = num_tokens_per_tp_rank * self.ep_world_size
+        self.fused_global_bs = max_num_tokens * self.ep_world_size
 
     def get_dispatch_mc2_kwargs(
         self,