[Refactor] Adjustments to moe_comm_method selection process (#3001)

### What this PR does / why we need it?
Fix issues mentioned in
https://github.com/vllm-project/vllm-ascend/pull/2791 and some minor
refactoring.
1. Use Enum instead of string.
2. Avoid setting a new property to forward_context in
AscendFusedMoE.forward().
3. Enabling TokenDispatcherWithMoge.
4. Remove redundant code.

### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?

Qwen3-30B-A3B/Qwen3-30B-A3B-W8A8/DeepSeek-V3-W4A8-Pruing/deepseek-mtp/pangu-pro-moe-pruing:
1. Enable/Disable EP
2. Aclgraph & eager


- vLLM version: v0.10.2
- vLLM main:
9607d5eb44

Signed-off-by: Pr0Wh1teGivee <calvin_zhu0210@outlook.com>
Co-authored-by: weijinqian0 <12153182+weijinqian0@users.noreply.github.com>

vllm_ascend/ops/fused_moe.py

@@ -41,9 +41,7 @@ from vllm_ascend.eplb.core.eplb_utils import (determine_default_expert_map,
                                               determine_default_log2phy_map)
 from vllm_ascend.ops.expert_load_balancer import ExpertLoadBalancer
 from vllm_ascend.ops.moe.experts_selector import select_experts
-from vllm_ascend.ops.moe.moe_comm_method import (AllGatherCommImpl,
-                                                 AlltoAllCommImpl, MC2CommImpl,
-                                                 NaiveMulticastCommImpl)
+from vllm_ascend.ops.moe.moe_comm_method import setup_moe_comm_method
 from vllm_ascend.ops.sequence_parallel import MetadataForPadding
 from vllm_ascend.utils import (ACL_FORMAT_FRACTAL_NZ,
                                get_all_reduce_merge_state,
@@ -339,13 +337,7 @@ class AscendFusedMoE(FusedMoE):
         self.moe_config.mc2_group = get_mc2_group()
         self.moe_config.num_global_redundant_experts = self.global_redundant_expert_num
 
-        for method in {
-                AllGatherCommImpl, AlltoAllCommImpl, MC2CommImpl,
-                NaiveMulticastCommImpl
-        }:
-            setattr(
-                self, method.__name__.lower(),
-                method(moe_config=self.moe_config))  # type: ignore[abstract]
+        setup_moe_comm_method(self.moe_config)
 
     def update_expert_map(self, new_expert_map):
         self.expert_map = new_expert_map
@@ -360,22 +352,6 @@ class AscendFusedMoE(FusedMoE):
         if self.moe_load is not None:
             self.moe_load.zero_()
 
-    def naive_multicast(self, x: torch.Tensor,
-                        cu_tokens_across_dp_cpu: torch.Tensor):
-        assert (len(x.shape) == 2)
-        buffer = torch.empty((cu_tokens_across_dp_cpu[-1], x.size(1)),
-                             device=x.device,
-                             dtype=x.dtype)
-        start = 0 if self.dp_rank == 0 else cu_tokens_across_dp_cpu[
-            self.dp_rank - 1]
-        end = cu_tokens_across_dp_cpu[self.dp_rank]
-        buffer[start:end, :].copy_(x)
-        for idx in range(self.dp_size):
-            start = 0 if idx == 0 else cu_tokens_across_dp_cpu[idx - 1]
-            end = cu_tokens_across_dp_cpu[idx]
-            get_dp_group().broadcast(buffer[start:end, :], idx)
-        return buffer
-
     def forward(self,
                 hidden_states: torch.Tensor,
                 router_logits: torch.Tensor,
@@ -412,9 +388,6 @@ class AscendFusedMoE(FusedMoE):
             mc2_mask = chunk_mc2_mask[tp_rank]
             replace_allreduce = True
 
-        moe_comm_method_name = forward_context.moe_comm_method_name
-        forward_context.moe_comm_method = getattr(self, moe_comm_method_name)
-
         hidden_states, router_logits = forward_context.moe_comm_method.prepare(
             hidden_states=hidden_states,
             router_logits=router_logits,