[bugfix] Fix dummy-run and multi-node issues in MoE routing and MTP (#4947)

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

- Fix a premature `return` in `moe_init_routing_quant_v2.cpp` so the
routing kernel completes correctly instead of exiting early in certain
paths.
- Switch `FusedAlltoAllCommImpl` to use the MC2-based token dispatcher
and prepare/finalize routines, aligning MoE communication with the MC2
algorithm optimized for Ascend devices.
- Add a temporary override in `MtpProposer` to map `FUSED_ALLTOALL` back
to `ALLTOALL` until the MoE communication type selection logic is fully
finalized, avoiding incorrect behavior in dummy-run flows.
- Simplify the MoE communication selection for Ascend 910-93 in
`NPUModelRunner` by removing the EP-size guard on `FUSED_ALLTOALL`,
which fixes failures in multi-node / larger-EP configurations while
keeping MC2 routing under the configured token capacity.

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

Signed-off-by: mojave2 <chenchen145@huawei.com>

csrc/dispatch_ffn_combine/op_kernel/moe_init_routing_quant_v2/moe_init_routing_quant_v2.cpp

@@ -114,7 +114,6 @@ __aicore__ inline  void moe_init_routing_quant_v2(
       srcToDstAndGatherOp.Init(x, scale, expandedRowIdx, expandedX, dynamicQuantScale, workspace, tilingData, &srcToDstGatherPipe);
       srcToDstAndGatherOp.Process();
       srcToDstGatherPipe.Destroy();
-      return;
     }
   }
 

vllm_ascend/spec_decode/mtp_proposer.py

@@ -734,6 +734,9 @@ class MtpProposer(Proposer):
              num_input_tokens, self.runner.with_prefill)
 
         moe_comm_type = self.runner._select_moe_comm_method(num_input_tokens)
+        # TODO: remove this after moe_comm_type selection logic is finalized
+        moe_comm_type = (MoECommType.ALLTOALL if moe_comm_type
+                         == MoECommType.FUSED_ALLTOALL else moe_comm_type)
 
         # Enable shared_expert_dp and MTP FULL graph may cause accuracy issues.
         if scheduler_output and not self.enable_shared_expert_dp:

vllm_ascend/utils.py

@@ -920,16 +920,17 @@ def calculate_ep_buffer_size() -> int:
     try:
         from vllm.config import get_current_vllm_config
         vllm_config = get_current_vllm_config()
+        tp_size = vllm_config.parallel_config.tensor_parallel_size
         hf_config = vllm_config.model_config.hf_config
 
         hidden_size = hf_config.hidden_size
-        topk = getattr(hf_config, "num_experts_per_token", 1)
-        batch_size = vllm_config.scheduler_config.max_num_batched_tokens
+        topk = getattr(hf_config, "num_experts_per_tok", 1)
+        batch_size = vllm_config.scheduler_config.max_num_batched_tokens // tp_size
         int8_size = torch.iinfo(torch.int8).bits // 8
         bf16_size = torch.finfo(torch.bfloat16).bits // 8
         ep_buffer_size = math.ceil(
             (batch_size * hidden_size * topk *
-             (int8_size * 2 + bf16_size)) / (1024 * 1024))
+             (int8_size + bf16_size) * 3) / (1024 * 1024))
     except Exception:
         pass
     return max(ep_buffer_size, _DEFAULT_BUFFER_SIZE)