[BugFix] Fix input parameter bug of dispatch_gmm_combine_decode[RFC: issue 5476] (#5932)

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

In [PR 5040](https://github.com/vllm-project/vllm-ascend/pull/5040), the
`dispatch_gmm_combine_decode` operator was configured with an incorrect
global_bs parameter. This PR is to fix the bug.

The global_bs provided as input should have the same meaning as in the
`moe_distributed_dispatch` operator, specifically: (the maximum batch
size across all cards) * (expert parallel world size).
However, the implementation incorrectly used the variable
max_num_tokens, which does not account for tensor parallelism. This
error likely resulted in an unnecessarily large (overestimated) value.

More info about this operator, please refer to RFC: issue
https://github.com/vllm-project/vllm-ascend/issues/5476

### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Acc
test qwen3-235b eplb on a single A3 node(ep16),
with dispatch_gmm_combine_decode

| dataset | version | metric | mode | vllm-api-stream-chat |
|----- | ----- | ----- | ----- | -----|
| aime2024 | 604a78 | accuracy | gen | 80.00 |
- vLLM version: v0.13.0
- vLLM main:
11b6af5280

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

vllm_ascend/ops/fused_moe/moe_comm_method.py

@@ -343,7 +343,7 @@ class FusedMC2CommImpl(MoECommMethod):
                 ep_rank_size=self.token_dispatcher.ep_world_size,
                 ep_rank_id=self.token_dispatcher.ep_rank_id,
                 moe_expert_num=self.moe_config.num_experts,
-                global_bs=self.token_dispatcher.fused_global_bs)
+                global_bs=self.token_dispatcher.global_bs)
         else:
             raise ValueError(
                 f"Wrong value of {envs_ascend.VLLM_ASCEND_ENABLE_FUSED_MC2=}")

vllm_ascend/ops/fused_moe/token_dispatcher.py

@@ -137,7 +137,6 @@ 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,