[WIP][BugFix]Fix accuracy issues caused by wrong etp_size passed into FusedMoEParallelConfig when using vLLM 0.9.0 (vllm-project#961)

<!--  Thanks for sending a pull request!

BEFORE SUBMITTING, PLEASE READ
https://docs.vllm.ai/en/latest/contributing/overview.html

-->
### What this PR does / why we need it?
This PR fix accuracy issues incurred by codes that adapt to
`FusedMoEParallelConfig` in vLLM 0.9.0 version. The `tp_size` used to
split weights are wrongly passed. The root cause is that vLLM community
and vLLM-Ascend are using different methods to decide whether to use
Expert Parallel.

vLLM:
vLLM use a flag `enable_expert_parallel` to indicate whether to use EP
and use the following codes to decide `ep_size`:
```
        use_ep = (dp_size_ * tp_size_ > 1
                  and vllm_parallel_config.enable_expert_parallel)

        dp_size = dp_size_
        dp_rank = get_dp_group().rank_in_group if dp_size > 1 else 0
        tp_size, tp_rank = flatten_tp_across_dp(dp_rank)

        if not use_ep:
            return FusedMoEParallelConfig(tp_size=tp_size,
                                          tp_rank=tp_rank,
                                          dp_size=dp_size,
                                          dp_rank=dp_rank,
                                          ep_size=1,
                                          ep_rank=0,
                                          use_ep=False)
        # DP + EP / TP + EP / DP + TP + EP
        assert use_ep
        # In EP, each device owns a set of experts fully. There is no tensor
        # parallel update tp_size, tp_rank, ep_size and ep_rank to reflect that.
        ep_size = tp_size
        ep_rank = tp_rank
        return FusedMoEParallelConfig(tp_size=1,
                                      tp_rank=0,
                                      dp_size=dp_size,
                                      dp_rank=dp_rank,
                                      ep_size=ep_size,
                                      ep_rank=ep_rank,
                                      use_ep=True)
```

vLLM-Ascend:
vLLM-Ascend uses `etp` to specify Tensor Parallel in MoE.
```
            self.ep_size = get_ep_group().world_size
            self.tp_size = get_etp_group().world_size
            self.dp_size = (dp_size if dp_size is not None else
                            get_dp_group().world_size)
```

So there will be conflicts if we simply combine these codes together.

### Does this PR introduce _any_ user-facing change?
<!--
Note that it means *any* user-facing change including all aspects such
as API, interface or other behavior changes.
Documentation-only updates are not considered user-facing changes.
-->

### How was this patch tested?
<!--
CI passed with new added/existing test.
If it was tested in a way different from regular unit tests, please
clarify how you tested step by step, ideally copy and paste-able, so
that other reviewers can test and check, and descendants can verify in
the future.
If tests were not added, please describe why they were not added and/or
why it was difficult to add.
-->

Signed-off-by: angazenn <zengyanjia@huawei.com>
Co-authored-by: angazenn <zengyanjia@huawei.com>

Author: Angazenn

vllm_ascend/ops/fused_moe.py

@@ -748,6 +748,7 @@ def __init__(
                     vllm_parallel_config=vllm_config.parallel_config))
 
             self.moe_parallel_config.ep_size = get_ep_group().world_size
+            self.moe_parallel_config.tp_size = get_etp_group().world_size
 
         self.top_k = top_k
         self.num_experts = num_experts