[BugFix] Fix accuracy bugs for unquantized deepseekv3 models (#897)

### What this PR does / why we need it?
This PR fixes two accuracy bugs incurred by PR #819 when running
deepseekv3 series models:
1. #819 adds `all_to_all` communication in quantized cases, but
`all_gather` && `reduce_scatter` are removed in both of quantized and
unquantized cases. When running unquantized deepseekv3 models with
`ep_size == world_size`, the moe modules fail to communicate. Therefore,
this PR adds `all_to_all` communication on unquantized situation to
solve this accuracy issue.
2. Use `ep_size` rather than `dp_size` to decide whether to use
`all_to_all` in moe.

### Does this PR introduce _any_ user-facing change?
No.

### How was this patch tested?
CI passed with new added/existing test.

---------

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

vllm_ascend/quantization/quant_config.py

@@ -323,14 +323,13 @@ class AscendFusedMoEMethod(FusedMoEMethodBase):
         e_score_correction_bias: Optional[torch.Tensor] = None,
         is_prefill: bool = True,
         enable_force_load_balance: bool = False,
-        dp_size: int = 1,
         **kwargs,
     ) -> torch.Tensor:
         return self.quant_method.apply(
             layer, x, router_logits, top_k, renormalize, use_grouped_topk,
             global_num_experts, expert_map, topk_group, num_expert_group,
             custom_routing_function, scoring_func, e_score_correction_bias,
-            is_prefill, enable_force_load_balance, dp_size)
+            is_prefill, enable_force_load_balance)
 
     def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
         if hasattr(self.quant_method, "process_weights_after_loading"):