[BugFix]add all2all when dp_size > 1 && downgrade npu_dequant_swiglu_quant (#819)

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### What this PR does / why we need it?
1. This PR introduces native `all_to_all` communication operator to fix
`allgather` bugs when dp_size > 1. Besides, it adds a naive
implementation of force-load-balance when doing profile runs.
2. The operator `npu_dequant_swiglu_quant` only supports input
hidden_states with dtype `torch.int32`. This tensor occupies space of
`global_bs * seq_len * topk * hidden_size`, which might be very large as
`ep_size` grows. Therefore we need to disable this operator and use
original `swiglu` && `quantize`.

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

### How was this patch tested?
By performing offline inference:

![image](https://github.com/user-attachments/assets/e003d5dc-0753-41ae-9303-e87f73ac6828)

---------

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

vllm_ascend/quantization/quant_config.py

@@ -321,14 +321,15 @@ class AscendFusedMoEMethod(FusedMoEMethodBase):
         scoring_func: str = "softmax",
         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)
+        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)
 
     def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
         if hasattr(self.quant_method, "process_weights_after_loading"):