[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"):

vllm_ascend/quantization/w8a8_dynamic.py

@@ -582,7 +582,6 @@ class AscendW8A8DynamicFusedMoEMethod:
         e_score_correction_bias: Optional[torch.Tensor] = None,
         is_prefill: bool = True,
         enable_force_load_balance: bool = True,
-        dp_size: int = 1,
         **kwargs,
     ) -> torch.Tensor:
         assert router_logits.shape[
@@ -635,7 +634,7 @@ class AscendW8A8DynamicFusedMoEMethod:
                 top_k=top_k,
                 expert_map=expert_map,
                 moe_all_to_all_group_name=self.moe_all_to_all_group_name)
-        elif dp_size == 1:
+        elif self.ep_group.world_size == 1:
             return fused_experts(hidden_states=x,
                                  w1=layer.w13_weight,
                                  w1_scale=layer.w13_weight_scale,
@@ -646,6 +645,10 @@ class AscendW8A8DynamicFusedMoEMethod:
                                  top_k=top_k,
                                  expert_map=expert_map)
         else:
+            # The current implementation of deepseek moe splits hidden_states
+            # according to tp_size before they are feed into fused_moe module.
+            # Therefore, all2all is needed no matter how dp/tp is set so as to
+            # dispatch/combine tokens.
             return fused_experts_with_all2all(hidden_states=x,
                                               w1=layer.w13_weight,
                                               w1_scale=layer.w13_weight_scale,