[Refactor] Add expert processed token count output for DispatchFFNCombine/DispatchFFNCombineBF16 (#6402)

### What this PR does / why we need it?
Add New Output for Expert Token Count
An additional output tensor expert_token_nums is added to both operators
to meet the requirement of tracking token distribution among experts:

Tensor Name: expert_token_nums
Dimension: 1D tensor
Shape: (local_expert_num,)
Data Type: int32
Semantics: Represents the number of tokens actually received by each
expert on the current card.
### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

- vLLM version: v0.14.1
- vLLM main:
dc917cceb8

---------

Signed-off-by: guanguan0308 <1546542263@qq.com>
Signed-off-by: guanguan0308 <162653673+guanguan0308@users.noreply.github.com>

vllm_ascend/ops/fused_moe/moe_comm_method.py

@@ -315,6 +315,7 @@ class FusedMC2CommImpl(MoECommMethod):
         expert_tokens = None
         if envs_ascend.VLLM_ASCEND_ENABLE_FUSED_MC2 == 1:
             out = torch.empty_like(hidden_states)
+            expert_token_nums = torch.zeros([self.moe_config.num_local_experts], dtype=torch.int32)
             torch.ops._C_ascend.dispatch_ffn_combine(  # type: ignore
                 x=hidden_states,
                 weight1=w1,
@@ -326,7 +327,9 @@ class FusedMC2CommImpl(MoECommMethod):
                 group=self.token_dispatcher.moe_all_to_all_group_name,
                 max_output_size=65536,
                 out=out,
+                expert_token_nums=expert_token_nums,
             )
+            expert_tokens = expert_token_nums
         elif envs_ascend.VLLM_ASCEND_ENABLE_FUSED_MC2 == 2:
             assert expert_map is not None, "expert_map cannot be None."
             group_list_type = 1