[Perf] Optimize fused_experts quantization code to save npu memory (#784)

### What this PR does / why we need it?
In the w8a8 quantization code of `fused_experts`, the output of almost
every operator is assigned a new variable name. If we want to save NPU
memory, we manually `del` these variables to end their lifecycle, which
fills the code with `del` statements and looks inelegant.
Therefore, I plan to names the output of most operators as
`hidden_states`, thereby ending the lifecycle of the previous
`hidden_states`.

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

### How was this patch tested?

Signed-off-by: ApsarasX <apsarax@outlook.com>

vllm_ascend/models/deepseek_v2.py

@@ -222,9 +222,6 @@ class CustomDeepseekV2MoE(nn.Module):
         num_tokens, hidden_dim = hidden_states.shape
         hidden_states = hidden_states.view(-1, hidden_dim)
 
-        if self.n_shared_experts is not None:
-            shared_output = self.shared_experts(hidden_states)
-
         if (self.tp_size > 1 and self.enable_mc2 and not is_prefill):
             chunks = torch.chunk(hidden_states,
                                  get_tp_group().world_size,
@@ -248,8 +245,8 @@ class CustomDeepseekV2MoE(nn.Module):
             else:
                 final_hidden_states = tensor_model_parallel_all_reduce(
                     final_hidden_states)
-
-        if shared_output is not None:
+        if self.n_shared_experts is not None:
+            shared_output = self.shared_experts(hidden_states)
             final_hidden_states = final_hidden_states + shared_output
 
         return final_hidden_states.view(num_tokens, hidden_dim)