Reapply "[MoE] [Refactor] Remove manual memory cleanup (#3365)" (#3483) (#3512)

### What this PR does / why we need it?
1. Replace manual memory cleanup with passing parameter.
2. FusedMoEPrepareAndFinalizeWithMC2 inherits All2All avoid duplicated
code.
3. Fix MC2 bug introduced in
https://github.com/vllm-project/vllm-ascend/pull/3365
4. Unify aclgraph & eager in W8A8_dynamic.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
e2e & ut

- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0

Signed-off-by: Pr0Wh1teGivee <calvin_zhu0210@outlook.com>

vllm_ascend/quantization/w8a8_dynamic.py

@@ -227,22 +227,6 @@ class AscendW8A8DynamicFusedMoEMethod:
         if enable_force_load_balance:
             topk_ids = torch.randint_like(topk_ids, 0, global_num_experts)
 
-        if self.use_aclgraph:
-            moe_comm_method = get_forward_context().moe_comm_method
-            return moe_comm_method.fused_experts(
-                hidden_states=x,
-                w1=layer.w13_weight,
-                w2=layer.w2_weight,
-                topk_weights=topk_weights,
-                topk_ids=topk_ids,
-                use_int8_w8a8=True,
-                w1_scale=layer.w13_weight_scale,
-                w2_scale=layer.w2_weight_scale,
-                expert_map=expert_map,
-                dynamic_eplb=self.dynamic_eplb,
-                log2phy=log2phy,
-                global_redundant_expert_num=global_redundant_expert_num)
-
         topk_weights = topk_weights.to(x.dtype)
 
         moe_comm_method = get_forward_context().moe_comm_method
@@ -261,7 +245,8 @@ class AscendW8A8DynamicFusedMoEMethod:
             shared_experts=shared_experts,
             quantized_x_for_share=quantized_x_for_share,
             dynamic_scale_for_share=dynamic_scale_for_share,
-            dynamic_eplb=self.dynamic_eplb)
+            dynamic_eplb=self.dynamic_eplb,
+            mc2_mask=kwargs.get("mc2_mask", None))
 
     def process_weights_after_loading(self, layer):
         if self.transpose_weight: