[MoE] [Refactor] Remove manual memory cleanup (#3365)

### What this PR does / why we need it?
1. Replace manual memory cleanup with passing parameter.
2. FusedMoEPrepareAndFinalizeWithMC2 inherits All2All avoid duplicated
code.

### 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>

tests/e2e/singlecard/ops/test_fused_moe.py

@@ -137,6 +137,7 @@ def test_token_dispatcher_with_all_gather(
     sorted_hidden_states = dispatch_output["hidden_states"]
     group_list = dispatch_output["group_list"]
     group_list_type = dispatch_output.get("group_list_type", 1)
+    context_metadata = dispatch_output["context_metadata"]
 
     expert_output = apply_mlp(hidden_states=sorted_hidden_states,
                               w1=w1_local,
@@ -144,8 +145,10 @@ def test_token_dispatcher_with_all_gather(
                               group_list=group_list,
                               group_list_type=group_list_type)
 
-    combined_output = dispatcher.token_combine(hidden_states=expert_output,
-                                               bias=None)
+    combined_output = dispatcher.token_combine(
+        hidden_states=expert_output,
+        context_metadata=context_metadata,
+        bias=None)
 
     torch_output = torch_moe(a, w1, w2, topk_weights, topk_ids, topk,
                              expert_map)
@@ -215,6 +218,7 @@ def test_token_dispatcher_with_all_gather_quant(
         group_list = dispatch_output["group_list"]
         group_list_type = dispatch_output.get("group_list_type", 1)
         dynamic_scale = dispatch_output["dynamic_scale"]
+        context_metadata = dispatch_output["context_metadata"]
 
         expert_output = unified_apply_mlp(hidden_states=sorted_hidden_states,
                                           w1=w1,
@@ -225,8 +229,10 @@ def test_token_dispatcher_with_all_gather_quant(
                                           group_list_type=group_list_type,
                                           dynamic_scale=dynamic_scale,
                                           with_quant=True)
-        combined_output = dispatcher.token_combine(hidden_states=expert_output,
-                                                   bias=None)
+        combined_output = dispatcher.token_combine(
+            hidden_states=expert_output,
+            context_metadata=context_metadata,
+            bias=None)
         assert combined_output.shape == (m, k)
         gc.collect()
         torch.npu.empty_cache()