[Refactor] [MoE] Rename moe-related classes & files (#3646)

### What this PR does / why we need it?
1. Rename common_fused_moe.py to fused_moe.py.
2. Rename fused_moe_prepare_and_finalize.py / FusedMoEPrepareAndFinalize
to prepare_finalize.py / PrepareAndFinalize.
3. Rename vllm_ascend/ops/moe to vllm_ascend/ops/fused_moe.
4. Move vllm_ascend/ops/fused_moe.py to
vllm_ascend/ops/fused_moe/fused_moe.py
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
e2e & ut

- vLLM version: v0.11.0rc3
- vLLM main:
17c540a993

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

tests/ut/quantization/test_w8a8.py

@@ -5,8 +5,8 @@ import torch
 
 from tests.ut.base import TestBase
 from vllm_ascend.attention.attention_v1 import AscendAttentionState
-from vllm_ascend.ops.moe.experts_selector import (_native_grouped_topk,
-                                                  select_experts)
+from vllm_ascend.ops.fused_moe.experts_selector import (_native_grouped_topk,
+                                                        select_experts)
 from vllm_ascend.quantization.w8a8 import (AscendC8KVCacheMethod,
                                            AscendW8A8FusedMoEMethod,
                                            AscendW8A8LinearMethod,
@@ -758,7 +758,7 @@ class TestSelectExperts(TestBase):
         self.mock_ctx = MagicMock()
         self.mock_ctx.weight_prefetch_method = MagicMock()
         patcher = patch(
-            'vllm_ascend.ops.moe.experts_selector.get_forward_context',
+            'vllm_ascend.ops.fused_moe.experts_selector.get_forward_context',
             return_value=self.mock_ctx)
         self.addCleanup(patcher.stop)
         patcher.start()
@@ -831,7 +831,7 @@ class TestSelectExperts(TestBase):
         self.assertEqual(ids.shape, (self.num_tokens, self.top_k))
         self.assertEqual(ids.dtype, torch.int32)
 
-    @patch('vllm_ascend.ops.moe.experts_selector._native_grouped_topk')
+    @patch('vllm_ascend.ops.fused_moe.experts_selector._native_grouped_topk')
     def test_grouped_topk_with_correction_bias(self, mock_grouped_topk):
         """Test grouped topk with expert score correction bias"""
         mock_grouped_topk.return_value = torch.ones(self.num_tokens,