[refactor] refactor deepseek-related files (#2849)

### What this PR does / why we need it?
This PR deletes ~2K lines of code about deepseek modeling. It falls back
CustomDeepseekV2 modules to original vllm implementations and adapts
some modifications in vllm about deepseek and moe.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
E2E  vllm serving with torchair graph mode and eager mode.

- vLLM version: v0.10.2
- vLLM main:
759ef49b15

---------

Signed-off-by: linfeng-yuan <1102311262@qq.com>
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
Co-authored-by: yiz-liu <136800916+yiz-liu@users.noreply.github.com>
Co-authored-by: Yizhou Liu <liu_yizhou@outlook.com>

tests/ut/ops/test_common_fused_moe.py

@@ -75,7 +75,7 @@ class TestLoadWeight(TestBase):
         with patch.object(AscendFusedMoE, "__init__",
                           lambda self, *args, **kwargs: None):
             moe = AscendFusedMoE(num_experts=4, top_k=2, hidden_size=8)
-            moe.hidden_size = 8
+
             expert_data = torch.randn(128, 8)
             loaded_weight = torch.randn(128, 4)
             moe._load_w13(expert_data, 1, "w1", loaded_weight, 0)