[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/e2e/multicard/test_expert_parallel.py

@@ -14,14 +14,24 @@ def test_e2e_ep_correctness(model_name):
     ]
     max_tokens = 5
 
-    with VllmRunner(model_name, tensor_parallel_size=2,
-                    enforce_eager=True) as vllm_model:
+    # FIXME: Really strange that chunked prefill might lead to different results, investigate further
+    with VllmRunner(
+            model_name,
+            tensor_parallel_size=2,
+            additional_config={"ascend_scheduler_config": {
+                "enabled": True
+            }},
+            enforce_eager=True) as vllm_model:
         tp_output = vllm_model.generate_greedy(example_prompts, max_tokens)
 
-    with VllmRunner(model_name,
-                    tensor_parallel_size=2,
-                    enable_expert_parallel=True,
-                    enforce_eager=True) as vllm_model:
+    with VllmRunner(
+            model_name,
+            tensor_parallel_size=2,
+            enable_expert_parallel=True,
+            additional_config={"ascend_scheduler_config": {
+                "enabled": True
+            }},
+            enforce_eager=True) as vllm_model:
         ep_output = vllm_model.generate_greedy(example_prompts, max_tokens)
 
     check_outputs_equal(