[Perf] add patch to optimize apply_topk_topp (#1732)

### What this PR does / why we need it?
Performance optimization for apply_top_k_top_p
### Does this PR introduce _any_ user-facing change?
Use VLLM_ASCEND_ENABLE_TOPK_TOPP_OPTIMIZATION to enable this feature
### How was this patch tested?
e2e & ut

















- vLLM version: v0.9.2
- vLLM main:
6a9e6b2abf

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

tests/e2e/multicard/test_offline_inference_distributed.py

@@ -145,3 +145,25 @@ def test_models_distributed_pangu():
             distributed_executor_backend="mp",
     ) as vllm_model:
         vllm_model.generate_greedy(example_prompts, max_tokens)
+
+
+@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_TOPK_TOPP_OPTIMIZATION": "1"})
+def test_models_distributed_topk() -> None:
+    example_prompts = [
+        "vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.",
+        "Briefly describe the major milestones in the development of artificial intelligence from 1950 to 2020.",
+        "Compare and contrast artificial intelligence with human intelligence in terms of processing information.",
+    ]
+    dtype = "half"
+    sampling_params = SamplingParams(max_tokens=5,
+                                     temperature=0.0,
+                                     top_k=50,
+                                     top_p=0.9)
+
+    with VllmRunner(
+            "deepseek-ai/DeepSeek-V2-Lite",
+            dtype=dtype,
+            tensor_parallel_size=4,
+            distributed_executor_backend="mp",
+    ) as vllm_model:
+        vllm_model.generate(example_prompts, sampling_params)