[main] mlp weight prefetch in Qwen Dense Models (#2816)

### What this PR does / why we need it?
This PR prefetchs the weight of mlp layers in Qwen Dense Models to
optimize the performance in Decode phase mainly.

### Does this PR introduce _any_ user-facing change?
 No.

### How was this patch tested?
CI passed with new added/existing test.

- vLLM version: main
- vLLM main:
a1213fae5f

Signed-off-by: rjg-lyh <1318825571@qq.com>
Co-authored-by: Shuming19 <313093131@qq.com>

vllm_ascend/envs.py

@@ -135,6 +135,15 @@ env_variables: Dict[str, Callable[[], Any]] = {
     # This feature will get better performance when concurrency is large.
     "VLLM_ASCEND_ENABLE_FLASHCOMM":
     lambda: bool(int(os.getenv("VLLM_ASCEND_ENABLE_FLASHCOMM", '0'))),
+    # Whether to enable MLP weight prefetch, only used in small concurrency.
+    "VLLM_ASCEND_ENABLE_PREFETCH_MLP":
+    lambda: bool(int(os.getenv("VLLM_ASCEND_ENABLE_PREFETCH_MLP", '0'))),
+    # buffer size for gate up prefetch
+    "MLP_GATE_UP_PREFETCH_SIZE":
+    lambda: int(os.getenv("MLP_GATE_UP_PREFETCH_SIZE", 18 * 1024 * 1024)),
+    # buffer size for down proj prefetch
+    "MLP_DOWN_PREFETCH_SIZE":
+    lambda: int(os.getenv("MLP_DOWN_PREFETCH_SIZE", 18 * 1024 * 1024)),
     # Whether to enable dense model and general optimizations for better performance.
     # Since we modified the base parent class `linear`, this optimization is also applicable to other model types.
     # However, there might be hidden issues, and it is currently recommended to prioritize its use with dense models.