[1/N][Feat] Add weight prefetch feature for Attention layers (#3146)

### What this PR does / why we need it?

- Refacotr and integrate a unified `WeightPrefetchMethod`
- Integrate `qkv_proj.weight` and `o_proj.weight` in quantized Attention
modules
- Prefetching these weights ahead of matmul-like operators imporves
performance by reducing L2 cache transfer latency

### Does this PR introduce _any_ user-facing change?

Add a new config in `--additional-config` for configuration:
```json
{
    "weight_prefetch_config": {
        "enabled": false,
        "prefetch_ratio": {
            "attn": {
                "qkv": 1.0,
                "o": 1.0,
            },
        },
    },
}
```
This feature is enabled by default, and can be disabled through this
configuration

### How was this patch tested?


- vLLM version: v0.11.0

---------

Signed-off-by: yuzhup <15705211260@163.com>
Signed-off-by: zhoux77899 <zhouxiang100@huawei.com>
Co-authored-by: yuzhup <15705211260@163.com>

vllm_ascend/utils.py

@@ -51,6 +51,7 @@ _CUSTOM_OP_ENABLED = None
 _IS_310P = None
 _SLEEP_MODE_ENABLED = None
 _CURRENT_STREAM = None
+_PREFETCH_STREAM = None
 _ASCEND_CUSTOMOP_IS_REIGISTERED = False
 
 
@@ -241,6 +242,15 @@ def current_stream() -> torch.npu.Stream:
     return _CURRENT_STREAM
 
 
+def prefetch_stream() -> torch.npu.Stream:
+    global _PREFETCH_STREAM
+    if _PREFETCH_STREAM is None:
+        # when this function is called before any stream is set,
+        # we return the default stream.
+        _PREFETCH_STREAM = torch_npu.npu.Stream()
+    return _PREFETCH_STREAM
+
+
 def adapt_patch(is_global_patch: bool = False):
     if is_global_patch:
         from vllm_ascend.patch import platform  # noqa: F401
@@ -446,20 +456,6 @@ class ProfileExecuteDuration:
         return durations
 
 
-# TODO(wxy): Move to ops module
-def npu_prefetch(input: torch.Tensor,
-                 dependency: torch.Tensor,
-                 max_size: int = 0,
-                 *,
-                 enabled: bool = True):
-    if not enabled:
-        return
-    input_size = input.element_size() * input.numel()
-    if max_size <= 0 or max_size > input_size:
-        max_size = input_size
-    torch_npu.npu_prefetch(input, dependency, max_size)
-
-
 # TODO(ttanzhiqiang): rm_router_logits
 # dp>1 will trigger
 # In theory, this solution is only applicable to AllGather and AllGatherEP, because in the dp scenario, the previous operation was gate + two communications, and now it is changed to one communication + gate operation, which can save some communication time. In theory, all moe AllGather and AllGatherEP solutions can follow this logic, but now other moe models (qwen3-235b) dp solutions are not adjusted, so use the switch to control it to prevent code errors.