[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>

tests/ut/attention/test_mla_v1.py

@@ -495,7 +495,7 @@ class TestAscendMLAImpl(TestBase):
         mock_up_proj.assert_called_once()
         mock_npu_fused_infer_attention_score.assert_called_once()
 
-    @patch("vllm_ascend.attention.mla_v1.npu_prefetch")
+    @patch("vllm_ascend.attention.mla_v1.maybe_npu_prefetch")
     def test_mla_preprocess(self, magic_npu_fetch):
         magic_npu_fetch.return_value = MagicMock()
         batch_size = 4