[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/torchair/models/torchair_deepseek_v2.py

@@ -70,11 +70,12 @@ from vllm.sequence import IntermediateTensors
 from vllm_ascend import envs
 from vllm_ascend.ascend_config import get_ascend_config
 from vllm_ascend.models.layers.sfa import Indexer
+from vllm_ascend.ops.weight_prefetch import maybe_npu_prefetch
 from vllm_ascend.quantization.quant_config import AscendLinearMethod
 from vllm_ascend.torchair.ops.torchair_fused_moe import TorchairAscendFusedMoE
 from vllm_ascend.torchair.quantization.torchair_w8a8_dynamic import \
     TorchairAscendW8A8DynamicLinearMethod
-from vllm_ascend.utils import dispose_tensor, npu_prefetch, oproj_tp_enable
+from vllm_ascend.utils import dispose_tensor, oproj_tp_enable
 
 
 class TorchairDeepseekV2SiluAndMul(SiluAndMul):
@@ -589,9 +590,9 @@ class TorchairDeepseekV2MLAAttention(DeepseekV2MLAAttention):
                                   and attn_metadata.num_decodes > 0)
         forward_kwargs = {"enable_multistream_mla": enable_multistream_mla}
         if self.q_lora_rank is not None:
-            npu_prefetch(self.q_a_proj.weight,
-                         hidden_states,
-                         enabled=enable_multistream_mla)
+            maybe_npu_prefetch(self.q_a_proj.weight,
+                               hidden_states,
+                               enabled=enable_multistream_mla)
             ckq = self.q_a_proj(hidden_states)[0]
             hidden_states_or_q_c = self.q_a_layernorm(ckq)
             forward_kwargs['ckq'] = ckq