[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/worker/model_runner_v1.py

@@ -113,6 +113,7 @@ from vllm_ascend.eplb.eplb_updator import EplbUpdator
 from vllm_ascend.eplb.utils import model_register
 from vllm_ascend.models.layers.mla import AscendMultiHeadLatentAttention
 from vllm_ascend.multistream.ms_split import compute_split_seq_index
+from vllm_ascend.ops.weight_prefetch import WeightPrefetchMethod
 from vllm_ascend.platform import NPUPlatform
 from vllm_ascend.sample.logits_processor import build_logitsprocs
 from vllm_ascend.sample.rejection_sampler import AscendRejectionSampler
@@ -285,6 +286,8 @@ class NPUModelRunner(LoRAModelRunnerMixin):
             self.chunked_prefill_enabled = self.scheduler_config.chunked_prefill_enabled
         else:
             self.chunked_prefill_enabled = True
+        self.weight_prefetch_method = WeightPrefetchMethod(
+            self.ascend_config.weight_prefetch_config)
 
         if self.cache_config.cache_dtype == "auto":
             self.kv_cache_dtype = self.dtype
@@ -1856,7 +1859,8 @@ class NPUModelRunner(LoRAModelRunnerMixin):
                     num_actual_tokens=scheduler_output.
                     total_num_scheduled_tokens,
                     prefetch_stream=self.prefetch_stream,
-                    model_instance=self.model):
+                    model_instance=self.model,
+                    weight_prefetch_method=self.weight_prefetch_method):
                 self.maybe_setup_kv_connector(scheduler_output)
 
                 hidden_states = self._generate_process_reqs_hidden_states(
@@ -2370,7 +2374,8 @@ class NPUModelRunner(LoRAModelRunnerMixin):
                     aclgraph_runtime_mode=aclgraph_runtime_mode,
                     batch_descriptor=batch_descriptor,
                     prefetch_stream=self.prefetch_stream,
-                    model_instance=self.model):
+                    model_instance=self.model,
+                    weight_prefetch_method=self.weight_prefetch_method):
                 hidden_states = self._generate_dummy_run_hidden_states(
                     with_prefill, is_torchair_compile, input_ids, positions,
                     attn_metadata, num_tokens, intermediate_tensors,