[2/N][Feat] Attention and MoE weight prefetch in Qwen3MoE models (#3203)

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

- Refacotr and integrate a unified `WeightPrefetchMethod`
- Integrate `gate_up_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": True,
        "prefetch_ratio": {
            "moe": {
                "gate_up": 0.8
            },
        },
    },
}
```
This feature is enabled by default, and can be disabled through this
configuration

### How was this patch tested?


- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0

---------

Signed-off-by: yuzhup <15705211260@163.com>

vllm_ascend/ops/moe/experts_selector.py

@@ -18,6 +18,7 @@ from typing import Callable, Optional
 
 import torch
 import torch_npu
+from vllm.forward_context import get_forward_context
 
 
 def return_row_idx(hidden_states, top_k):
@@ -65,7 +66,11 @@ def select_experts(hidden_states: torch.Tensor,
         topk_weights: router weights of shape (num_tokens, top_k).
         topk_ids: selected expert IDs of shape (num_tokens, top_k).
     """
-
+    # prefetch w1_w3_proj.weight preprocess
+    weight_prefetch_method = get_forward_context().weight_prefetch_method
+    if weight_prefetch_method:
+        weight_prefetch_method.maybe_prefetch_moe_weight_preprocess(
+            hidden_states, "gate_up")
     topk_weights, topk_ids, row_idx = _select_experts_with_fusion_ops(
         hidden_states=hidden_states,
         router_logits=router_logits,

vllm_ascend/ops/moe/moe_mlp.py

@@ -78,6 +78,10 @@ def quant_apply_mlp(hidden_states: torch.Tensor,
     bias1, bias2 = None, None
     _output_dtype = w2_scale.dtype
 
+    weight_prefetch_method = get_forward_context().weight_prefetch_method
+    if weight_prefetch_method:
+        weight_prefetch_method.maybe_prefetch_moe_weight_postprocess(
+            hidden_states)
     is_mc2 = get_forward_context().moe_comm_type == MoECommType.MC2
     if w1_scale_bias is None and is_mc2:
         if fusion and not dynamic_eplb: