[Refactor]refactor 310p ops and add ut (#6591)

### What this PR does / why we need it?
This pull request focuses on a significant refactoring effort within the
vllm-ascend project, specifically targeting operations optimized for the
Ascend 310P hardware. The changes aim to streamline the implementation
of core components like quantization and multi-head attention, making
the codebase more maintainable and robust. Concurrently, new unit tests
have been introduced to ensure the correctness and reliability of these
refactored modules.

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?
E2E test with qwen3-32b w8a8

- vLLM version: v0.15.0
- vLLM main:
d7e17aaacd

---------

Signed-off-by: pu-zhe <zpuaa@outlook.com>

vllm_ascend/_310p/ops/activation.py

@@ -19,16 +19,10 @@ import torch
 import torch.nn.functional as F
 
 from vllm_ascend.ops.activation import AscendSiluAndMul
-from vllm_ascend.utils import get_weight_prefetch_method
 
 
 class AscendSiluAndMul310(AscendSiluAndMul):
     def forward(self, x: torch.Tensor) -> torch.Tensor:
-        weight_prefetch_method = get_weight_prefetch_method()
-        if weight_prefetch_method:
-            weight_prefetch_method.maybe_prefetch_mlp_weight_preprocess(weight_prefetch_method.MLP_DOWN, x)
         h = x.shape[-1] // 2
         out = (F.silu(x[..., :h].to(torch.float32)) * x[..., h:].to(torch.float32)).to(torch.float16)
-        if weight_prefetch_method:
-            weight_prefetch_method.maybe_prefetch_mlp_weight_postprocess(out)
         return out