[Refactor] MLP weight prefetch to consistency with MoE Model's prefetching in terms of code and usage (#6442)

### What this PR does / why we need it?
Refactor MLP weight prefetch to consistency with MoE Model's prefetching
in terms of code and usage.
Environments VLLM_ASCEND_ENABLE_PREFETCH_MLP,
VLLM_ASCEND_MLP_DOWN_PREFETCH_SIZE and
VLLM_ASCEND_MLP_GATE_UP_PREFETCH_SIZE is removed, usage as following:

--additional-config '{"weight_prefetch_config": { "enabled": true,
"prefetch_ratio": {"mlp": { "gate_up": 1.0, "down": 1.0} }}}'

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

- vLLM version: v0.14.1
- vLLM main:
dc917cceb8

---------

Signed-off-by: leo-pony <nengjunma@outlook.com>

vllm_ascend/ops/linear_op.py

@@ -65,8 +65,8 @@ from vllm_ascend.ops.flashcomm2_oshard_manager import flashcomm2_oshard_manager
 from vllm_ascend.utils import (enable_dsa_cp, enable_dsa_cp_with_layer_shard, enable_sp, flashcomm2_enable,
                                get_flashcomm2_reorgnized_batch_ids,
                                matmul_allreduce_enable, mlp_tp_enable,
-                               oproj_tp_enable, shared_expert_dp_enabled)
-
+                               oproj_tp_enable, shared_expert_dp_enabled,
+                               get_weight_prefetch_method)
 
 class CustomLinearOp:
 
@@ -138,8 +138,10 @@ class CustomRowParallelOp(CustomLinearOp):
 
     def apply(self, input_):
         output, output_bias = self.apply_impl(input_)
-        if envs_ascend.VLLM_ASCEND_ENABLE_PREFETCH_MLP:
-            torch.ops.vllm.maybe_prefetch_mlp_gate_up_proj(output, self.prefix)
+        weight_prefetch_method = get_weight_prefetch_method()
+        if weight_prefetch_method:
+            weight_prefetch_method.maybe_prefetch_mlp_weight_preprocess(weight_prefetch_method.MLP_GATE_UP, output, self.prefix)
+
         if not self.return_bias:
             return output
         return output, output_bias