[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>

tests/e2e/multicard/2-cards/test_offline_inference_distributed.py

@@ -222,7 +222,7 @@ def test_qwen3_dense_fc1_tp2(model):
 
 
 @pytest.mark.parametrize("model", QWEN_DENSE_MODELS)
-@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_PREFETCH_MLP": "1"})
+@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_FLASHCOMM1": "1"})
 def test_qwen3_dense_prefetch_mlp_weight_tp2(model):
     example_prompts = [
         "Hello, my name is",
@@ -236,6 +236,7 @@ def test_qwen3_dense_prefetch_mlp_weight_tp2(model):
             tensor_parallel_size=2,
             cudagraph_capture_sizes=[1, 2, 4, 8],
             quantization="ascend",
+            additional_config={"weight_prefetch_config": {"enabled": True}},
     ) as vllm_model:
         vllm_model.generate_greedy(example_prompts, max_tokens)