[Perf] move quant before allgather in Allgather EP (#3420)

### What this PR does / why we need it?
move quant before allgather in Allgather EP, rely on
https://github.com/vllm-project/vllm-ascend/pull/3334

Deepseek R1 W8A8 performance on A2 with
`HCCL_ALGO="level0:NA;level1:pipeline"`:
| Seq length | Mean TTFT (ms) main | Mean TTFT (ms)  this PR |
|----------|----------|----------|
| 4k   |  375.21  | 364.99   |
| 16k  | 1465.23   | 1421.75  |
### Does this PR introduce _any_ user-facing change?

### How was this patch tested?


- vLLM version: v0.11.0
- vLLM main:
83f478bb19

---------

Signed-off-by: realliujiaxu <realliujiaxu@163.com>

tests/e2e/multicard/test_offline_inference_distributed.py

@@ -189,6 +189,25 @@ def test_sp_for_qwen3_moe() -> None:
         vllm_model.generate(example_prompts, sampling_params)
 
 
+@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_FLASHCOMM1": "1"})
+def test_models_distributed_deepseek_v2_lite_with_flashcomm_v1() -> None:
+    example_prompts = [
+        "test" * 1001,
+    ]
+    sampling_params = SamplingParams(max_tokens=5,
+                                     temperature=0.0,
+                                     top_k=50,
+                                     top_p=0.9)
+    with VllmRunner(snapshot_download("vllm-ascend/DeepSeek-V2-Lite-W8A8"),
+                    dtype="auto",
+                    tensor_parallel_size=2,
+                    distributed_executor_backend="mp",
+                    enable_expert_parallel=True,
+                    enforce_eager=True,
+                    quantization="ascend") as vllm_model:
+        vllm_model.generate(example_prompts, sampling_params)
+
+
 @pytest.mark.parametrize("model", QWEN_DENSE_MODELS)
 @patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_DENSE_OPTIMIZE": "1"})
 @patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_FLASHCOMM1": "1"})