bugfix(MC2): refactor the comm group of MC2 to be compatible with PP (#7291)

### What this PR does / why we need it?
This PR refactors the communication group of MC2 to keep it consistent
with vllm's EP group, making it compatible with PP.

- vLLM version: v0.17.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: QiuChunshuo <qiuchunshuo@huawei.com>

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

@@ -1,49 +0,0 @@
-# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
-# Copyright 2023 The vLLM team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# This file is a part of the vllm-ascend project.
-#
-import pytest
-
-from tests.e2e.conftest import VllmRunner
-
-MODELS = [
-    "Qwen/Qwen3-0.6B",
-    "deepseek-ai/DeepSeek-V2-Lite-Chat",
-]
-
-TENSOR_PARALLELS = [1]
-PIPELINE_PARALLELS = [2]
-DIST_EXECUTOR_BACKEND = ["mp", "ray"]
-
-prompts = [
-    "Hello, my name is",
-    "The future of AI is",
-]
-
-
-@pytest.mark.parametrize("model", MODELS)
-@pytest.mark.parametrize("tp_size", TENSOR_PARALLELS)
-@pytest.mark.parametrize("pp_size", PIPELINE_PARALLELS)
-@pytest.mark.parametrize("distributed_executor_backend", DIST_EXECUTOR_BACKEND)
-def test_models_pp2(model: str, tp_size: int, pp_size: int, distributed_executor_backend: str) -> None:
-    with VllmRunner(
-        model,
-        tensor_parallel_size=tp_size,
-        pipeline_parallel_size=pp_size,
-        cudagraph_capture_sizes=[1, 2, 4, 8],
-        distributed_executor_backend=distributed_executor_backend,
-        gpu_memory_utilization=0.7,
-    ) as vllm_model:
-        vllm_model.generate_greedy(prompts, 64)