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)

tests/e2e/multicard/4-cards/test_pipeline_parallel.py

@@ -0,0 +1,88 @@
+# 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 DPVllmRunner, VllmRunner, wait_until_npu_memory_free
+from tests.e2e.model_utils import check_outputs_equal
+
+DS3 = "deepseek-ai/DeepSeek-V2-Lite-Chat"
+MODELS = [
+    DS3,
+]
+MOE_MODELS = [
+    DS3,
+]
+
+DATA_PARALLELS = [2]
+TENSOR_PARALLELS = [1,2]
+PIPELINE_PARALLELS = [2]
+DIST_EXECUTOR_BACKEND = ["mp", "ray"]
+
+prompts = [
+    "Hello, my name is",
+    "The future of AI is",
+]
+GOLDEN = [([100000, 17464, 11, 601, 1210, 317, 46462, 608, 245, 4541, 7712, 13, 2682, 6207, 317, 276, 2774, 340, 366, 254, 1608, 2784], 'Hello, my name is***** am a computer expert. My goal is to provide you with the best experience'), ([100000, 549, 3680, 280, 20838, 317, 6464, 11, 548, 359, 487, 82, 441, 1673, 895, 10694, 13, 1733, 20838, 5495, 11106, 276], 'The future of AI is bright, but it’s not without its challenges. As AI technology continues to')]
+
+
+
+@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)
+@wait_until_npu_memory_free(target_free_percentage=0.6)
+def test_models_pp2_tp2(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],
+        distributed_executor_backend=distributed_executor_backend,
+        gpu_memory_utilization=0.7,
+        enable_expert_parallel=model in MOE_MODELS,
+    ) as vllm_model:
+        outputs = vllm_model.generate_greedy(prompts, 16)
+        check_outputs_equal(
+            outputs_0_lst=outputs,
+            outputs_1_lst=GOLDEN,
+            name_0=f"{model}-tp{tp_size}pp{pp_size}",
+            name_1="GOLDEN",
+        )
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dp_size", DATA_PARALLELS)
+@pytest.mark.parametrize("pp_size", PIPELINE_PARALLELS)
+@pytest.mark.parametrize("distributed_executor_backend", DIST_EXECUTOR_BACKEND)
+@wait_until_npu_memory_free(target_free_percentage=0.6)
+def test_models_pp2_dp2(model: str, dp_size: int, pp_size: int, distributed_executor_backend: str) -> None:
+    with DPVllmRunner(
+        model,
+        data_parallel_size=dp_size,
+        pipeline_parallel_size=pp_size,
+        cudagraph_capture_sizes=[1, 2, 4],
+        distributed_executor_backend=distributed_executor_backend,
+        gpu_memory_utilization=0.7,
+        enable_expert_parallel=model in MOE_MODELS,
+    ) as vllm_model:
+        outputs = vllm_model.generate_greedy(prompts, 16)
+        check_outputs_equal(
+            outputs_0_lst=outputs,
+            outputs_1_lst=GOLDEN,
+            name_0=f"{model}-dp{dp_size}pp{pp_size}",
+            name_1="GOLDEN",
+        )
+