[Feat] Adapt FlashComm2 with PCP

.github/workflows/_e2e_test.yaml

@@ -203,6 +203,7 @@ jobs:
           pytest -sv --durations=0 tests/e2e/multicard/test_qwen3_moe.py
           pytest -sv --durations=0 tests/e2e/multicard/test_offline_weight_load.py
 
+
   e2e-4-cards:
     name: multicard-4
     needs: [e2e, e2e-2-cards]
@@ -267,6 +268,7 @@ jobs:
           pytest -sv --durations=0 tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_DeepSeek_multistream_moe
           pytest -sv --durations=0 tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_Kimi_K2_Thinking_W4A16
           pytest -sv --durations=0 tests/e2e/multicard/test_data_parallel_tp2.py
+          pytest -sv --durations=0 tests/e2e/multicard/long_sequence/test_flashcomm2.py
           pytest -sv --durations=0 tests/e2e/multicard/long_sequence/test_basic.py
 
       - name: Install Ascend toolkit & triton_ascend (for Qwen3-Next-80B-A3B-Instruct)

tests/e2e/multicard/long_sequence/test_flashcomm2.py

@@ -0,0 +1,54 @@
+#
+# 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.
+# Adapted from vllm/tests/basic_correctness/test_basic_correctness.py
+#
+"""Compare the short outputs of HF and vLLM when using greedy sampling.
+
+Run `pytest tests/e2e/multicard/test_flashcomm2.py`.
+"""
+
+import os
+
+import pytest
+from vllm import SamplingParams
+
+from tests.e2e.conftest import VllmRunner
+from vllm_ascend.utils import vllm_version_is
+
+os.environ["VLLM_ASCEND_ENABLE_FLASHCOMM2_OSHARED"] = "1"
+os.environ["VLLM_ASCEND_FLASHCOMM2_PARALLEL_SIZE"] = "1"
+
+
+@pytest.mark.skipif(vllm_version_is('0.12.0'),
+                    reason="0.12.0 is not supported for context sequence.")
+def test_pcp_dcp_flashcomm2():
+    prompts = [
+        "The capital of France is", "Hello, my name is Tom, I am",
+        "The president of United States is", "AI future is"
+    ]
+    model = "deepseek-ai/DeepSeek-V2-Lite-Chat"
+    sampling_params = SamplingParams(max_tokens=32, temperature=0.0)
+    with VllmRunner(model,
+                    enforce_eager=True,
+                    max_model_len=1024,
+                    tensor_parallel_size=2,
+                    prefill_context_parallel_size=2,
+                    decode_context_parallel_size=2,
+                    max_num_batched_tokens=1024,
+                    enable_expert_parallel=True,
+                    block_size=128) as runner:
+        runner.model.generate(prompts, sampling_params)

vllm_ascend/distributed/parallel_state.py

@@ -41,6 +41,7 @@ def init_ascend_model_parallel(parallel_config: ParallelConfig, ):
     global_tp_size = parallel_config.tensor_parallel_size
     global_dp_size = parallel_config.data_parallel_size
     global_pp_size = parallel_config.pipeline_parallel_size
+    global_pcp_size = parallel_config.prefill_context_parallel_size
 
     # The layout of all ranks: ExternalDP * EP
     # ExternalDP is the data parallel group that is not part of the model,
@@ -154,16 +155,22 @@ def _create_shared_weight_group(group_name: str) -> GroupCoordinator:
         for pp_idx in range(global_pp_size):
             group = []
             for dp_idx in range(global_dp_size):
-                base = (dp_idx * global_pp_size + pp_idx) * global_tp_size
-                for i in range(global_tp_size):
-                    global_rank = base + i
-                    group.append(global_rank)
+                for pcp_idx in range(global_pcp_size):
+                    base = (dp_idx * global_pp_size * global_pcp_size *
+                            global_tp_size +
+                            pp_idx * global_pcp_size * global_tp_size +
+                            pcp_idx * global_tp_size)
+                    for tp_idx in range(global_tp_size):
+                        global_rank = base + tp_idx
+                        group.append(global_rank)
             group_ranks.append(group)
 
-        return init_model_parallel_group(group_ranks,
-                                         get_world_group().local_rank,
-                                         backend,
-                                         group_name=group_name)
+        return init_model_parallel_group(
+            group_ranks,
+            get_world_group().local_rank,
+            backend,
+            group_name=group_name,
+        )
 
     global _SHARED_WEIGHT
     # TODO: Check if the model is Deepseek V3.2 with enabled SFA CP and activated shared weights. It will then be normalized within the PCP parameters. -- clrs97