support basic long_seq feature st

.github/workflows/_e2e_test.yaml

@@ -267,6 +267,7 @@ 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_basic.py
 
       - name: Install Ascend toolkit & triton_ascend (for Qwen3-Next-80B-A3B-Instruct)
         shell: bash -l {0}

tests/e2e/multicard/long_sequence/test_basic.py

@@ -0,0 +1,141 @@
+#
+# 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_qwen3_moe.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["HCCL_BUFFSIZE"] = "768"
+
+
+@pytest.mark.skipif(vllm_version_is('0.12.0'),
+                    reason="0.12.0 is not supported for context sequence.")
+def test_pcp_dcp_basic():
+    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)
+
+    model = "vllm-ascend/Qwen3-30B-A3B-W8A8"
+    with VllmRunner(
+            model,
+            enforce_eager=True,
+            max_model_len=1024,
+            tensor_parallel_size=2,
+            prefill_context_parallel_size=2,
+            decode_context_parallel_size=1,
+            enable_expert_parallel=True,
+            block_size=128,
+            quantization="ascend",
+    ) as runner:
+        runner.model.generate(prompts, sampling_params)
+
+
+@pytest.mark.skipif(vllm_version_is('0.12.0'),
+                    reason="0.12.0 is not supported for context sequence.")
+def test_pcp_dcp_full_graph():
+    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=False,
+                    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,
+                    compilation_config={
+                        "cudagraph_mode": "FULL_DECODE_ONLY",
+                        "cudagraph_capture_sizes": [4, 8, 24, 48, 60]
+                    }) as runner:
+        runner.model.generate(prompts, sampling_params)
+
+    model = "vllm-ascend/Qwen3-30B-A3B-W8A8"
+    with VllmRunner(model,
+                    enforce_eager=False,
+                    max_model_len=1024,
+                    tensor_parallel_size=2,
+                    prefill_context_parallel_size=2,
+                    decode_context_parallel_size=1,
+                    enable_expert_parallel=True,
+                    block_size=128,
+                    quantization="ascend",
+                    compilation_config={
+                        "cudagraph_mode": "FULL_DECODE_ONLY",
+                        "cudagraph_capture_sizes": [4, 8, 24, 48, 60]
+                    }) as runner:
+        runner.model.generate(prompts, sampling_params)
+
+
+@pytest.mark.skipif(vllm_version_is('0.12.0'),
+                    reason="0.12.0 is not supported for context sequence.")
+def test_pcp_dcp_piece_wise():
+    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=False,
+                    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)
+
+    model = "vllm-ascend/Qwen3-30B-A3B-W8A8"
+    with VllmRunner(model,
+                    enforce_eager=False,
+                    max_model_len=1024,
+                    tensor_parallel_size=2,
+                    prefill_context_parallel_size=2,
+                    decode_context_parallel_size=1,
+                    enable_expert_parallel=True,
+                    block_size=128,
+                    quantization="ascend") as runner:
+        runner.model.generate(prompts, sampling_params)