add qwq testcase

.github/workflows/vllm_ascend_test_nightly_a3.yaml

@@ -68,6 +68,9 @@ jobs:
           - name: deepseek-r1-0528-w8a8-prefix-cache
             os: linux-aarch64-a3-16
             tests: tests/e2e/nightly/features/test_prefix_cache_deepseek_r1_0528_w8a8.py
+          - name: qwq-32b-a3
+            os: linux-aarch64-a3-4
+            tests: tests/e2e/nightly/models/test_qwq_32b.py
     uses: ./.github/workflows/_e2e_nightly_single_node.yaml
     with:
       vllm: v0.11.0

tests/e2e/nightly/models/test_qwq_32b.py

@@ -0,0 +1,120 @@
+# 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.
+#
+from typing import Any
+
+import openai
+import pytest
+from vllm.utils import get_open_port
+
+from tests.e2e.conftest import RemoteOpenAIServer
+from tools.aisbench import run_aisbench_cases
+
+MODELS = [
+    "Qwen/QwQ-32B",
+]
+
+MODES = [
+    "aclgraph",
+    "single",
+]
+
+TENSOR_PARALLELS = [4]
+
+prompts = [
+    "San Francisco is a",
+]
+
+api_keyword_args = {
+    "max_tokens": 10,
+}
+
+aisbench_cases = [{
+    "case_type": "accuracy",
+    "dataset_path": "vllm-ascend/gsm8k-lite",
+    "request_conf": "vllm_api_general_chat",
+    "dataset_conf": "gsm8k/gsm8k_gen_0_shot_cot_chat_prompt",
+    "max_out_len": 32768,
+    "batch_size": 32,
+    "baseline": 95,
+    "threshold": 5
+}, {
+    "case_type": "performance",
+    "dataset_path": "vllm-ascend/GSM8K-in3500-bs400",
+    "request_conf": "vllm_api_stream_chat",
+    "dataset_conf": "gsm8k/gsm8k_gen_0_shot_cot_str_perf",
+    "num_prompts": 176,
+    "max_out_len": 1500,
+    "batch_size": 44,
+    "baseline": 1,
+    "threshold": 0.97
+}]
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("mode", MODES)
+@pytest.mark.parametrize("tp_size", TENSOR_PARALLELS)
+async def test_models(model: str, mode: str, tp_size: int) -> None:
+    port = get_open_port()
+    env_dict = {
+        "TASK_QUEUE_ENABLE": "1",
+        "OMP_PROC_BIND": "false",
+        "VLLM_USE_V1": "1",
+        "HCCL_OP_EXPANSION_MODE": "AIV",
+        "VLLM_ASCEND_ENABLE_FLASHCOMM": "1",
+        "VLLM_ASCEND_ENABLE_TOPK_OPTIMIZE": "1",
+        "VLLM_ASCEND_ENABLE_DEBSE_OPTIMIZE": "1",
+        "VLLM_ASCEND_ENABLE_PREFETCH": "1"
+    }
+    server_args = [
+        "--tensor-parallel-size",
+        str(tp_size), "--port",
+        str(port), "--max-model-len", "36864", "--max-num-batched-tokens",
+        "36864", "--block-size", "128", "--trust-remote-code",
+        "--gpu-memory-utilization", "0.9", "--compilation_config",
+        '{"cudagraph_mode":"FULL_DECODE_ONLY", "cudagraph_capture_sizes": [1, 8, 24, 48, 60]}',
+        "--reasoning-parser", "deepseek_r1", "--distributed_executor_backend",
+        "mp"
+    ]
+    if mode == "single":
+        server_args.remove("--compilation_config")
+        server_args.remove(
+            '{"cudagraph_mode":"FULL_DECODE_ONLY", "cudagraph_capture_sizes": [1, 8, 24, 48, 60]}'
+        )
+        server_args.append("--additional-config")
+        server_args.append('{"ascend_scheduler_config":{"enabled":true}}')
+        server_args.append("--enforce-eager")
+    request_keyword_args: dict[str, Any] = {
+        **api_keyword_args,
+    }
+    with RemoteOpenAIServer(model,
+                            server_args,
+                            server_port=port,
+                            env_dict=env_dict,
+                            auto_port=False) as server:
+        client = server.get_async_client()
+        batch = await client.completions.create(
+            model=model,
+            prompt=prompts,
+            **request_keyword_args,
+        )
+        choices: list[openai.types.CompletionChoice] = batch.choices
+        assert choices[0].text, "empty response"
+        if mode == "single":
+            return
+        # aisbench test
+        run_aisbench_cases(model, port, aisbench_cases)