[TEST]Add initial prefix cache case for nightly test (#3709)

### What this PR does / why we need it?
This PR adds the initial prefix cache case for nightly test for
Qwen3-32b-int8 on A3, we need test them daily.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By running the test

- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0

Signed-off-by: jiangyunfan1 <[email protected]>

Author: jiangyunfan1

tests/e2e/nightly/features/test_prefix_cache_qwen3_32b_int8.py

@@ -0,0 +1,104 @@
+# 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 json
+
+import pytest
+from vllm.utils import get_open_port
+
+from tests.e2e.conftest import RemoteOpenAIServer
+from tools.aisbench import get_TTFT, run_aisbench_cases
+
+MODELS = [
+    "vllm-ascend/Qwen3-32B-W8A8",
+]
+
+aisbench_warm_up = [{
+    "case_type": "performance",
+    "dataset_path": "vllm-ascend/GSM8K-in1024-bs210",
+    "request_conf": "vllm_api_stream_chat",
+    "dataset_conf": "gsm8k/gsm8k_gen_0_shot_cot_str_perf",
+    "num_prompts": 210,
+    "max_out_len": 2,
+    "batch_size": 1000,
+    "baseline": 0,
+    "threshold": 0.97
+}]
+
+aisbench_cases0 = [{
+    "case_type": "performance",
+    "dataset_path": "vllm-ascend/prefix0-in3500-bs210",
+    "request_conf": "vllm_api_stream_chat",
+    "dataset_conf": "gsm8k/gsm8k_gen_0_shot_cot_str_perf",
+    "num_prompts": 210,
+    "max_out_len": 1500,
+    "batch_size": 48,
+    "baseline": 1,
+    "threshold": 0.97
+}]
+
+aisbench_cases75 = [{
+    "case_type": "performance",
+    "dataset_path": "vllm-ascend/prefix75-in3500-bs210",
+    "request_conf": "vllm_api_stream_chat",
+    "dataset_conf": "gsm8k/gsm8k_gen_0_shot_cot_str_perf",
+    "num_prompts": 210,
+    "max_out_len": 1500,
+    "batch_size": 48,
+    "baseline": 1,
+    "threshold": 0.97
+}]
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("model", MODELS)
+async def test_models(model: str) -> None:
+    port = get_open_port()
+    env_dict = {"TASK_QUEUE_ENABLE": "1", "HCCL_OP_EXPANSION_MODE": "AIV"}
+    additional_config = {
+        "ascend_scheduler_config": {
+            "enabled": False
+        },
+        "enable_weight_nz_layout": True
+    }
+    server_args = [
+        "--quantization", "ascend", "--reasoning-parser", "qwen3",
+        "--tensor-parallel-size", "4", "--port",
+        str(port), "--max-model-len", "8192", "--max-num-batched-tokens",
+        "8192", "--max-num-seqs", "256", "--trust-remote-code",
+        "--gpu-memory-utilization", "0.9", "--additional-config",
+        json.dumps(additional_config)
+    ]
+    with RemoteOpenAIServer(model,
+                            server_args,
+                            server_port=port,
+                            env_dict=env_dict,
+                            auto_port=False):
+        run_aisbench_cases(model, port, aisbench_warm_up)
+        result = run_aisbench_cases(model, port, aisbench_cases0)
+        TTFT0 = get_TTFT(result)
+    with RemoteOpenAIServer(model,
+                            server_args,
+                            server_port=port,
+                            env_dict=env_dict,
+                            auto_port=False):
+        run_aisbench_cases(model, port, aisbench_warm_up)
+        result = run_aisbench_cases(model, port, aisbench_cases75)
+        TTFT75 = get_TTFT(result)
+    assert TTFT75 < 0.4 * TTFT0, f"The TTFT for prefix75 {TTFT75} is not less than 0.4*TTFT for prefix0 {TTFT0}."
+    print(
+        f"The TTFT for prefix75 {TTFT75} is less than 0.4*TTFT for prefix0 {TTFT0}."
+    )

tools/aisbench.py

@@ -205,15 +205,16 @@ def _get_result_performance(self):
                                        f"{dataset_type}dataset.csv")
         result_json_file = os.path.join(result_dir,
                                         f"{dataset_type}dataset.json")
-        self.result_csv = pd.read_csv(result_csv_file)
+        self.result_csv = pd.read_csv(result_csv_file, index_col=0)
         print("Getting performance results from file: ", result_json_file)
         with open(result_json_file, 'r', encoding='utf-8') as f:
             self.result_json = json.load(f)
+        self.result = [self.result_csv, self.result_json]
 
     def _get_result_accuracy(self):
         acc_file = re.search(r'write csv to (.*)', self.result_line).group(1)
         df = pd.read_csv(acc_file)
-        return float(df.loc[0][-1])
+        self.result = float(df.loc[0][-1])
 
     def _performance_verify(self):
         self._get_result_performance()
@@ -224,21 +225,30 @@ def _performance_verify(self):
         ) >= self.threshold * self.baseline, f"Performance verification failed. The current Output Token Throughput is {output_throughput} token/s, which is not greater than or equal to {self.threshold} * baseline {self.baseline}."
 
     def _accuracy_verify(self):
-        acc_value = self._get_result_accuracy()
+        self._get_result_accuracy()
+        acc_value = self.result
         assert self.baseline - self.threshold <= acc_value <= self.baseline + self.threshold, f"Accuracy verification failed. The accuracy of {self.dataset_path} is {acc_value}, which is not within {self.threshold} relative to baseline {self.baseline}."
 
 
 def run_aisbench_cases(model, port, aisbench_cases):
+    aisbench_results = []
     aisbench_errors = []
     for aisbench_case in aisbench_cases:
         try:
-            with AisbenchRunner(model, port, aisbench_case):
-                pass
+            with AisbenchRunner(model, port, aisbench_case) as aisbench:
+                aisbench_results.append(aisbench.result)
         except Exception as e:
+            aisbench_results.append("")
             aisbench_errors.append([aisbench_case, e])
             print(e)
     for failed_case, error_info in aisbench_errors:
         print(
             f"The following aisbench case failed: {failed_case}, reason is {error_info}."
         )
     assert not aisbench_errors, "some aisbench cases failed, info were shown above."
+    return aisbench_results
+
+
+def get_TTFT(result):
+    TTFT = result[0][0].loc["TTFT", "Average"][:-3]
+    return float(TTFT)