[Tests] Add qwen3-8b nightly test (#5597)
### What this PR does / why we need it?
Add qwen3-8b nightly test
- vLLM version: v0.13.0
- vLLM main:
7157596103
---------
Signed-off-by: wxsIcey <1790571317@qq.com>
This commit is contained in:
Icey
3
.github/workflows/nightly_test_a2.yaml
vendored
3
.github/workflows/nightly_test_a2.yaml
vendored
@@ -49,6 +49,9 @@ jobs:
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fail-fast: false
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matrix:
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test_config:
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- name: qwen3-8b
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os: linux-aarch64-a2-1
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tests: tests/e2e/nightly/single_node/models/test_qwen3_8b.py
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- name: qwen3-32b
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os: linux-aarch64-a2-4
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tests: tests/e2e/nightly/single_node/models/test_qwen3_32b.py
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99
tests/e2e/nightly/single_node/models/test_qwen3_8b.py
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99
tests/e2e/nightly/single_node/models/test_qwen3_8b.py
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@@ -0,0 +1,99 @@
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# Copyright 2023 The vLLM team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This file is a part of the vllm-ascend project.
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#
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from typing import Any
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import openai
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import pytest
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from vllm.utils.network_utils import get_open_port
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from tests.e2e.conftest import RemoteOpenAIServer
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from tools.vllm_bench import run_vllm_bench_case
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MODELS = [
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"Qwen/Qwen3-8B",
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]
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prompts = [
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"San Francisco is a",
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]
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api_keyword_args = {
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"max_tokens": 10,
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}
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vllm_bench_cases = {
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"dataset-name": "random",
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"num_prompts": 1000,
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"request_rate": 20,
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"random_input_len": 128,
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"max_concurrency": 40,
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"random_output_len": 100,
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}
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baseline_throughput = 1622.08 # baseline throughput for Qwen3-8B
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.asyncio
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async def test_models(model: str) -> None:
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port = get_open_port()
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env_dict = {
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"TASK_QUEUE_ENABLE": "1",
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"HCCL_OP_EXPANSION_MODE": "AIV",
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"VLLM_ASCEND_ENABLE_PREFETCH_MLP": "1",
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}
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server_args = [
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"--async-scheduling",
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"--distributed-executor-backend",
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"mp",
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"--tensor-parallel-size",
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"1",
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"--port",
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str(port),
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"--max-model-len",
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"5500",
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"--max-num-batched-tokens",
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"40960",
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"--compilation-config",
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'{"cudagraph_mode": "FULL_DECODE_ONLY"}',
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"--additional-config",
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'{"pa_shape_list":[48,64,72,80]}',
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"--block-size",
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"128",
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"--trust-remote-code",
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"--gpu-memory-utilization",
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"0.9",
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]
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request_keyword_args: dict[str, Any] = {
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**api_keyword_args,
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}
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with RemoteOpenAIServer(model,
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server_args,
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server_port=port,
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env_dict=env_dict,
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auto_port=False) as server:
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client = server.get_async_client()
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batch = await client.completions.create(
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model=model,
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prompt=prompts,
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**request_keyword_args,
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)
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choices: list[openai.types.CompletionChoice] = batch.choices
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assert choices[0].text, "empty response"
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# vllm bench test
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run_vllm_bench_case(model, port, vllm_bench_cases, baseline_throughput)
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@@ -47,6 +47,8 @@ class VllmbenchRunner:
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model_name: str,
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port: int,
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config: dict,
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baseline: float,
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threshold: float = 0.97,
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model_path: str = "",
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host_ip: str = "localhost"):
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self.model_name = model_name
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@@ -60,10 +62,12 @@ class VllmbenchRunner:
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curr_time = datetime.now().strftime('%Y%m%d%H%M%S')
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self.result_filename = f"result_vllm_bench_{curr_time}.json"
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self.config = config
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self.baseline = baseline
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self.threshold = threshold
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self._run_vllm_bench_task()
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self._wait_for_task()
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self._get_result()
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self._performance_verify()
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def _concat_config_args(self, vllm_bench_cmd):
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if "ignore_eos" in self.config:
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@@ -87,16 +91,30 @@ class VllmbenchRunner:
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self.proc.kill()
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def _wait_for_task(self):
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result_msg = "========================="
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while True:
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line = self.proc.stdout.readline().strip()
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if line:
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print(line)
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if result_msg in line:
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return
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if "ERROR" in line:
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error_msg = f"Some errors happened to vllm_bench runtime, the first error is {line}"
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raise RuntimeError(error_msg) from None
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"""Wait for the vllm bench command to complete and check the execution result"""
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stdout, stderr = self.proc.communicate()
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if self.proc.returncode != 0:
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logging.error(
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f"vllm bench command failed, return code: {self.proc.returncode}"
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)
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logging.error(f"Standard output: {stdout}")
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logging.error(f"Standard error: {stderr}")
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raise RuntimeError(
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f"vllm bench command execution failed: {stderr}")
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logging.info(
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f"vllm bench command completed, return code: {self.proc.returncode}"
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)
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if stdout:
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lines = stdout.split('\n')
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last_lines = lines[-100:] if len(lines) > 100 else lines
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logging.info(f"Last {len(last_lines)} lines of standard output:")
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for line in last_lines:
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logging.info(line)
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else:
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logging.info("Standard output is empty")
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def _get_result(self):
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result_file = os.path.join(os.getcwd(), self.result_filename)
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@@ -104,16 +122,27 @@ class VllmbenchRunner:
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with open(result_file, 'r', encoding='utf-8') as f:
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self.result = json.load(f)
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def _performance_verify(self):
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self._get_result()
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output_throughput = self.result["output_throughput"]
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assert float(
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output_throughput
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) >= self.baseline * self.threshold, 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}."
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def run_vllm_bench_case(model_name,
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port,
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config,
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baseline,
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threshold=0.97,
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model_path="",
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host_ip="localhost"):
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try:
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with VllmbenchRunner(model_name,
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port,
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config,
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baseline,
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threshold,
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model_path=model_path,
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host_ip=host_ip) as vllm_bench:
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vllm_bench_result = vllm_bench.result
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