9e59fc1510
### What this PR does / why we need it? This PR adds the first aisbench case for nightly test, it lays a foundation for following performance and accuracy tests in nightly test. ### 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: Yikun Jiang <yikunkero@gmail.com> Co-authored-by: jiangyunfan1 <jiangyunfan1@h-partners.com>
100 lines
3.0 KiB
Python
100 lines
3.0 KiB
Python
# 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 import get_open_port
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from tests.e2e.conftest import RemoteOpenAIServer
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from tools.aisbench import run_aisbench_cases
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MODELS = [
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"Qwen/Qwen3-32B",
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]
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TENSOR_PARALLELS = [4]
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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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aisbench_cases = [{
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"case_type": "accuracy",
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"dataset_path": "vllm-ascend/gsm8k-lite",
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"request_conf": "vllm_api_general_chat",
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"dataset_conf": "gsm8k/gsm8k_gen_0_shot_cot_chat_prompt",
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"max_out_len": 32768,
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"batch_size": 32,
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"baseline": 95,
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"threshold": 5
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}, {
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"case_type": "performance",
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"dataset_path": "vllm-ascend/GSM8K-in3500-bs400",
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"request_conf": "vllm_api_stream_chat",
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"dataset_conf": "gsm8k/gsm8k_gen_0_shot_cot_str_perf",
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"num_prompts": 80,
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"max_out_len": 1500,
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"batch_size": 20,
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"request_rate": 0,
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"baseline": 1,
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"threshold": 0.97
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}]
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@pytest.mark.asyncio
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("tp_size", TENSOR_PARALLELS)
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async def test_models(model: str, tp_size: int) -> 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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"OMP_PROC_BIND": "false",
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"HCCL_OP_EXPANSION_MODE": "AIV",
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"PAGED_ATTENTION_MASK_LEN": "5500"
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}
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server_args = [
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"--no-enable-prefix-caching", "--tensor-parallel-size",
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str(tp_size), "--port",
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str(port), "--max-model-len", "36864", "--max-num-batched-tokens",
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"36864", "--block-size", "128", "--trust-remote-code",
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"--gpu-memory-utilization", "0.9", "--additional-config",
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'{"enable_weight_nz_layout":true}'
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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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# aisbench test
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run_aisbench_cases(model, port, aisbench_cases)
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