Files
xc-llm-ascend/tests/e2e/multicard/test_qwen3_next.py
zhangxinyuehfad 8f6f967028 [Test] Add e2e test and accuracy test for Qwen3-Next-80B-A3B-Instruct (#3450)
### What this PR does / why we need it?

Add e2e test and accuracy test for Qwen3-Next-80B-A3B-Instruct

### How was this patch tested?
accuracy test:
https://github.com/vllm-project/vllm-ascend/actions/runs/18771221544/job/53556027634?pr=3450
ci test:
https://github.com/vllm-project/vllm-ascend/actions/runs/18771221530/job/53556027614?pr=3450
<img width="1703" height="562" alt="image"
src="https://github.com/user-attachments/assets/973b6cfa-8240-41e3-893a-5024ff8d0693"
/>



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

Signed-off-by: hfadzxy <starmoon_zhang@163.com>
2025-10-25 10:57:56 +08:00

39 lines
1.4 KiB
Python

#
# 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_next.py`.
"""
from tests.e2e.conftest import VllmRunner
def test_models_distributed_Qwen3_NEXT_TP4():
example_prompts = [
"Hello, my name is",
]
max_tokens = 5
with VllmRunner("Qwen/Qwen3-Next-80B-A3B-Instruct",
tensor_parallel_size=4,
max_model_len=4096,
gpu_memory_utilization=0.7,
distributed_executor_backend="mp",
enforce_eager=True) as vllm_model:
vllm_model.generate_greedy(example_prompts, max_tokens)