f60bb474f9
### What this PR does / why we need it?
Currently our workflow run time takes about 3 hours in total, which
seriously affects the developer experience, so it is urgent to have a
optimization, after this pr, It is expected that the running time of the
full CI can be shortened to 1h40min.
- Enable linux-aarch64-a2 (64GB) to replace linux-arm64-npu (32GB)
- Change TP4 ---> TP2 * 2 max-parallel
- Move DeepSeek-V2-Lite-W8A8 to single card test
### Does this PR introduce _any_ user-facing change?
No
- vLLM version: v0.10.0
- vLLM main:
a2480251ec
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
43 lines
1.3 KiB
Python
43 lines
1.3 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.
|
|
#
|
|
|
|
import pytest
|
|
from modelscope import snapshot_download # type: ignore[import-untyped]
|
|
|
|
from tests.e2e.conftest import VllmRunner
|
|
|
|
MODELS = [
|
|
"vllm-ascend/DeepSeek-V2-Lite-W8A8",
|
|
"vllm-ascend/Qwen2.5-0.5B-Instruct-W8A8"
|
|
]
|
|
|
|
|
|
@pytest.mark.parametrize("model", MODELS)
|
|
def test_quant_W8A8(example_prompts, model):
|
|
max_tokens = 5
|
|
model_path = snapshot_download(model)
|
|
with VllmRunner(
|
|
model_path,
|
|
max_model_len=8192,
|
|
enforce_eager=True,
|
|
dtype="auto",
|
|
gpu_memory_utilization=0.7,
|
|
quantization="ascend",
|
|
) as vllm_model:
|
|
vllm_model.generate_greedy(example_prompts, max_tokens)
|