xc-llm-ascend/docs/source/developer_guide/contribution/testing.md

Testing

This document explains how to write E2E tests and unit tests to verify the implementation of your feature.

Set up a test environment

The fastest way to set up a test environment is to use the main branch's container image:

:::::{tab-set} :sync-group: e2e

::::{tab-item} Local (CPU) :selected: :sync: cpu

You can run the unit tests on CPUs with the following steps:

   :substitutions:

cd ~/vllm-project/
# ls
# vllm  vllm-ascend

# Use mirror to speed up download
# docker pull quay.nju.edu.cn/ascend/cann:|cann_image_tag|
export IMAGE=quay.io/ascend/cann:|cann_image_tag|
docker run --rm --name vllm-ascend-ut \
    -v $(pwd):/vllm-project \
    -v ~/.cache:/root/.cache \
    -ti $IMAGE bash

# (Optional) Configure mirror to speed up download
sed -i 's|ports.ubuntu.com|mirrors.huaweicloud.com|g' /etc/apt/sources.list
pip config set global.index-url https://mirrors.huaweicloud.com/repository/pypi/simple/

# For torch-npu dev version or x86 machine
export PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cpu/ https://mirrors.huaweicloud.com/ascend/repos/pypi"

# src path
export SRC_WORKSPACE=/vllm-workspace
mkdir -p $SRC_WORKSPACE

apt-get update -y
apt-get install -y python3-pip git vim wget net-tools gcc g++ cmake libnuma-dev curl gnupg2

git clone -b |vllm_ascend_version| --depth 1 https://github.com/vllm-project/vllm-ascend.git
git clone --depth 1 https://github.com/vllm-project/vllm.git

# vllm
cd $SRC_WORKSPACE/vllm
VLLM_TARGET_DEVICE=empty python3 -m pip install .
python3 -m pip uninstall -y triton

# vllm-ascend
cd $SRC_WORKSPACE/vllm-ascend
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/Ascend/ascend-toolkit/latest/$(uname -m)-linux/devlib
# For cpu environment, set SOC_VERSION for different chips.
# See https://github.com/vllm-project/vllm-ascend/blob/3cb0af0bcf3299089ca7e72159fa36e825a470f8/setup.py#L132 for detail.
export SOC_VERSION="ascend910b1"
python3 -m pip install -v .
python3 -m pip install -r requirements-dev.txt

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::::{tab-item} Single card :sync: single

   :substitutions:

# Update DEVICE according to your device (/dev/davinci[0-7])
export DEVICE=/dev/davinci0
# Update the vllm-ascend image
export IMAGE=quay.io/ascend/vllm-ascend:main
docker run --rm \
    --name vllm-ascend \
    --shm-size=1g \
    --device $DEVICE \
    --device /dev/davinci_manager \
    --device /dev/devmm_svm \
    --device /dev/hisi_hdc \
    -v /usr/local/dcmi:/usr/local/dcmi \
    -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
    -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
    -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
    -v /etc/ascend_install.info:/etc/ascend_install.info \
    -v /root/.cache:/root/.cache \
    -p 8000:8000 \
    -it $IMAGE bash

After starting the container, you should install the required packages:

# Prepare
pip config set global.index-url https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple

# Install required packages
pip install -r requirements-dev.txt

::::

::::{tab-item} Multi cards :sync: multi

   :substitutions:
# Update the vllm-ascend image
export IMAGE=quay.io/ascend/vllm-ascend:main
docker run --rm \
    --name vllm-ascend \
    --shm-size=1g \
    --device /dev/davinci0 \
    --device /dev/davinci1 \
    --device /dev/davinci2 \
    --device /dev/davinci3 \
    --device /dev/davinci_manager \
    --device /dev/devmm_svm \
    --device /dev/hisi_hdc \
    -v /usr/local/dcmi:/usr/local/dcmi \
    -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
    -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
    -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
    -v /etc/ascend_install.info:/etc/ascend_install.info \
    -v /root/.cache:/root/.cache \
    -p 8000:8000 \
    -it $IMAGE bash

After starting the container, you should install the required packages:

cd /vllm-workspace/vllm-ascend/

# Prepare
pip config set global.index-url https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple

# Install required packages
pip install -r requirements-dev.txt

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:::::

Running tests

Unit tests

There are several principles to follow when writing unit tests:

• The test file path should be consistent with the source file and start with the test_ prefix, such as: vllm_ascend/worker/worker.py --> tests/ut/worker/test_worker.py
• The vLLM Ascend test uses unittest framework. See here to understand how to write unit tests.
• All unit tests can be run on CPUs, so you must mock the device-related function to host.
• Example: tests/ut/test_ascend_config.py.
• You can run the unit tests using pytest:

:::::{tab-set} :sync-group: e2e

::::{tab-item} Local (CPU) :selected: :sync: cpu

# Run unit tests
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/Ascend/ascend-toolkit/latest/$(uname -m)-linux/devlib
TORCH_DEVICE_BACKEND_AUTOLOAD=0 pytest -sv tests/ut

::::

::::{tab-item} Single-card :sync: single

cd /vllm-workspace/vllm-ascend/
# Run all single-card tests
pytest -sv tests/ut

# Run single test
pytest -sv tests/ut/test_ascend_config.py

::::

::::{tab-item} Multi-card :sync: multi

cd /vllm-workspace/vllm-ascend/
# Run all multi-card tests
pytest -sv tests/ut

# Run single test
pytest -sv tests/ut/test_ascend_config.py

::::

:::::

E2E test

Although vllm-ascend CI provides the E2E test on Ascend CI, you can run it locally.

:::::{tab-set} :sync-group: e2e

::::{tab-item} Local (CPU) :sync: cpu

You can't run the E2E test on CPUs. ::::

::::{tab-item} Single-card :selected: :sync: single

cd /vllm-workspace/vllm-ascend/
# Run all single-card tests
VLLM_USE_MODELSCOPE=true pytest -sv tests/e2e/singlecard/

# Run a certain test script
VLLM_USE_MODELSCOPE=true pytest -sv tests/e2e/singlecard/test_offline_inference.py

# Run a certain case in test script
VLLM_USE_MODELSCOPE=true pytest -sv tests/e2e/singlecard/test_offline_inference.py::test_models

::::

::::{tab-item} Multi-card :sync: multi

cd /vllm-workspace/vllm-ascend/
# Run all multi-card tests
VLLM_USE_MODELSCOPE=true pytest -sv tests/e2e/singlecard/

# Run a certain test script
VLLM_USE_MODELSCOPE=true pytest -sv tests/e2e/singlecard/test_aclgraph_accuracy.py

# Run a certain case in test script
VLLM_USE_MODELSCOPE=true pytest -sv tests/e2e/singlecard/test_aclgraph_accuracy.py::test_models_output

::::

:::::

This will reproduce the E2E test. See vllm_ascend_test.yaml.

For running nightly multi-node test cases locally, refer to the Running Locally section in Multi Node Test.

E2E test example

• Offline test example: tests/e2e/singlecard/test_offline_inference.py

• Online test examples: tests/e2e/singlecard/test_prompt_embedding.py

• Correctness test example: tests/e2e/singlecard/test_aclgraph_accuracy.py

  The CI resource is limited, and you might need to reduce the number of layers of a model. Below is an example of how to generate a reduced layer model:

  1. Fork the original model repo in modelscope. All the files in the repo except for weights are required.

  2. Set num_hidden_layers to the expected number of layers, e.g., {"num_hidden_layers": 2,}

  3. Copy the following python script as generate_random_weight.py. Set the relevant parameters MODEL_LOCAL_PATH, DIST_DTYPE and DIST_MODEL_PATH as needed:

     import torch
     from transformers import AutoTokenizer, AutoConfig
     from modeling_deepseek import DeepseekV3ForCausalLM
     from modelscope import snapshot_download
     
     MODEL_LOCAL_PATH = "~/.cache/modelscope/models/vllm-ascend/DeepSeek-V3-Pruning"
     DIST_DTYPE = torch.bfloat16
     DIST_MODEL_PATH = "./random_deepseek_v3_with_2_hidden_layer"
     
     config = AutoConfig.from_pretrained(MODEL_LOCAL_PATH, trust_remote_code=True)
     model = DeepseekV3ForCausalLM(config)
     model = model.to(DIST_DTYPE)
     model.save_pretrained(DIST_MODEL_PATH)
     

View CI log summary in GitHub Actions

After a CI job finishes, you can open the corresponding GitHub Actions job page and check the Summary tab to view the generated CI log summary.

The summary is intended to help developers triage failures more quickly. It may include:

• failed test files
• failed test cases
• distinct root-cause errors
• short error context extracted from the job log

This summary is generated from the job log by /.github/workflows/scripts/ci_log_summary_v2.py for unit-test and e2e workflows.

Run doctest

vllm-ascend provides a vllm-ascend/tests/e2e/run_doctests.sh command to run all doctests in the doc files. The doctest is a good way to make sure docs stay current and examples remain executable, which can be run locally as follows:

# Run doctest
/vllm-workspace/vllm-ascend/tests/e2e/run_doctests.sh

This will reproduce the same environment as the CI. See vllm_ascend_doctest.yaml.