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.

Setup a test environment

The fastest way to setup 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"

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

# Install vllm
cd /vllm-project/vllm
VLLM_TARGET_DEVICE=empty python3 -m pip -v install .

# Install vllm-ascend
cd /vllm-project/vllm-ascend
# [IMPORTANT] Import LD_LIBRARY_PATH to enumerate the CANN environment under CPU
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/Ascend/ascend-toolkit/latest/$(uname -m)-linux/devlib
python3 -m pip install -r requirements-dev.txt
python3 -m pip install -v .

::::

::::{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 \
    --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 \
    --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

::::

:::::

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_v1.py --> tests/ut/worker/test_worker_v1.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 the 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 single card the 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 the 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 the single card tests
VLLM_USE_MODELSCOPE=true pytest -sv tests/e2e/multicard/

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

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

::::

:::::

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

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.py

• Reduced Layer model test example: test_torchair_graph_mode.py - DeepSeek-V3-Pruning

  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)
     

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.