75de3fa172
### What this PR does / why we need it? Update doc Signed-off-by: hfadzxy <starmoon_zhang@163.com>
286 lines
8.8 KiB
Markdown
286 lines
8.8 KiB
Markdown
# Testing
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This document explains how to write E2E tests and unit tests to verify the implementation of your feature.
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## Setup a test environment
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The fastest way to setup a test environment is to use the main branch's container image:
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:::::{tab-set}
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:sync-group: e2e
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::::{tab-item} Local (CPU)
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:selected:
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:sync: cpu
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You can run the unit tests on CPUs with the following steps:
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```{code-block} bash
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:substitutions:
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cd ~/vllm-project/
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# ls
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# vllm vllm-ascend
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# Use mirror to speed up download
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# docker pull quay.nju.edu.cn/ascend/cann:|cann_image_tag|
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export IMAGE=quay.io/ascend/cann:|cann_image_tag|
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docker run --rm --name vllm-ascend-ut \
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-v $(pwd):/vllm-project \
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-v ~/.cache:/root/.cache \
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-ti $IMAGE bash
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# (Optional) Configure mirror to speed up download
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sed -i 's|ports.ubuntu.com|mirrors.huaweicloud.com|g' /etc/apt/sources.list
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pip config set global.index-url https://mirrors.huaweicloud.com/repository/pypi/simple/
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# For torch-npu dev version or x86 machine
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export PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cpu/ https://mirrors.huaweicloud.com/ascend/repos/pypi"
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apt-get update -y
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apt-get install -y python3-pip git vim wget net-tools gcc g++ cmake libnuma-dev curl gnupg2
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# Install vllm
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cd /vllm-project/vllm
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VLLM_TARGET_DEVICE=empty python3 -m pip -v install .
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# Install vllm-ascend
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cd /vllm-project/vllm-ascend
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# [IMPORTANT] Import LD_LIBRARY_PATH to enumerate the CANN environment under CPU
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export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/Ascend/ascend-toolkit/latest/$(uname -m)-linux/devlib
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python3 -m pip install -r requirements-dev.txt
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python3 -m pip install -v .
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```
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::::
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::::{tab-item} Single card
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:sync: single
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```{code-block} bash
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:substitutions:
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# Update DEVICE according to your device (/dev/davinci[0-7])
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export DEVICE=/dev/davinci0
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# Update the vllm-ascend image
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export IMAGE=quay.io/ascend/vllm-ascend:main
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docker run --rm \
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--name vllm-ascend \
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--device $DEVICE \
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--device /dev/davinci_manager \
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--device /dev/devmm_svm \
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--device /dev/hisi_hdc \
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-v /usr/local/dcmi:/usr/local/dcmi \
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-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
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-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
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-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
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-v /etc/ascend_install.info:/etc/ascend_install.info \
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-v /root/.cache:/root/.cache \
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-p 8000:8000 \
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-it $IMAGE bash
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```
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After starting the container, you should install the required packages:
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```bash
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# Prepare
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pip config set global.index-url https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple
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# Install required packages
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pip install -r requirements-dev.txt
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```
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::::
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::::{tab-item} Multi cards
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:sync: multi
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```{code-block} bash
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:substitutions:
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# Update the vllm-ascend image
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export IMAGE=quay.io/ascend/vllm-ascend:main
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docker run --rm \
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--name vllm-ascend \
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--device /dev/davinci0 \
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--device /dev/davinci1 \
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--device /dev/davinci2 \
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--device /dev/davinci3 \
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--device /dev/davinci_manager \
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--device /dev/devmm_svm \
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--device /dev/hisi_hdc \
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-v /usr/local/dcmi:/usr/local/dcmi \
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-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
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-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
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-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
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-v /etc/ascend_install.info:/etc/ascend_install.info \
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-v /root/.cache:/root/.cache \
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-p 8000:8000 \
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-it $IMAGE bash
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```
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After starting the container, you should install the required packages:
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```bash
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cd /vllm-workspace/vllm-ascend/
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# Prepare
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pip config set global.index-url https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple
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# Install required packages
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pip install -r requirements-dev.txt
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```
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::::
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:::::
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## Running tests
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### Unit tests
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There are several principles to follow when writing unit tests:
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- 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`
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- The vLLM Ascend test uses unittest framework. See [here](https://docs.python.org/3/library/unittest.html#module-unittest) to understand how to write unit tests.
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- All unit tests can be run on CPUs, so you must mock the device-related function to host.
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- Example: [tests/ut/test_ascend_config.py](https://github.com/vllm-project/vllm-ascend/blob/main/tests/ut/test_ascend_config.py).
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- You can run the unit tests using `pytest`:
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:::::{tab-set}
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:sync-group: e2e
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::::{tab-item} Local (CPU)
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:selected:
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:sync: cpu
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```bash
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# Run unit tests
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export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/Ascend/ascend-toolkit/latest/$(uname -m)-linux/devlib
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TORCH_DEVICE_BACKEND_AUTOLOAD=0 pytest -sv tests/ut
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```
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::::
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::::{tab-item} Single-card
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:sync: single
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```bash
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cd /vllm-workspace/vllm-ascend/
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# Run all single card the tests
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pytest -sv tests/ut
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# Run single test
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pytest -sv tests/ut/test_ascend_config.py
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```
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::::
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::::{tab-item} Multi-card
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:sync: multi
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```bash
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cd /vllm-workspace/vllm-ascend/
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# Run all single card the tests
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pytest -sv tests/ut
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# Run single test
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pytest -sv tests/ut/test_ascend_config.py
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```
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::::
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:::::
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### E2E test
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Although vllm-ascend CI provides the [E2E test](https://github.com/vllm-project/vllm-ascend/blob/main/.github/workflows/vllm_ascend_test.yaml) on Ascend CI, you can run it
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locally.
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:::::{tab-set}
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:sync-group: e2e
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::::{tab-item} Local (CPU)
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:sync: cpu
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You can't run the E2E test on CPUs.
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::::
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::::{tab-item} Single-card
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:selected:
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:sync: single
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```bash
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cd /vllm-workspace/vllm-ascend/
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# Run all single card the tests
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VLLM_USE_MODELSCOPE=true pytest -sv tests/e2e/singlecard/
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# Run a certain test script
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VLLM_USE_MODELSCOPE=true pytest -sv tests/e2e/singlecard/test_offline_inference.py
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# Run a certain case in test script
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VLLM_USE_MODELSCOPE=true pytest -sv tests/e2e/singlecard/test_offline_inference.py::test_models
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```
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::::
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::::{tab-item} Multi-card
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:sync: multi
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```bash
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cd /vllm-workspace/vllm-ascend/
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# Run all the single card tests
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VLLM_USE_MODELSCOPE=true pytest -sv tests/e2e/multicard/
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# Run a certain test script
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VLLM_USE_MODELSCOPE=true pytest -sv tests/e2e/multicard/test_dynamic_npugraph_batchsize.py
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# Run a certain case in test script
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VLLM_USE_MODELSCOPE=true pytest -sv tests/e2e/multicard/test_offline_inference.py::test_models
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```
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::::
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:::::
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This will reproduce the E2E test. See [vllm_ascend_test.yaml](https://github.com/vllm-project/vllm-ascend/blob/main/.github/workflows/vllm_ascend_test.yaml).
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#### E2E test example:
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- Offline test example: [`tests/e2e/singlecard/test_offline_inference.py`](https://github.com/vllm-project/vllm-ascend/blob/main/tests/e2e/singlecard/test_offline_inference.py)
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- Online test examples: [`tests/e2e/singlecard/test_prompt_embedding.py`](https://github.com/vllm-project/vllm-ascend/blob/main/tests/e2e/singlecard/test_prompt_embedding.py)
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- Correctness test example: [`tests/e2e/singlecard/test_aclgraph.py`](https://github.com/vllm-project/vllm-ascend/blob/main/tests/e2e/singlecard/test_aclgraph.py)
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- Reduced Layer model test example: [test_torchair_graph_mode.py - DeepSeek-V3-Pruning](https://github.com/vllm-project/vllm-ascend/blob/20767a043cccb3764214930d4695e53941de87ec/tests/e2e/multicard/test_torchair_graph_mode.py#L48)
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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:
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1. Fork the original model repo in modelscope. All the files in the repo except for weights are required.
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2. Set `num_hidden_layers` to the expected number of layers, e.g., `{"num_hidden_layers": 2,}`
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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:
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```python
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import torch
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from transformers import AutoTokenizer, AutoConfig
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from modeling_deepseek import DeepseekV3ForCausalLM
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from modelscope import snapshot_download
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MODEL_LOCAL_PATH = "~/.cache/modelscope/models/vllm-ascend/DeepSeek-V3-Pruning"
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DIST_DTYPE = torch.bfloat16
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DIST_MODEL_PATH = "./random_deepseek_v3_with_2_hidden_layer"
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config = AutoConfig.from_pretrained(MODEL_LOCAL_PATH, trust_remote_code=True)
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model = DeepseekV3ForCausalLM(config)
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model = model.to(DIST_DTYPE)
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model.save_pretrained(DIST_MODEL_PATH)
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```
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### Run doctest
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vllm-ascend provides a `vllm-ascend/tests/e2e/run_doctests.sh` command to run all doctests in the doc files.
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The doctest is a good way to make sure docs stay current and examples remain executable, which can be run locally as follows:
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```bash
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# Run doctest
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/vllm-workspace/vllm-ascend/tests/e2e/run_doctests.sh
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```
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This will reproduce the same environment as the CI. See [vllm_ascend_doctest.yaml](https://github.com/vllm-project/vllm-ascend/blob/main/.github/workflows/vllm_ascend_doctest.yaml).
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