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[Doc] fix the nit in docs (#6826)

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Refresh the doc, fix the nit in the docs

- vLLM version: v0.15.0
- vLLM main:
83b47f67b1

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
This commit is contained in:
wangxiyuan
2026-02-27 11:50:27 +08:00
committed by GitHub
parent 981d803cb7
commit a95c0b8b82
30 changed files with 145 additions and 118 deletions
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2
docs/source/developer_guide/contribution/index.md
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@@ -103,7 +103,7 @@ If the PR spans more than one category, please include all relevant prefixes.
## Others
You may find more information about contributing to vLLM Ascend backend plugin on [<u>docs.vllm.ai</u>](https://docs.vllm.ai/en/latest/contributing/overview.html).
If you find any problem when contributing, you can feel free to submit a PR to improve the doc to help other developers.
If you encounter any problems while contributing, feel free to submit a PR to improve the documentation to help other developers.
:::{toctree}
:caption: Index

11
docs/source/developer_guide/contribution/multi_node_test.md
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@@ -2,9 +2,9 @@
Multi-Node CI is designed to test distributed scenarios of very large models, eg: disaggregated_prefill multi DP across multi nodes and so on.
## How is works
## How it works
The following picture shows the basic deployment view of the multi-node CI mechanism, It shows how the github action interact with [lws](https://lws.sigs.k8s.io/docs/overview/) (a kind of kubernetes crd resource)
The following picture shows the basic deployment view of the multi-node CI mechanism. It shows how the GitHub action interacts with [lws](https://lws.sigs.k8s.io/docs/overview/) (a kind of kubernetes crd resource).
![alt text](../../assets/deployment.png)
@@ -16,7 +16,7 @@ From the workflow perspective, we can see how the final test script is executed,
1. Upload custom weights
If you need customized weights, for example, you quantized a w8a8 weight for DeepSeek-V3 and you want your weight to run on CI, Uploading weights to ModelScope's [vllm-ascend](https://www.modelscope.cn/organization/vllm-ascend) organization is welcome, If you do not have permission to upload, please contact @Potabk
If you need customized weights, for example, you quantized a w8a8 weight for DeepSeek-V3 and you want your weight to run on CI, uploading weights to ModelScope's [vllm-ascend](https://www.modelscope.cn/organization/vllm-ascend) organization is welcome. If you do not have permission to upload, please contact @Potabk
2. Add config yaml
@@ -71,7 +71,8 @@ From the workflow perspective, we can see how the final test script is executed,
```
3. Add the case to nightly workflow
currently, the multi-node test workflow defined in the [nightly_test_a3.yaml](https://github.com/vllm-project/vllm-ascend/blob/main/.github/workflows/nightly_test_a3.yaml)
Currently, the multi-node test workflow is defined in the [nightly_test_a3.yaml](https://github.com/vllm-project/vllm-ascend/blob/main/.github/workflows/nightly_test_a3.yaml)
```yaml
multi-node-tests:
@@ -106,7 +107,7 @@ currently, the multi-node test workflow defined in the [nightly_test_a3.yaml](ht
KUBECONFIG_B64: ${{ secrets.KUBECONFIG_B64 }}
```
The matrix above defines all the parameters required to add a multi-machine use case, The parameters worth paying attention to (I mean if you are adding a new use case) are size and the path to the yaml configuration file. The former defines the number of nodes required for your use case, and the latter defines the path to the configuration file you have completed in step 2.
The matrix above defines all the parameters required to add a multi-machine use case. The parameters worth noting (if you are adding a new use case) are `size` and the path to the yaml configuration file. The former defines the number of nodes required for your use case, and the latter defines the path to the configuration file you have completed in step 2.
## Run Multi-Node tests locally

14
docs/source/developer_guide/contribution/testing.md
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@@ -2,9 +2,9 @@
This document explains how to write E2E tests and unit tests to verify the implementation of your feature.
## Setup a test environment
## Set up a test environment
The fastest way to setup a test environment is to use the main branch's container image:
The fastest way to set up a test environment is to use the main branch's container image:
:::::{tab-set}
:sync-group: e2e
@@ -178,7 +178,7 @@ TORCH_DEVICE_BACKEND_AUTOLOAD=0 pytest -sv tests/ut
```bash
cd /vllm-workspace/vllm-ascend/
# Run all single card the tests
# Run all single-card tests
pytest -sv tests/ut
# Run single test
@@ -192,7 +192,7 @@ pytest -sv tests/ut/test_ascend_config.py
```bash
cd /vllm-workspace/vllm-ascend/
# Run all single card the tests
# Run all multi-card tests
pytest -sv tests/ut
# Run single test
@@ -223,7 +223,7 @@ You can't run the E2E test on CPUs.
```bash
cd /vllm-workspace/vllm-ascend/
# Run all single card the tests
# Run all single-card tests
VLLM_USE_MODELSCOPE=true pytest -sv tests/e2e/singlecard/
# Run a certain test script
@@ -240,7 +240,7 @@ VLLM_USE_MODELSCOPE=true pytest -sv tests/e2e/singlecard/test_offline_inference.
```bash
cd /vllm-workspace/vllm-ascend/
# Run all the single card tests
# Run all multi-card tests
VLLM_USE_MODELSCOPE=true pytest -sv tests/e2e/singlecard/
# Run a certain test script
@@ -256,7 +256,7 @@ VLLM_USE_MODELSCOPE=true pytest -sv tests/e2e/singlecard/test_aclgraph_accuracy.
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).
Run nightly multi-node test cases locally refer to section of `Running Locally` of [Multi Node Test](./multi_node_test.md).
For running nightly multi-node test cases locally, refer to the `Running Locally` section in [Multi Node Test](./multi_node_test.md).
#### E2E test example