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

Multi Node Test

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

The following picture shows the basic deployment view of the multi-node CI mechanism, It shows how the github action interact with lws (a kind of kubernetes crd resource)

From the workflow perspective, we can see how the final test script is executed, The key point is that these two lws.yaml and run.sh, The former defines how our k8s cluster is pulled up, and the latter defines the entry script when the pod is started, Each node executes different logic according to the LWS_WORKER_INDEX environment variable, so that multiple nodes can form a distributed cluster to perform tasks.

How to contribute

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 organization is welcome, If you do not have permission to upload, please contact @Potabk

2. Add config yaml

   As the entrypoint script run.sh shows, A k8s pod startup means traversing all *.yaml files in the directory, reading and executing according to different configurations, so what we need to do is just add "yamls" like DeepSeek-V3.yaml.

   Suppose you have 2 nodes running a 1P1D setup (1 Prefillers + 1 Decoder):

   you may add a config file looks like:

   test_name: "test DeepSeek-V3 disaggregated_prefill"
   # the model being tested
   model: "vllm-ascend/DeepSeek-V3-W8A8"
   # how large the cluster is
   num_nodes: 2
   npu_per_node: 16
   # All env vars you need should add it here
   env_common:
   VLLM_USE_MODELSCOPE: true
   OMP_PROC_BIND: false
   OMP_NUM_THREADS: 100
   HCCL_BUFFSIZE: 1024
   SERVER_PORT: 8080
   disaggregated_prefill:
   enabled: true
   # node index(a list) which meet all the conditions:
   #  - prefiller
   #  - no headless(have api server)
   prefiller_host_index: [0]
   # node index(a list) which meet all the conditions:
   #  - decoder
   #  - no headless(have api server)
   decoder_host_index: [1]
   
   # Add each node's vllm serve cli command just like you run locally
   deployment:
   -
       server_cmd: >
           vllm serve ...
   -
       server_cmd: >
           vllm serve ...
   benchmarks:
   perf:
       # fill with performance test kwargs
   acc:
       # fill with accuracy test kwargs
   

3. Add the case to nightly workflow currently, the multi-node test workflow defined in the vllm_ascend_test_nightly_a2/a3.yaml

    multi-node-tests:
        needs: single-node-tests
        if: always() && (github.event_name == 'schedule' || github.event_name == 'workflow_dispatch')
        strategy:
        fail-fast: false
        max-parallel: 1
        matrix:
            test_config:
            - name: multi-node-deepseek-pd
                config_file_path: tests/e2e/nightly/multi_node/config/models/DeepSeek-V3.yaml
                size: 2
            - name: multi-node-qwen3-dp
                config_file_path: tests/e2e/nightly/multi_node/config/models/Qwen3-235B-A3B.yaml
                size: 2
            - name: multi-node-dpsk-4node-pd
                config_file_path: tests/e2e/nightly/multi_node/config/models/DeepSeek-R1-W8A8.yaml
                size: 4
        uses: ./.github/workflows/_e2e_nightly_multi_node.yaml
        with:
        soc_version: a3
        image: m.daocloud.io/quay.io/ascend/cann:8.3.rc2-a3-ubuntu22.04-py3.11
        replicas: 1
        size: ${{ matrix.test_config.size }}
        config_file_path: ${{ matrix.test_config.config_file_path }}
   

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.