Nvidia Megatron Core Onboard Gb200 1node Tests

Onboard GB200 1-Node GitHub MR Tests

Create 1-node (mr-github) variants of existing 2-node (mr-scoped) GB200 functional tests. Each GB200 node has 4 GPUs. A 2-node test uses 8 GPUs total; the 1-node variant uses 4.

Background

GB200 functional tests live in tests/test_utils/recipes/gb200/:

Model configs live at: tests/functional_tests/test_cases/{model}/{test_case}/model_config.yaml

1-node test cases use the _1node suffix: tests/functional_tests/test_cases/{model}/{test_case}_1node/model_config.yaml

Workflow

Step 1 — Find candidate tests

Scan the products: block in gpt.yaml and moe.yaml for entries with scope: [mr, ...] or scope: [mr-slim, ...]. These are the 2-node tests that need 1-node mr-github counterparts.

Ignore tests already covered in *-1node.yaml files, and ignore nightly, weekly, mr-broken scopes.

Step 2 — Read each model config

For each candidate, read its model_config.yaml and extract the key parallelism arguments:

--tensor-model-parallel-size   (TP)
--pipeline-model-parallel-size (PP)
--expert-model-parallel-size   (EP)
--expert-tensor-parallel-size  (ETP)
--context-parallel-size        (CP)
--global-batch-size
--micro-batch-size

Step 3 — Classify: trivial copy vs. needs adaptation

The world size formula is: world_size = TP × PP × DP where DP ≥ EP.

Going from 8 GPUs → 4 GPUs:

Do not change GBS — let gradient accumulation absorb the reduced DP.

Step 4 — Create _1node model config directories

# Trivial copy
mkdir -p tests/functional_tests/test_cases/{model}/{test_case}_1node
cp tests/functional_tests/test_cases/{model}/{test_case}/model_config.yaml \
   tests/functional_tests/test_cases/{model}/{test_case}_1node/model_config.yaml

# Then apply any parallelism changes (EP or PP) with Edit tool

Step 5 — Create or update recipe files

For GPT tests — create tests/test_utils/recipes/gb200/gpt-1node.yaml (if absent) by cloning gpt.yaml's spec block with nodes: 1. Use this template for the spec:

type: basic
format_version: 1
maintainers: [mcore]
loggers: [stdout]
spec:
  name: "{test_case}_{environment}_{platforms}"
  model: gpt          # or moe
  build: mcore-pyt-{environment}
  nodes: 1
  gpus: 4
  n_repeat: 5
  platforms: dgx_gb200
  script_setup: |    # copy verbatim from gpt.yaml / moe.yaml
    ...
  script: |-         # copy verbatim from gpt.yaml / moe.yaml
    ...

For MoE tests — append entries to the existing moe-1node.yaml.

Step 6 — Add products entries

Scope convention:

• 1–2 most representative tests per recipe: scope: [mr-github, mr-github-slim]
• All other tests: scope: [mr-github]

products:
  - test_case: [<test_case>_1node]
    products:
      - environment: [dev]
        scope: [mr-github, mr-github-slim]   # or [mr-github]
        platforms: [dgx_gb200]

Quick parallelism reference

Checklist

• Identified all mr-scoped tests in gpt.yaml and moe.yaml not yet in *-1node.yaml
• Read model config for each candidate
• Classified trivial vs. adaptation needed
• Created _1node/model_config.yaml for each test
• Applied EP or PP reductions where needed
• Created/updated recipe YAML with nodes: 1, gpus: 4
• Assigned mr-github scope (+ mr-github-slim for 1–2 representative tests per recipe)
• Verified no mr-github-slim overload (slim suite should stay small)