mcore-testing

Testing Guide

Answer-First Testing Facts

For questions about disabling tests without deleting them:

• Functional recipe entries stay in YAML; disable by suffixing scope with -broken, for example scope: [mr-github] -> scope: [mr-github-broken].
• Unit-test skips use pytest markers instead: @pytest.mark.flaky_in_dev skips in the default dev environment, and @pytest.mark.flaky skips in LTS.
• Do not delete the test case or recipe entry when the goal is discoverability and easy re-enable.

Test Layout

tests/
├── unit_tests/          # pytest, 1 node × 8 GPUs, torch.distributed runner
├── functional_tests/    # end-to-end shell + training scripts
│   └── test_cases/
│       └── {model}/{test_case}/
│           ├── model_config.yaml          # training args
│           └── golden_values_{env}_{platform}.json
└── test_utils/
    ├── recipes/
    │   ├── h100/        # YAML recipes for H100 jobs
    │   └── gb200/       # YAML recipes for GB200 jobs
    └── python_scripts/  # helpers (recipe_parser, golden-value download, …)

How Tests Execute

The GitHub Actions runner invokes launch_nemo_run_workload.py, which uses nemo-run to launch a DockerExecutor container. The repo is bind-mounted at /opt/megatron-lm; training data is mounted at /mnt/artifacts.

Unit tests are dispatched through torch.distributed.run:

• Ranks 0 and 3 are tee-d to stdout; all other ranks write only to log files.
• Per-rank log files land at {assets_dir}/logs/1/ and are uploaded as a GitHub artifact after the run.

Functional tests are driven by tests/functional_tests/shell_test_utils/run_ci_test.sh. Only rank 0 runs the pytest validation step; training output from all ranks is uploaded as an artifact.

Flaky-failure auto-retry: launch_nemo_run_workload.py retries up to 3 times for known transient patterns (NCCL timeout, ECC error, segfault, HuggingFace connectivity, …) before declaring a genuine failure.

Recipe YAML Structure

Recipes live in tests/test_utils/recipes/ and are parsed by tests/test_utils/python_scripts/recipe_parser.py. Each file expands a cartesian products block into individual workload specs:

type: basic
format_version: 1
maintainers: [mcore]
loggers: [stdout]
spec:
  name: "{test_case}_{environment}_{platforms}"
  model: gpt              # maps to tests/functional_tests/test_cases/{model}/
  build: mcore-pyt-{environment}
  nodes: 1
  gpus: 8
  n_repeat: 5
  platforms: dgx_h100
  time_limit: 1800
  script_setup: |
    ...
  script: |-
    bash tests/functional_tests/shell_test_utils/run_ci_test.sh ...
products:
  - test_case: [my_test]
    products:
      - environment: [dev, lts]
        scope: [mr-github]
        platforms: [dgx_h100]

Key runtime placeholders: {assets_dir}, {artifacts_dir}, {test_case}, {environment}, {platforms}, {n_repeat}.

Disabling a Test Without Deleting It

To temporarily disable a test case in a recipe YAML, suffix its scope value with -broken — do not delete the entry:

# before (test runs in CI)
scope: [mr-github]

# after (test is skipped; entry preserved for easy re-enable)
scope: [mr-github-broken]

Running Unit Tests Locally

All unit tests initialize a torch.distributed group, so every invocation requires GPU access and must go through torch.distributed.run:

# Full suite
uv run python -m torch.distributed.run --nproc-per-node 8 -m pytest -q \
  tests/unit_tests

# Single file
uv run python -m torch.distributed.run --nproc-per-node 8 -m pytest -q \
  tests/unit_tests/models/test_gpt_model.py

# Single test
uv run python -m torch.distributed.run --nproc-per-node 8 -m pytest -q \
  tests/unit_tests/models/test_gpt_model.py::TestGPTModel::test_constructor

# Filter by name substring
uv run python -m torch.distributed.run --nproc-per-node 8 -m pytest -q \
  tests/unit_tests -k optimizer

Marker filters

# Exclude flaky tests during development
uv run python -m torch.distributed.run --nproc-per-node 8 -m pytest -q \
  tests/unit_tests -m "not flaky and not flaky_in_dev"

# Include experimental tests
uv run python -m torch.distributed.run --nproc-per-node 8 -m pytest -q \
  tests/unit_tests --experimental

CI parity

Use tests/unit_tests/run_ci_test.sh to reproduce a CI bucket failure exactly. For ad-hoc runs, prefer the direct torch.distributed.run invocations above.

Gotchas

• pyproject.toml sets addopts = --durations=15 -s -rA — stdout is not captured (-s), so ranks interleave during multi-rank runs. Override with --capture=fd when debugging a specific rank.
• tests/unit_tests/conftest.py looks for test data under /opt/data and attempts a download if missing. Supply it manually or skip data-dependent tests when running outside the canonical container.

Adding a Unit Test

1. Create tests/unit_tests/<category>/test_<name>.py.
2. Use fixtures from tests/unit_tests/conftest.py.
3. Apply markers as needed:
   • @pytest.mark.internal — skipped on legacy tag
   • @pytest.mark.flaky_in_dev — skipped in dev environment (CI default; use this to disable a flaky test without blocking the standard pipeline)
   • @pytest.mark.flaky — skipped in lts environment
   • @pytest.mark.experimental — latest tag only
4. Verify locally (see Running Unit Tests Locally above).
5. If the test needs a dedicated CI bucket, add an entry to tests/test_utils/recipes/h100/unit-tests.yaml.
6. If the change adds or modifies a GPU kernel (Triton, jit_fuser / torch.compile, CUDA extension, TE or external-library dispatch, or a scatter/index accumulation), add or update its bit-exact replay test under tests/unit_tests/determinism/kernels/ and register it in tests/unit_tests/determinism/kernels/manifest.py. The linting CI job (tools/check_kernel_determinism_coverage.py) fails kernel PRs without this; the determinism-exempt label overrides it for non-numeric edits. See docs/developer/determinism/testing.md.

Adding a Functional / Integration Test

1. Create tests/functional_tests/test_cases/<model>/<test_name>/.

2. Write model_config.yaml with MODEL_ARGS, ENV_VARS, and TEST_TYPE.

3. Add a YAML recipe under tests/test_utils/recipes/h100/ (and gb200/ if needed). Required fields: scope, environment, platform, n_repeat, time_limit.

4. Push the PR, add the label "Run functional tests" to trigger a full run.

5. After a successful run, download golden values:

   python tests/test_utils/python_scripts/download_golden_values.py \
     --source github --pipeline-id <run-id>
   

6. Commit the downloaded golden values.

Golden values keep the full float32 precision of the TensorBoard scalars, record it as "value_precision": "full", and deterministic test cases are compared bit-exactly against them. Never round or hand-edit them: tools/check_golden_values.py (run by the linting CI job on changed golden files) rejects golden files of deterministically compared cases whose metrics lack the full marker. Legacy files (no marker) are still compared at five decimals until they are regenerated from a CI run.

Golden comparisons report absolute and relative errors; see diagnostics.

Common Pitfalls