sgl-project--sglang/test

Test and Continuous Integration (CI) System in SGLang

This page covers principles and essentials: folder layout, how to run tests, registration, and suite selection. For complete references, see the skill guides:

• Writing tests — templates, fixtures, model selection, complete suite tables, checklist: .claude/skills/write-sglang-test/SKILL.md
• CI pipeline internals — stage flow diagrams, fast-fail layers, gating, partitioning, execution modes, debugging failures: .claude/skills/ci-workflow-guide/SKILL.md

CI Pipeline Overview

The CI pipeline runs in three sequential stages: A (pre-flight, ~3 min) → B (basic, ~30 min) → C (advanced, ~30 min). Kernel and multimodal-gen tests run in parallel with stage B. For details on stage gating, fast-fail mechanisms, execution modes (PR vs scheduled vs /rerun-stage), and debugging CI failures, see the CI workflow guide.

Folder Organization

• registered/: CI test files, auto-discovered by run_suite.py. Most tests live here. JIT kernel tests are an exception (see below).
• manual/: Non-CI tests for local debugging or special setups.
• run_suite.py: CI runner — scans registered/ and JIT kernel directories.

The system supports both unittest and pytest. The launcher runs python filename.py -f with failfast enabled by default.

Make sure your file ends with exactly one of:

# for unittest
if __name__ == "__main__":
    unittest.main()

# for pytest
if __name__ == "__main__":
    import sys
    sys.exit(pytest.main([__file__]))

Do not add custom argparse or modify sys.argv before these calls — the CI runner appends -f for failfast.

Run Tests Locally

# Single file
python3 test/registered/core/test_srt_endpoint.py

# Single test method
python3 test/registered/core/test_srt_endpoint.py TestSRTEndpoint.test_simple_decode

# Single JIT kernel test
python3 test/registered/jit/test_add_constant.py

# Run a suite
python3 test/run_suite.py --hw cpu --suite base-a-test-cpu
python3 test/run_suite.py --hw cuda --suite base-a-test-1-gpu-small

# Nightly tests
python3 test/run_suite.py --hw cuda --suite nightly-1-gpu --nightly

# With auto-partitioning (for parallel CI jobs)
python3 test/run_suite.py --hw cuda --suite base-b-test-1-gpu-small \
    --auto-partition-id 0 --auto-partition-size 4

CI Registration

Every CI-discovered test file must call a registration function at module level:

from sglang.test.ci.ci_register import register_cuda_ci

register_cuda_ci(est_time=80, stage="base-b", runner_config="1-gpu-small")

Parameters: est_time (seconds), stage + runner_config (target stage and runner pool from scripts/ci/runner_configs.yml), nightly=True (nightly-only), disabled="reason" (temporarily disable).

Keep est_time, stage, runner_config as literal values — run_suite.py collects them by AST parsing.

JIT kernel correctness tests and benchmarks live under test/registered/jit/, same as other registered tests (their helpers stay alongside the kernel source under python/sglang/jit_kernel/ and are imported by absolute path):

• Correctness tests: test/registered/jit/test_*.py → base-b-kernel-unit-test-1-gpu-large
• Benchmarks: test/registered/jit/benchmark/bench_*.py → base-b-kernel-benchmark-test-1-gpu-large

Choosing a Suite

Use the lightest suite that meets your test's needs. Full suite tables are in the write-sglang-test skill.

Need	Suite
No GPU required	base-a-test-cpu
Small GPU (fits 5090, 32GB)	base-b-test-1-gpu-small (most tests go here)
Large GPU memory or Hopper features	base-b-test-1-gpu-large
JIT kernel correctness	base-b-kernel-unit-test-1-gpu-large
JIT kernel benchmarks	base-b-kernel-benchmark-test-1-gpu-large
Multi-GPU (2/4/8)	base-b-test-2-gpu-large, base-c-test-*
Long-running or experimental	nightly-* suites

Steps for Adding a Test

See the write-sglang-test skill for templates, fixtures, model selection, and a complete checklist.

Multi-Hardware Backends

This README mostly describes the NVIDIA GPU CI pipeline. Other hardware backends (AMD, NPU) follow the same practices and use the multi-backend registry system. A scheduled job summarizes test coverage across all backends; here is an example run.

Tips

• Learn from existing examples in test/registered.
• Reuse servers — launching is expensive. Share one server across many test methods via setUpClass.
• Use as few GPUs as possible. Prefer 1-GPU runners.
• Each test file should take < 500 seconds; split if longer.
• Each GitHub Actions job should take < 30 minutes; split if longer.
• If tests are too slow for per-commit, consider nightly suites.

Other Notes

Adding New Models to Nightly CI

• Text models: Extend the global model list variables in test_utils.py.
• VLMs: Extend the MODEL_THRESHOLDS dictionary in test/registered/eval/test_vlms_mmmu_eval.py.