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

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.

• 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.
• srt/: Legacy CI setup, to be deprecated.

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.

# 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 python/sglang/jit_kernel/tests/test_add_constant.py

# Run a suite
python3 test/run_suite.py --hw cpu --suite stage-a-test-cpu
python3 test/run_suite.py --hw cuda --suite stage-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 stage-b-test-1-gpu-small \
    --auto-partition-id 0 --auto-partition-size 4

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, suite="stage-b-test-1-gpu-small")

Parameters: est_time (seconds), suite (target suite), nightly=True (nightly-only), disabled="reason" (temporarily disable).

Keep est_time and suite as literal values — run_suite.py collects them by AST parsing.

JIT kernel files live outside test/registered/ but still use registration:

• Correctness tests: python/sglang/jit_kernel/tests/test_*.py → stage-b-kernel-unit-1-gpu-large
• Benchmarks: python/sglang/jit_kernel/benchmark/bench_*.py → stage-b-kernel-benchmark-1-gpu-large

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

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

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.

• 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.

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