Writing SGLang CI / UT Tests

Apply kernel-organization for the public operator namespace, logical grouping, lazy registry metadata, and test placement. The implementation tutorial below does not replace that API contract.

This skill covers how to write and register tests. For CI pipeline internals (stage ordering, fail-fast, gating, partitioning, debugging CI failures), see the CI workflow guide. Whether a case is worth adding at all is decided by unit-test-admission — read it before writing the case, not after.

Core Rules

1. Always use CustomTestCase — never raw unittest.TestCase. It ensures tearDownClass runs even when setUpClass fails, preventing resource leaks in CI.
2. tearDownClass must shut the server down gracefully — call terminate_and_kill_process_tree(cls.process), never a bare kill_process_tree. SIGKILL alone skips the server's userspace cleanup and leaves its GPU memory charged to the dead process; the next class then OOMs while loading weights. Keep it defensive too: hasattr/null checks before accessing resources (e.g. cls.process) that setUpClass may not have finished allocating.
3. Place non-kernel tests in test/registered/<kind>/<subsystem>/ — <kind> is unit, e2e, accuracy, perf, or stress; kernel tests use test/registered/kernels/{ops,benchmark}/<group>/; hardware belongs in registrations, not directory names
4. Reuse server fixtures — inherit from DefaultServerBase or write setUpClass/tearDownClass with popen_launch_server
5. Mock boundaries, not SGLang behavior — mock slow or external dependencies only when the assertion still checks an observable result, state transition, or error. A test whose evidence is only assert_called* mirrors its mock and is not admissible. Launch a real server only when inference results or lifecycle behavior are the contract under test.

Existing files are not the reference. About 290 test files still call the bare kill_process_tree(cls.process.pid), against 43 on the current helper. They predate rule 2 and are being migrated, so grepping the repo for a teardown pattern finds the wrong one roughly seven times out of eight. The same goes for register_cuda_ci(suite=...): four files still pass it, all of them under test/registered/stress/.

# Bad:  kill_process_tree(cls.process.pid)           # SIGKILL only; GPU memory lingers
# Good: terminate_and_kill_process_tree(cls.process)

# Bad:  register_cuda_ci(est_time=80, suite="base-b-test-1-gpu-small")
# Good: register_cuda_ci(est_time=80, stage="base-b", runner_config="1-gpu-small")

JIT kernel notes:

• If the task is adding or updating code under python/sglang/kernels/jit/, prefer the add-jit-kernel skill first.
• JIT kernel correctness tests use test/registered/kernels/ops/<group>/test_*.py.
• JIT kernel benchmarks use test/registered/kernels/benchmark/<group>/bench_*.py.
• Those files are executed by test/run_suite.py through dedicated kernel suites (base-b-kernel-*); a register_*_ci(...) call placed under python/sglang/ is rejected by the check-no-registered-tests-in-package pre-commit hook.

---

Model & Backend Selection

Scenario	Model	CI Registration	Suite
Unit tests (no server / engine launch)	None	register_cpu_ci	base-a-test-cpu
Common / backend-independent (middleware, abort, routing, config, arg parsing)	DEFAULT_SMALL_MODEL_NAME_FOR_TEST (1B)	register_cuda_ci only	base-b-test-1-gpu-small
Model-agnostic functionality (sampling, session, OpenAI API features)	DEFAULT_SMALL_MODEL_NAME_FOR_TEST (1B)	register_cuda_ci (+ AMD if relevant)	base-b-test-1-gpu-small
General performance (single node, no spec/DP/parallelism)	DEFAULT_MODEL_NAME_FOR_TEST (8B)	register_cuda_ci	base-b-test-1-gpu-large
Bigger features (spec, DP, TP, disaggregation)	Case by case	Case by case	See Choosing a Suite below

Key principle for E2E tests: Do NOT add register_amd_ci unless the test specifically exercises AMD/ROCm code paths. Common E2E tests just need any GPU to run — duplicating across backends wastes CI time with no extra coverage.

All model constants

Defined in python/sglang/test/test_utils.py:

Constant	Model	When to use
DEFAULT_SMALL_MODEL_NAME_FOR_TEST	Llama-3.2-1B-Instruct	Common features, model-agnostic tests
DEFAULT_SMALL_MODEL_NAME_FOR_TEST_BASE	Llama-3.2-1B	Base (non-instruct) model tests
DEFAULT_MODEL_NAME_FOR_TEST	Llama-3.1-8B-Instruct	General performance (single node)
DEFAULT_MOE_MODEL_NAME_FOR_TEST	Mixtral-8x7B-Instruct	MoE-specific tests
DEFAULT_SMALL_EMBEDDING_MODEL_NAME_FOR_TEST	—	Embedding tests
DEFAULT_SMALL_VLM_MODEL_NAME_FOR_TEST	—	Vision-language tests

Naming Conventions

A per-commit suite name is generated from registration metadata as {stage}-test-{runner_config} — you don't hand-write it:

• stage — the CI stage (e.g. base-b, base-b-kernel-unit, base-c).
• runner_config — a runner-pool key from scripts/ci/runner_configs.yml, which maps it to the physical runner label (so 1-gpu-large runs on 1-gpu-h100). AMD/NPU use their own keys (e.g. amd).
• Suite — register_cuda_ci(stage="base-b", runner_config="1-gpu-small") → base-b-test-1-gpu-small, the name you pass to run_suite.py --suite. The -test- is just the connector; never put it in register_*_ci.

CUDA nightly uses the same shape with stage="nightly" (e.g. stage="nightly", runner_config="1-gpu-large" → nightly-test-1-gpu-large) and no nightly=True — the stage name carries the cadence, and setting the flag makes the test silently never run. Legacy single-string suite= is left only for stress and some AMD/CPU/NPU pools.

All CI Suites

Do not work from a list copied into this file; it goes stale silently. Read the current one:

grep -n "_SUITES = {" test/run_suite.py   # PER_COMMIT_SUITES, NIGHTLY_SUITES, OTHER_SUITES
cat scripts/ci/runner_configs.yml         # runner_config -> physical runner label

scripts/ci/runner_configs.yml calls itself the single source of truth for the runner_config field, and run_suite.py is what actually dispatches, so those two files settle any disagreement with prose anywhere else.

Nightly suites live in NIGHTLY_SUITES and run via nightly-test-nvidia.yml, nightly-test-amd.yml, and nightly-test-npu.yml, not pr-test.yml. CUDA nightly is named nightly-test-{runner_config} — one suite per machine type, holding everything that runs nightly on it, with auto_partition splitting the work. There is no per-purpose split; kernel, eval, perf, and precision all share their machine's suite.

Note: Multimodal diffusion uses python/sglang/multimodal_gen/test/run_suite.py, not test/run_suite.py.

Choosing a Suite

Use the lightest suite that meets your test's needs:

• No GPU required → base-a-test-cpu
• Most small GPU tests → base-b-test-1-gpu-small (default choice)
• Need H100 memory or Hopper features → base-b-test-1-gpu-large
• JIT kernel correctness → base-b-kernel-unit-test-1-gpu-large
• JIT kernel correctness for B200 / SM100 paths → base-b-kernel-unit-test-4-gpu-b200
• JIT kernel benchmarks → base-b-kernel-benchmark-test-1-gpu-large
• Multi-GPU → only when the test actually needs multiple GPUs

---

Test File Templates

Unit Tests (no server / engine launch)

See test/registered/unit/README.md for quick-start and rules. Unit tests live in test/registered/unit/, mirroring python/sglang/srt/:

"""Unit tests for srt/<module>"""

import unittest
from unittest.mock import MagicMock, patch

from sglang.srt.<module> import TargetClass
from sglang.test.ci.ci_register import register_cpu_ci
from sglang.test.test_utils import CustomTestCase

register_cpu_ci(est_time=5, suite="base-a-test-cpu")
# Unit tests are CPU-only. GPU operator tests belong under `kernel/`.

class TestTargetClass(CustomTestCase):
    def test_basic_behavior(self):
        obj = TargetClass(...)
        self.assertEqual(obj.method(), expected)

    @patch("sglang.srt.<module>.some_dependency")
    def test_with_mock(self, mock_dep):
        mock_dep.return_value = MagicMock()
        # test logic with dependency mocked
        ...


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

Use unittest.mock.patch / MagicMock only at dependency boundaries. Assert the resulting value, state, protocol output, or error—not merely that the mock was called. If the module transitively imports GPU-only packages (e.g. sgl_kernel), they can be stubbed so the test runs on CPU CI. Do not modify sys.modules at module level—use patch.dict (as a class decorator or with start/stop) to ensure cleanup and avoid cross-test pollution. See test/registered/unit/README.md for details and examples.

Quality bar — test real logic (validation boundaries, state transitions, error paths, branching, etc.). Skip tests that just verify Python itself works (e.g., "does calling an abstract method raise NotImplementedError?", "does a dataclass store the field I assigned?"). Consolidate repetitive patterns into parameterized tests. No production code changes in test PRs.

E2E test (small model, server needed)

import unittest

import requests

from sglang.test.ci.ci_register import register_cuda_ci
from sglang.test.test_utils import (
    DEFAULT_SMALL_MODEL_NAME_FOR_TEST,
    DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
    DEFAULT_URL_FOR_TEST,
    CustomTestCase,
    popen_launch_server,
    terminate_and_kill_process_tree,
)

register_cuda_ci(est_time=60, stage="base-b", runner_config="1-gpu-small")


class TestMyFeature(CustomTestCase):
    @classmethod
    def setUpClass(cls):
        cls.model = DEFAULT_SMALL_MODEL_NAME_FOR_TEST
        cls.base_url = DEFAULT_URL_FOR_TEST
        cls.process = popen_launch_server(
            cls.model,
            cls.base_url,
            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
            other_args=["--arg1", "value1"],  # feature-specific args
        )

    @classmethod
    def tearDownClass(cls):
        if hasattr(cls, "process") and cls.process:
            terminate_and_kill_process_tree(cls.process)

    def test_basic_functionality(self):
        response = requests.post(
            self.base_url + "/generate",
            json={"text": "Hello", "sampling_params": {"max_new_tokens": 32}},
        )
        self.assertEqual(response.status_code, 200)


if __name__ == "__main__":
    unittest.main(verbosity=3)

Copy the tearDownClass above verbatim. Most existing E2E files still show the bare kill_process_tree; that form is being migrated out and must not be reproduced.

E2E test (8B model, server needed, performance)

import time
import unittest

import requests

from sglang.test.ci.ci_register import register_cuda_ci
from sglang.test.test_utils import (
    DEFAULT_MODEL_NAME_FOR_TEST,
    DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
    DEFAULT_URL_FOR_TEST,
    CustomTestCase,
    popen_launch_server,
    terminate_and_kill_process_tree,
)

register_cuda_ci(est_time=300, stage="base-b", runner_config="1-gpu-large")


class TestMyFeaturePerf(CustomTestCase):
    @classmethod
    def setUpClass(cls):
        cls.model = DEFAULT_MODEL_NAME_FOR_TEST
        cls.base_url = DEFAULT_URL_FOR_TEST
        cls.process = popen_launch_server(
            cls.model,
            cls.base_url,
            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
        )

    @classmethod
    def tearDownClass(cls):
        if hasattr(cls, "process") and cls.process:
            terminate_and_kill_process_tree(cls.process)

    def test_latency(self):
        start = time.perf_counter()
        response = requests.post(
            self.base_url + "/generate",
            json={"text": "Hello", "sampling_params": {"max_new_tokens": 128}},
        )
        elapsed = time.perf_counter() - start
        self.assertEqual(response.status_code, 200)
        self.assertLess(elapsed, 5.0, "Latency exceeded threshold")


if __name__ == "__main__":
    unittest.main(verbosity=3)

---

Server Fixture Reuse

For tests that only need a standard server, inherit from DefaultServerBase and override class attributes:

from sglang.test.server_fixtures.default_fixture import DefaultServerBase

class TestMyFeature(DefaultServerBase):
    model = DEFAULT_SMALL_MODEL_NAME_FOR_TEST
    other_args = ["--enable-my-feature"]

    def test_something(self):
        ...

Available fixtures in python/sglang/test/server_fixtures/:

Fixture	Use case
DefaultServerBase	Standard single-server tests
EagleServerBase	EAGLE speculative decoding
PDDisaggregationServerBase	Disaggregated prefill/decode
MMMUServerBase	Multimodal VLM tests

---

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_amd_ci,
    register_cpu_ci,
    register_npu_ci,
)

# Per-commit test (small 1-gpu, runs on 5090)
register_cuda_ci(est_time=80, stage="base-b", runner_config="1-gpu-small")

# Per-commit test (large 1-gpu, runs on H100)
register_cuda_ci(est_time=120, stage="base-b", runner_config="1-gpu-large")

# Nightly-only test (same shape as per-commit, stage is just "nightly")
register_cuda_ci(est_time=200, stage="nightly", runner_config="1-gpu-large")

# Multi-backend test (only when testing backend-specific code paths)
register_cuda_ci(est_time=80, stage="base-a", runner_config="1-gpu-small")
register_amd_ci(est_time=120, suite="stage-a-test-1-gpu-small-amd")
register_npu_ci(est_time=400, suite="nightly-8-npu-a3", nightly=True)

# Temporarily disabled test
register_cuda_ci(
    est_time=80, stage="base-b", runner_config="1-gpu-small", disabled="flaky - see #12345"
)

Parameters:

• est_time: estimated runtime in seconds (used for CI partitioning)
• stage + runner_config: the canonical pair for CUDA; the suite name is generated from them (see Naming Conventions)
• suite: legacy single-string form. Only stress and some AMD/CPU/NPU pools still take it; register_cpu_ci(suite="base-a-test-cpu") is correct and is not being migrated
• nightly=True: legacy cadence flag, for non-CUDA nightly suites only. CUDA nightly uses stage="nightly" and must leave this unset
• disabled="reason": temporarily disable with explanation

Key principle: Only add register_amd_ci / register_npu_ci when the test exercises backend-specific code paths. Common E2E tests just need register_cuda_ci — duplicating across backends wastes CI time.

JIT Kernel Registration

run_suite.py discovers every test/registered/**/*.py, JIT kernel files included. They are ordinary registered tests; only their stage differs:

from sglang.test.ci.ci_register import register_cuda_ci

# Correctness tests in test/registered/kernels/ops/<group>/
register_cuda_ci(est_time=30, stage="base-b-kernel-unit", runner_config="1-gpu-large")
register_cuda_ci(est_time=30, stage="base-b-kernel-unit", runner_config="4-gpu-b200")
register_cuda_ci(est_time=120, stage="base-b-kernel-unit", runner_config="8-gpu-h200")

# Benchmarks in test/registered/kernels/benchmark/<group>/
register_cuda_ci(est_time=6, stage="base-b-kernel-benchmark", runner_config="1-gpu-large")

# Optional nightly registration — same form, stage is just "nightly"
register_cuda_ci(est_time=120, stage="nightly", runner_config="1-gpu-large")
register_cuda_ci(est_time=120, stage="nightly", runner_config="8-gpu-h200")

Every call generates a suite named {stage}-test-{runner_config}, e.g. base-b-kernel-unit-test-1-gpu-large and nightly-test-1-gpu-large. Keep est_time, stage, runner_config, and suite as literal values — run_suite.py collects them by AST parsing.

---

Test Placement

test/
├── registered/          # CI tests (auto-discovered by run_suite.py)
│   ├── unit/<subsystem>/      # CPU-only; no server or model weights
│   ├── kernel/<group>/        # accelerator operator correctness/benchmarks
│   ├── e2e/<subsystem>/       # engine/server integration
│   ├── accuracy/<family>/     # scheduled eval floors
│   ├── perf/<family>/         # scheduled latency/throughput contracts
│   └── stress/<subsystem>/    # stress/weekly coverage
├── manual/              # Non-CI: debugging, one-off, manual verification
└── run_suite.py         # CI runner (globs test/registered/**/*.py; nothing outside it)

python/sglang/kernels/jit/   # implementation + test-only helpers, never registered tests

A register_*_ci(...) under python/sglang/ is rejected by the check-no-registered-tests-in-package pre-commit hook.

Decision rule (see also test/registered/README.md):

• CPU component logic, no server → registered/unit/<subsystem>/
• JIT kernel correctness → registered/kernels/ops/<group>/
• JIT kernel benchmarks → registered/kernels/benchmark/<group>/
• Other accelerator operator correctness → registered/kernels/ops/<group>/
• Server needed → registered/e2e/<subsystem>/
• Eval floor / performance contract → registered/{accuracy,perf}/<family>/
• Local debugging → manual/

---

Eval Accuracy Mixins

Design philosophy: Most test files don't care about eval logic — they only need a "does this feature break model output quality?" sanity check. The mixin pattern separates what to test (threshold) from how to test (the eval call, assertions, CI summary). Test classes declare thresholds as class attributes; the mixin provides the test_* method. Override when you need extra assertions (e.g. EAGLE accept length).

Available mixins in python/sglang/test/kits/eval_accuracy_kit.py: MMLUMixin, HumanEvalMixin, MGSMEnMixin, GSM8KMixin. Can be combined freely. Read the source for attrs and defaults. MMLUMixin already scores through sgl-eval; GSM8KMixin still defaults to the deprecated run_eval backend for existing tests, so new tests set gsm8k_backend = "sgl_eval". The exception is a base or completion-only model: sgl-eval only sends chat requests, so those stay on the 5-shot completion backend.

class TestMyFeature(CustomTestCase, MMLUMixin):
    mmlu_score_threshold = 0.65
    mmlu_num_examples = 64
    mmlu_num_threads = 32
    # test_mmlu is inherited — no code needed

---

Key Utilities

from sglang.test.test_utils import (
    CustomTestCase,              # base class with retry logic
    popen_launch_server,         # launch server subprocess
    terminate_and_kill_process_tree,    # SIGTERM, then SIGKILL, then wait for
                                        # the GPU memory to come back
    DEFAULT_URL_FOR_TEST,        # auto-configured base URL
    DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,  # 600s default
    run_bench_serving,           # benchmark helper (launch + bench)
)

---

Checklist

Before submitting a test:

• Inherits from CustomTestCase (not unittest.TestCase)
• Has register_*_ci(...) call at module level
• Placed in test/registered/<kind>/<subsystem>/
• JIT kernel work: correctness tests live in test/registered/kernels/ops/<group>/, benchmarks live in test/registered/kernels/benchmark/<group>/, and only test-only helpers stay under python/sglang/kernels/jit/
• Backend-independent tests: register_cuda_ci only + smallest model
• Logic that doesn't need a server / engine launch → unit test in registered/unit/ (see Unit Tests section)
• tearDownClass is defensive — uses hasattr/null checks before accessing resources that may not have been allocated
• Every case answers "what future diff turns this red?" — see unit-test-admission
• est_time is reasonable (measure locally)

Run these against the new file and paste the output rather than self-attesting:

f=<your new test file>
grep -n "kill_process_tree" $f          # every hit must be terminate_and_kill_process_tree
grep -n "register_.*_ci(" $f            # CUDA: stage= + runner_config=, never suite=
grep -n "CustomTestCase\|unittest.main" $f   # both must appear