Write Sglang Test

JIT kernel exception:

• If the task is adding or updating code under python/sglang/jit_kernel/, prefer the add-jit-kernel skill first.
• JIT kernel correctness tests use python/sglang/jit_kernel/tests/**/test_*.py.
• JIT kernel benchmarks use python/sglang/jit_kernel/benchmark/**/bench_*.py.
• Those files are still executed by test/run_suite.py, but through dedicated kernel suites rather than test/registered/.

Model & Backend Selection

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:

Naming Conventions

• Suite: stage-{a,b,c}-test-{gpu_count}-gpu-{hardware} (e.g., stage-b-test-1-gpu-small)
• CI runner: {gpu_count}-gpu-{hardware} (e.g., 1-gpu-5090, 4-gpu-h100, 8-gpu-h200)

All CI Suites

Per-commit (CUDA)

Per-commit (AMD)

Per-commit (Ascend NPU)

Nightly

Nightly suites are listed in NIGHTLY_SUITES in test/run_suite.py. They run via nightly-test-nvidia.yml, nightly-test-amd.yml amd nightly-test-npu.yml, not pr-test.yml. Examples:

• nightly-1-gpu (CUDA)
• nightly-kernel-1-gpu (CUDA, JIT kernel full grids)
• nightly-kernel-8-gpu-h200 (CUDA, multi-GPU JIT kernel nightly)
• nightly-8-gpu-h200 (CUDA)
• nightly-eval-vlm-2-gpu (CUDA)
• nightly-amd (AMD)
• nightly-amd-8-gpu-mi35x (AMD)
• nightly-1-npu-a3 (NPU)
• nightly-2-npu-a3 (NPU)
• nightly-4-npu-a3 (NPU)
• nightly-8-npu-a3 (NPU)
• nightly-16-npu-a3 (NPU)

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 → stage-a-test-cpu
• Most small GPU tests → stage-b-test-1-gpu-small (default choice)
• Need H100 memory or Hopper features → stage-b-test-1-gpu-large
• JIT kernel correctness → stage-b-kernel-unit-1-gpu-large
• JIT kernel benchmarks → stage-b-kernel-benchmark-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="stage-a-test-cpu")
# Prefer CPU. Only use register_cuda_ci when the test truly needs a GPU.

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 to mock dependencies and isolate the logic under test. 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.srt.utils import kill_process_tree
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,
)

register_cuda_ci(est_time=60, suite="stage-b-test-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:
            kill_process_tree(cls.process.pid)

    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)

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

import time
import unittest

import requests

from sglang.srt.utils import kill_process_tree
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,
)

register_cuda_ci(est_time=300, suite="stage-b-test-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:
            kill_process_tree(cls.process.pid)

    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/:

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

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

# Nightly-only test
register_cuda_ci(est_time=200, suite="nightly-1-gpu", nightly=True)

# Multi-backend test (only when testing backend-specific code paths)
register_cuda_ci(est_time=80, suite="stage-a-test-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, suite="stage-b-test-1-gpu-small", disabled="flaky - see #12345")

Parameters:

• est_time: estimated runtime in seconds (used for CI partitioning)
• suite: which CI suite to run in (see suite tables above)
• nightly=True: for nightly-only tests (default False = per-commit)
• 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

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

from sglang.test.ci.ci_register import register_cuda_ci

# Correctness tests in python/sglang/jit_kernel/tests/
register_cuda_ci(est_time=30, suite="stage-b-kernel-unit-1-gpu-large")
register_cuda_ci(est_time=120, suite="stage-b-kernel-unit-8-gpu-h200")

# Benchmarks in python/sglang/jit_kernel/benchmark/
register_cuda_ci(est_time=6, suite="stage-b-kernel-benchmark-1-gpu-large")

# Optional nightly registration
register_cuda_ci(est_time=120, suite="nightly-kernel-1-gpu", nightly=True)
register_cuda_ci(est_time=120, suite="nightly-kernel-8-gpu-h200", nightly=True)

Keep est_time 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/            # No server / engine launch (see test/registered/unit/README.md)
│   ├── kernels/         # CUDA kernel correctness (no server, GPU required)
│   ├── sampling/        # test_penalty.py, test_sampling_params.py ...
│   ├── sessions/        # test_session_control.py ...
│   ├── openai_server/   # basic/, features/, validation/ ...
│   ├── spec/            # eagle/, utils/ ...
│   ├── models/          # model-specific accuracy tests
│   ├── perf/            # performance benchmarks
│   └── <category>/      # create new category if needed
├── manual/              # Non-CI: debugging, one-off, manual verification
└── run_suite.py         # CI runner (scans registered/ plus jit_kernel test/benchmark files)

python/sglang/jit_kernel/
├── tests/               # JIT kernel correctness tests (CI-discovered by test/run_suite.py)
└── benchmark/           # JIT kernel benchmarks (CI-discovered by test/run_suite.py)

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

• Component logic, no server → registered/unit/
• JIT kernel correctness / benchmarks → python/sglang/jit_kernel/tests/ or python/sglang/jit_kernel/benchmark/
• Other kernel correctness → registered/kernels/
• Server needed → registered/<category>/
• 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 (run_eval, 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.

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
    DEFAULT_URL_FOR_TEST,        # auto-configured base URL
    DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,  # 600s default
    run_bench_serving,           # benchmark helper (launch + bench)
)
from sglang.srt.utils import kill_process_tree  # cleanup server

Checklist

Before submitting a test:

• Inherits from CustomTestCase (not unittest.TestCase)
• Has register_*_ci(...) call at module level
• Placed in test/registered/<category>/, unless this is a JIT kernel test/benchmark
• JIT kernel work: files live in python/sglang/jit_kernel/tests/ or python/sglang/jit_kernel/benchmark/
• 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)
• setUpClass launches server, tearDownClass kills it (if server-based)
• tearDownClass is defensive — uses hasattr/null checks before accessing resources that may not have been allocated
• Has if __name__ == "__main__": unittest.main()
• est_time is reasonable (measure locally)