Files
sglang/test/registered/unit
laoyao0822 9c8e3e99cb Align OpenAI serving behavior with Para deployments
Absorb PR 11's final Para compatibility surface as an opt-in OpenAI serving layer rather than hard-coding business defaults into protocol models. The change adds server args for Para chat defaults, Kimi/GLM compatibility, tool-choice normalization, tool-role text flattening, and streaming first-chunk error preflight while preserving default upstream behavior unless explicitly enabled.

Reasoning token usage is also propagated through chat/completion usage paths, with GLM compatibility emitting completion_tokens_details.reasoning_tokens. Low-risk protocol fixes accept string image_url content parts and preserve GLM function-call argument value whitespace.

Constraint: Online Para-compatible deployments require request/response semantics that differ from default OpenAI serving behavior.

Constraint: Current CP/HiCache/bs>1 work must not be coupled to OpenAI serving compatibility changes.

Rejected: Merge PR 11 history directly | intermediate commits briefly hard-code chat max_tokens=32768 before later gating it by server args.

Rejected: Enable Para compatibility by default | would change non-Para OpenAI-compatible deployments.

Confidence: high

Scope-risk: moderate

Directive: Keep Para-specific serving policies behind explicit server args unless the business contract changes globally.

Tested: PYTHONPATH=python:. python -m unittest discover -s test/registered/unit/entrypoints/openai -p 'test_para_serving_protocol.py' -v (19 tests OK)

Tested: python -m py_compile modified OpenAI serving, tokenizer manager, server_args, function-call detector, and test files

Not-tested: Live router/prefill/decode OpenAI serving E2E after enabling Para flags.

Co-authored-by: OmX <omx@oh-my-codex.dev>
2026-06-11 05:59:08 +08:00
..

Unit Tests

Component-level tests that do not launch a server or load model weights. Tests can use CPU or GPU — the key criterion is no server process.

Quick Start

  1. Find the source file under python/sglang/srt/.
  2. Create the corresponding test here, mirroring the source tree:
    srt/mem_cache/radix_cache.py       →  unit/mem_cache/test_radix_cache.py
    srt/sampling/sampling_params.py    →  unit/sampling/test_sampling_params.py
    
  3. Register for CI at the top of the file (after imports, before test classes):
    from sglang.test.ci.ci_register import register_cpu_ci
    register_cpu_ci(est_time=5, suite="stage-a-test-cpu")
    # or: register_cuda_ci(est_time=10, suite="stage-b-test-1-gpu-small")
    
  4. Run locally:
    pytest test/registered/unit/ -v            # all unit tests
    pytest test/registered/unit/mem_cache/ -v  # one module
    
  5. Run with coverage:
    # summary
    pytest test/registered/unit/ --cov --cov-config=.coveragerc -v
    
    # PR incremental check (require ≥60% on changed lines)
    pytest test/registered/unit/ --cov --cov-config=.coveragerc --cov-report=xml
    diff-cover coverage.xml --compare-branch=origin/main --fail-under=60
    

Example

"""Unit tests for <module> — no server, no model loading."""

from sglang.test.ci.ci_register import register_cpu_ci

register_cpu_ci(est_time=5, suite="stage-a-test-cpu")

import unittest

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


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


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

Rules

  • No popen_launch_server() or Engine(...).
  • No model weight loading.
  • Use CustomTestCase (from sglang.test.test_utils, adds CI retry).
  • Use unittest.mock for dependencies that are expensive to construct.