LuminolT
c00630088c
Infer the FP4 weight scale group size from the loaded scale tensor instead of hard-coding K/32. This keeps upstream-style FP4 expert layouts working while allowing GLM/ModelOpt NVFP4 layouts that use K/16 scale columns to build MegaMoE sidecar weights. Constraint: preserve the existing runner layout and only change MegaMoE sidecar metadata/recipe. Feature-flag: --moe-a2a-backend=megamoe. Conflict-hotspots: python/sglang/srt/layers/moe/mega_moe.py. Scope-risk: actual DeepGEMM recipe support still needs target GPU runtime validation. Tested: PYTHONPYCACHEPREFIX=/private/tmp/sglang_pycache python3 -m py_compile python/sglang/srt/layers/moe/mega_moe.py test/registered/unit/moe/test_glm_megamoe.py. Tested: git diff --check. Not-tested: GLM 5.2 MegaMoE GPU e2e; local environment lacks target runtime and hardware.
Registered Tests
Tests under this directory are auto-discovered by run_suite.py via CI registration decorators.
Where Should I Put My New Test?
No server / engine launch required
| What you're testing | Directory | Requires |
|---|---|---|
| Component logic in isolation (cache, scheduler, config, parser, etc.) | unit/<module>/ |
CPU or GPU |
| CUDA kernel correctness | kernels/ |
GPU |
Server / engine launch required (E2E)
| What you're testing | Directory | Requires |
|---|---|---|
| Model inference correctness | models/, 4-gpu-models/, 8-gpu-models/ |
GPU |
| Feature-specific (OpenAI API, LoRA, speculative, distributed, VLM, etc.) | openai_server/, lora/, spec/, distributed/, ... |
GPU |
| Benchmarks (performance, accuracy, stress) | benchmark/ |
GPU |
| Platform-specific | amd/, ascend/ |
Vendor GPU |
See unit/README.md for unit test conventions.