LocalAI/backend/python/sglang/Makefile

24 lines
362 B
Makefile
Raw Normal View History

.PHONY: sglang
sglang:
bash install.sh
.PHONY: run
run: sglang
@echo "Running sglang..."
bash run.sh
@echo "sglang run."
feat(sglang): wire engine_args, add cuda13 build, ship MTP gallery demos (#9686) Bring the sglang Python backend up to feature parity with vllm by adding the same engine_args:-map plumbing the vLLM backend already has. Any ServerArgs field (~380 in sglang 0.5.11) becomes settable from a model YAML, including the speculative-decoding flags needed for Multi-Token Prediction. Validation matches the vllm backend's: keys are checked against dataclasses.fields(ServerArgs), unknown keys raise ValueError with a difflib close-match suggestion at LoadModel time, and the typed ModelOptions fields keep their existing meaning with engine_args overriding them. Backend code: * backend/python/sglang/backend.py: add _apply_engine_args, import dataclasses/difflib/ServerArgs, call from LoadModel; rename Seed -> sampling_seed (sglang 0.5.11 renamed the SamplingParams field). * backend/python/sglang/test.py + test.sh + Makefile: six unit tests exercising the helper directly (no engine load required). Build / CI / backend gallery (cuda13 + l4t13 paths are now first-class): * backend/python/sglang/install.sh: add --prerelease=allow because sglang 0.5.11 hard-pins flash-attn-4 which only ships beta wheels; add --index-strategy=unsafe-best-match for cublas12 so the cu128 torch index wins over default-PyPI's cu130; new pyproject.toml-driven l4t13 install path so [tool.uv.sources] can pin torch/torchvision/ torchaudio/sglang to the jetson-ai-lab index without forcing every transitive PyPI dep through the L4T mirror's flaky proxy (mirrors the equivalent fix in backend/python/vllm/install.sh). * backend/python/sglang/pyproject.toml (new): L4T project spec with explicit-source jetson-ai-lab index. Replaces requirements-l4t13.txt for the l4t13 BUILD_PROFILE; other profiles still go through the requirements-*.txt pipeline via libbackend.sh's installRequirements. * backend/python/sglang/requirements-l4t13.txt: removed; superseded by pyproject.toml. * backend/python/sglang/requirements-cublas{12,13}{,-after}.txt: pin sglang>=0.5.11 (Gemma 4 floor); add cu130 torch index for cublas13 (new files) and cu128 torch index for cublas12 (default PyPI now ships cu130 torch wheels by default and breaks cu12 hosts). * backend/index.yaml: add cuda13-sglang and cuda13-sglang-development capability mappings + image entries pointing at quay.io/.../-gpu-nvidia-cuda-13-sglang. * .github/workflows/backend.yml: new cublas13 sglang matrix entry, mirroring vllm's cuda13 build. Model gallery + docs: * gallery/sglang.yaml: base sglang config template, mirrors vllm.yaml. * gallery/sglang-gemma-4-{e2b,e4b}-mtp.yaml: Gemma 4 MTP demos transcribed verbatim from the SGLang Gemma 4 cookbook MTP commands. * gallery/sglang-mimo-7b-mtp.yaml: MiMo-7B-RL with built-in MTP heads + online fp8 weight quantization, verified end-to-end on a 16 GB RTX 5070 Ti at ~88 tok/s. Uses mem_fraction_static: 0.7 because the MTP draft worker's vocab embedding is loaded unquantised and OOMs the static reservation at sglang's 0.85 default. * gallery/index.yaml: three new entries (gemma-4-e2b-it:sglang-mtp, gemma-4-e4b-it:sglang-mtp, mimo-7b-mtp:sglang). * docs/content/features/text-generation.md: new SGLang section with setup, engine_args reference, MTP demos, version requirements. * .agents/sglang-backend.md (new): agent one-pager covering the flat ServerArgs structure, the typed-vs-engine_args precedence, the speculative-decoding cheatsheet, and the mem_fraction_static gotcha documented above. * AGENTS.md: index entry for the new agent doc. Known limitation: the two Gemma 4 MTP gallery entries ship a recipe that doesn't yet run on stock libraries. The drafter checkpoints (google/gemma-4-{E2B,E4B}-it-assistant) declare model_type: gemma4_assistant / Gemma4AssistantForCausalLM, which neither transformers (<=5.6.0, including the SGLang cookbook's pinned commit 91b1ab1f... and main HEAD) nor sglang's own model registry (<=0.5.11) registers as of 2026-05-06. They will start working when HF or sglang upstream registers the architecture -- no LocalAI changes needed. The MiMo MTP demo and the non-MTP Gemma 4 paths work today on this build (verified on RTX 5070 Ti, 16 GB). Assisted-by: Claude:claude-opus-4-7 [Read] [Edit] [Bash] [WebFetch] [WebSearch] Signed-off-by: Richard Palethorpe <io@richiejp.com>
2026-05-07 15:27:29 +00:00
.PHONY: test
test: sglang
@echo "Testing sglang..."
bash test.sh
@echo "sglang tested."
.PHONY: protogen-clean
protogen-clean:
$(RM) backend_pb2_grpc.py backend_pb2.py
.PHONY: clean
clean: protogen-clean
rm -rf venv __pycache__