The bs>1 path needs index top-k, shared-index prepare, current-index compact, and current-slot compose to consume flattened batch descriptors instead of falling back to per-request or per-segment Python/Torch work. This change wires SGLang to the new TAI batch prepare kernels, keeps fallback explicit, and records the remaining HiCache/load-backup gaps in the bs>1 workstream docs. Constraint: CP shared-KV bs>1 must reuse fast paths rather than adding slow batch-only fallbacks Constraint: No new collective operations were introduced Rejected: Leave current-only cp_index as Python slice/cat | it keeps per-segment overhead in the short-extend bs>1 case Rejected: Infer max segment lengths from CUDA descriptor tensors | .item() would add CPU synchronization on the hot path Confidence: medium Scope-risk: moderate Directive: Do not remove the explicit fallback warnings without verifying the corresponding TAI symbols are present in production Tested: local py_compile for touched SGLang files Tested: remote g0034 test_nsa_cp_utils.py passed, 53 tests Tested: remote g0034 test_fill_current_index_page_slots_uses_tai_kernel_when_available passed Not-tested: full ETE bs>1 traffic with HiCache load/backup and draft/EAGLE enabled
SGLang Documentation
This is the documentation website for the SGLang project (https://github.com/sgl-project/sglang).
We recommend new contributors start from writing documentation, which helps you quickly understand SGLang codebase.
Most documentation files are located under the docs/ folder.
Docs Workflow
Install Dependency
Linux:
apt-get update && apt-get install -y pandoc parallel retry
pip install -r requirements.txt
macOS:
brew install pandoc parallel retry
pip install -r requirements.txt
Update Documentation
Update your Jupyter notebooks in the appropriate subdirectories under docs/. If you add new files, remember to update index.rst (or relevant .rst files) accordingly.
pre-commit run --all-filesmanually runs all configured checks, applying fixes if possible. If it fails the first time, re-run it to ensure lint errors are fully resolved. Make sure your code passes all checks before creating a Pull Request.
# 1) Compile all Jupyter notebooks
make compile # This step can take a long time (10+ mins). You can consider skipping this step if you can make sure your added files are correct.
make html
# 2) Compile and Preview documentation locally with auto-build
# This will automatically rebuild docs when files change
# Open your browser at the displayed port to view the docs
bash serve.sh
# 2a) Alternative ways to serve documentation
# Directly use make serve
make serve
# With custom port
PORT=8080 make serve
# 3) Clean notebook outputs
# nbstripout removes notebook outputs so your PR stays clean
pip install nbstripout
find . -name '*.ipynb' -exec nbstripout {} \;
# 4) Pre-commit checks and create a PR
# After these checks pass, push your changes and open a PR on your branch
pre-commit run --all-files
Documentation Style Guidelines
- For common functionalities, we prefer Jupyter Notebooks over Markdown so that all examples can be executed and validated by our docs CI pipeline. For complex features (e.g., distributed serving), Markdown is preferred.
- Keep in mind the documentation execution time when writing interactive Jupyter notebooks. Each interactive notebook will be run and compiled against every commit to ensure they are runnable, so it is important to apply some tips to reduce the documentation compilation time:
- Use small models (e.g.,
qwen/qwen2.5-0.5b-instruct) for most cases to reduce server launch time. - Reuse the launched server as much as possible to reduce server launch time.
- Use small models (e.g.,
- Do not use absolute links (e.g.,
https://docs.sglang.io/get_started/install.html). Always prefer relative links (e.g.,../get_started/install.md). - Follow the existing examples to learn how to launch a server, send a query and other common styles.
Documentation Build, Deployment, and CI
The SGLang documentation pipeline is based on Sphinx and supports rendering Jupyter notebooks (.ipynb) into HTML/Markdown for web display. Detailed logits can be found in the Makefile.
Notebook Execution (make compile)
The make compile target is responsible for executing notebooks before rendering:
- Finds all
.ipynbfiles underdocs/(excluding_build/) - Executes notebooks in parallel using GNU Parallel, with a relatively small
--mem-fraction-static - Wraps execution with
retryto reduce flaky failures - Executes notebooks via
jupyter nbconvert --execute --inplace - Records execution timing in
logs/timing.log
This step ensures notebooks contain up-to-date outputs with each commit in the main branch before rendering.
Web Rendering (make html)
After compilation, Sphinx builds the website:
- Reads Markdown, reStructuredText, and Jupyter notebooks
- Renders them into HTML pages
- Outputs the website into:
docs/_build/html/
This directory is the source for online documentation hosting.
Markdown Export (make markdown)
To support downstream consumers, we add a new Makefile target:
make markdown
This target:
- Does not modify
make compile - Scans all
.ipynbfiles (excluding_build/) - Converts notebooks directly to Markdown using
jupyter nbconvert --to markdown - Writes Markdown artifacts into the existing build directory:
docs/_build/html/markdown/<relative-path>.md
Example:
docs/advanced_features/lora.ipynb
→ docs/_build/html/markdown/advanced_features/lora.md
CI Execution
In our CI, the documentation pipeline first gets all the executed results and renders HTML and Markdown by:
make compile # execute notebooks (ensure outputs are up to date)
make html # build website as usual
make markdown # export markdown artifacts into _build/html/markdown
Then, the compiled results are forced pushed to sgl-project.io for rendering. In other words, sgl-project.io is push-only. All the changes of SGLang docs should be made directly in SGLang main repo, then push to the sgl-project.io.