* [feat]: simplify sglang installation with submodule, auto-sync CI, and version alignment
- Add kvcache-ai/sglang as git submodule at third_party/sglang (branch = main)
- Add top-level install.sh for one-click source installation (sglang + kt-kernel)
- Add sglang-kt as hard dependency in kt-kernel/pyproject.toml
- Add CI workflow to auto-sync sglang submodule daily and create PR
- Add CI workflow to build and publish sglang-kt to PyPI
- Integrate sglang-kt build into release-pypi.yml (version.py bump publishes both packages)
- Align sglang-kt version with ktransformers via SGLANG_KT_VERSION env var injection
- Update Dockerfile to use submodule and inject aligned version
- Update all 13 doc files, CLI hints, and i18n strings to reference new install methods
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* [build]: bump version to 0.5.2
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* [build]: rename PyPI package from kt-kernel to ktransformers
Users can now `pip install ktransformers` to get everything
(sglang-kt is auto-installed as a dependency).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Revert "[build]: rename PyPI package from kt-kernel to ktransformers"
This reverts commit e0cbbf6364.
* [build]: add ktransformers meta-package for PyPI
`pip install ktransformers` now works as a single install command.
It pulls kt-kernel (which in turn pulls sglang-kt).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* [fix]: show sglang-kt package version in kt version command
- Prioritize sglang-kt package version (aligned with ktransformers)
over sglang internal __version__
- Update display name from "sglang" to "sglang-kt"
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* [fix]: improve sglang-kt detection in kt doctor and kt version
Recognize sglang-kt package name as proof of kvcache-ai fork installation.
Previously both commands fell through to "PyPI (not recommended)" for
non-editable local source installs. Now version.py reuses the centralized
check_sglang_installation() logic.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* [build]: bump version to 0.5.2.post1
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
* refactor: move legacy code to archive/ directory
- Moved ktransformers, csrc, third_party, merge_tensors to archive/
- Moved build scripts and configurations to archive/
- Kept kt-kernel, KT-SFT, doc, and README files in root
- Preserved complete git history for all moved files
* refactor: restructure repository to focus on kt-kernel and KT-SFT modules
* fix README
* fix README
* fix README
* fix README
* docs: add performance benchmarks to kt-kernel section
Add comprehensive performance data for kt-kernel to match KT-SFT's presentation:
- AMX kernel optimization: 21.3 TFLOPS (3.9× faster than PyTorch)
- Prefill phase: up to 20× speedup vs baseline
- Decode phase: up to 4× speedup
- NUMA optimization: up to 63% throughput improvement
- Multi-GPU (8×L20): 227.85 tokens/s total throughput with DeepSeek-R1 FP8
Source: https://lmsys.org/blog/2025-10-22-KTransformers/
This provides users with concrete performance metrics for both core modules,
making it easier to understand the capabilities of each component.
* refactor: improve kt-kernel performance data with specific hardware and models
Replace generic performance descriptions with concrete benchmarks:
- Specify exact hardware: 8×L20 GPU + Xeon Gold 6454S, Single/Dual-socket Xeon + AMX
- Include specific models: DeepSeek-R1-0528 (FP8), DeepSeek-V3 (671B)
- Show detailed metrics: total throughput, output throughput, concurrency details
- Match KT-SFT presentation style for consistency
This provides users with actionable performance data they can use to evaluate
hardware requirements and expected performance for their use cases.
* fix README
* docs: clean up performance table and improve formatting
* add pic for README
* refactor: simplify .gitmodules and backup legacy submodules
- Remove 7 legacy submodules from root .gitmodules (archive/third_party/*)
- Keep only 2 active submodules for kt-kernel (llama.cpp, pybind11)
- Backup complete .gitmodules to archive/.gitmodules
- Add documentation in archive/README.md for researchers who need legacy submodules
This reduces initial clone size by ~500MB and avoids downloading unused dependencies.
* refactor: move doc/ back to root directory
Keep documentation in root for easier access and maintenance.
* refactor: consolidate all images to doc/assets/
- Move kt-kernel/assets/heterogeneous_computing.png to doc/assets/
- Remove KT-SFT/assets/ (images already in doc/assets/)
- Update KT-SFT/README.md image references to ../doc/assets/
- Eliminates ~7.9MB image duplication
- Centralizes all documentation assets in one location
* fix pic path for README
