Fused experts (e.g. Qwen3MoeExperts) store weights as 3D Parameters
(gate_up_proj [E,2I,H], down_proj [E,H,I]) instead of per-expert
nn.Linear modules. PEFT cannot attach LoRA to these, so we create
KT-managed LoRA buffers with kaiming init, nn.Parameter wrappers
for the optimizer, and pre-assigned .grad for C++ backward.
- arch.py: detect_fused_experts() detection
- weights.py: fused format extraction and weight clearing
- wrapper.py: detect fused at wrap time, store _fused_experts/_lora_rank
- lora.py: _create_fused_expert_lora_buffers, save/load fused LoRA,
get_kt_lora_params collects fused params, deduplicate wrapper finding
- layer.py: handle v5 TopKRouter tuple output, remove dead code
- autograd.py: sync_forward_sft/submit_forward_sft API rename
Verified: v5 loss/expert-LoRA values match v4 baseline, v4 backward compat.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
KTMoEWrapper.__new__() requires gpu_experts_mask as a positional argument,
but the SFT wrapper omitted it, causing MoE layer wrapping to fail silently
and FSDP2 to attempt broadcasting all expert weights (OOM/NCCL crash).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- kt_share_backward_bb defaults to True (always saves memory)
- kt_share_cache_pool no longer reads from env var; defaults False,
auto-set to True by trainer_config_process when gradient checkpointing
is enabled
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- KTConfig fields all use kt_ prefix matching dict keys — eliminates
_OLD_TO_NEW mapping and prefix-stripping in wrapper.py
- Add kt_share_cache_pool field, auto-enabled when gradient_checkpointing
is on (via training_args.py), flows through to C++ cache allocation
- Remove dead checkpoint detection code: in_ckpt_recompute,
in_ckpt_first_forward vars (assigned but never read), fallback
_is_in_checkpoint_first_forward() function, unused inspect import
- Remove redundant env var fallbacks in wrapper.py for share_backward_bb
and share_cache_pool (KTConfig.__post_init__ already handles env vars)
- Simplify layer.py checkpoint logic to single _checkpoint_hook_mode() check
Verified: Qwen3-235B 3-step training on sap4, loss matches baseline
(1.2886 / 1.9824 / 1.377 vs 1.2886 / 1.9766 / 1.3809)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Increase timeout for Check if --kt-gpu-prefill-token-threshold is in the help output to 90 seconds.
In cloud environments,CUDA initialization and Python module loading can easily exceed 30 seconds.
* Update kt-kernel/python/cli/utils/sglang_checker.py
add comment about the change
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
---------
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
* [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>
* Handle unquoted paths and special characters in model scanner
* Fix ValueError: capture_output cannot be used with stderr
`capture_output=True` internally sets `stderr=PIPE`, which conflicts
with `stderr=subprocess.DEVNULL`. Replace `capture_output=True` with
explicit `stdout=subprocess.PIPE` to keep stderr suppressed correctly.
Also remove redundant `shell=False` (already the default).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---------
Co-authored-by: ErvinXie <ervinxie@foxmail.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
* [feat]: redesign kt run interactive configuration with i18n support
- Redesign kt run with 8-step interactive flow (model selection, inference method, NUMA/CPU, GPU experts, KV cache, GPU/TP selection, parsers, host/port)
- Add configuration save/load system (~/.ktransformers/run_configs.yaml)
- Add i18n support for kt chat (en/zh translations)
- Add universal input validators with auto-retry and Chinese comma support
- Add port availability checker with auto-suggestion
- Add parser configuration (--tool-call-parser, --reasoning-parser)
- Remove tuna command and clean up redundant files
- Fix: variable reference bug in run.py, filter to show only MoE models
* [feat]: unify model selection UI and enable shared experts fusion by default
- Unify kt run model selection table with kt model list display
* Add Total size, MoE Size, Repo, and SHA256 status columns
* Use consistent formatting and styling
* Improve user decision-making with more information
- Enable --disable-shared-experts-fusion by default
* Change default value from False to True
* Users can still override with --enable-shared-experts-fusion
* [feat]: improve kt chat with performance metrics and better CJK support
- Add performance metrics display after each response
* Total time, TTFT (Time To First Token), TPOT (Time Per Output Token)
* Accurate input/output token counts using model tokenizer
* Fallback to estimation if tokenizer unavailable
* Metrics shown in dim style (not prominent)
- Fix Chinese character input issues
* Replace Prompt.ask() with console.input() for better CJK support
* Fixes backspace deletion showing half-characters
- Suppress NumPy subnormal warnings
* Filter "The value of the smallest subnormal" warnings
* Cleaner CLI output on certain hardware environments
* [fix]: correct TTFT measurement in kt chat
- Move start_time initialization before API call
- Previously start_time was set when receiving first chunk, causing TTFT ≈ 0ms
- Now correctly measures time from request sent to first token received
* [docs]: 添加 Clawdbot 集成指南 - KTransformers 企业级 AI 助手部署方案
* [docs]: 强调推荐使用 Kimi K2.5 作为核心模型，突出企业级推理能力
* [docs]: 添加 Clawdbot 飞书接入教程链接
* [feat]: improve CLI table display, model verification, and chat experience
- Add sequence number (#) column to all model tables by default
- Filter kt edit to show only MoE GPU models (exclude AMX)
- Extend kt model verify to check *.json and *.py files in addition to weights
- Fix re-verification bug where repaired files caused false failures
- Suppress tokenizer debug output in kt chat token counting
* [fix]: fix cpu cores.
---------
Co-authored-by: skqliao <skqliao@gmail.com>
* [feat]: Enhance CPU feature detection and support for AVX512 extensions
- Added cmake/DetectCPU.cmake for automatic CPU feature detection.
- Updated CMakeLists.txt to include auto-detection logic for AVX512 features.
- Modified install.sh to include new AVX512_VBMI option for FP8 MoE.
- Enhanced _cpu_detect.py to support progressive matching of CPU variants.
- Created scripts/check_cpu_features.py for manual CPU feature checks.
- Updated setup.py to reflect changes in CPU variant building and environment variables.
* [fix](kt-kernel): Add conditional inclusion of FP8 MoE for AVX512 BF16 support
* [chore](kt-kernel): update project version to 0.5.0 in CMakeLists.txt and version.py
* fix(amx): add BufferASmallKGroupImpl to fix buffer overflow in from_mat
The original BufferAKGroupImpl::from_mat writes 64 bytes per K_STEP iteration
but when K_STEP=32 (for GemmKernel224Int4SmallKGroup), this causes buffer overflow.
BufferASmallKGroupImpl overrides from_mat to write only 32 bytes per iteration.
* perf(k2-moe): optimize memory allocation with pooled buffers
- Replace per-expert buffer allocation with shared memory pools
- Dynamically assign buffer slices based on activated experts
- Add group_size inference from scale tensor shape in amx.py
* delete kimi k2 forward test
* add TODO comment for pool_count_ calculation
* support Kimi-K2-Thinking original weight
fix amx kernel bug
* update k2 avx kernel.
* feat: add CPUInfer write buffer task
* [feat]: add kimi k2 cpu write buffer support
- Implement write_weights_to_buffer function in k2-moe.hpp for extracting GPU expert weights
- Fix down (w2) weight column-wise slicing for different TP configurations
- Support three TP scenarios: cpu_tp == gpu_tp, cpu_tp > gpu_tp, cpu_tp < gpu_tp
- Add comprehensive test cases for weight extraction validation
- Ensure compatibility with Kimi model's MoE architecture
* [fix]: correct write_weight_scale_to_buffer expert offset calculation
Fixed the bug in write_weight_scale_to_buffer_task where expert offsets in GPU buffers were incorrectly calculated. Changed from using per_expert_gpu sizes to using full gpu_tp sizes, ensuring correct memory layout for multi-expert scenarios.
Also added benchmark scripts for k2 moe and write buffer operations, and cleaned up debug output in test files.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* [feat]: add write buffer wrapper
* [fix] fix comment
---------
Co-authored-by: ouqingliang <1692110604@qq.com>
Co-authored-by: Claude <noreply@anthropic.com>
* update README for kt-kernel
* style: format C++ and Python code in kt-kernel
- Format C++ files: task_queue, ext_bindings, and MoE operators
- Format Python utility modules: amx, llamafile, and loader
- Improve code readability and consistency
