* Add full FT development snapshot
* [fix](kt-kernel): fix Full FT TP base weight gradients
* [fix]: bug fix of 2d81e86
* [fix](kt-kernel): fix AMX BF16 full-weight gradients
* [docs](kt-kernel): document Full FT fork changes and debug history
* [docs](kt-kernel): align fork remote terminology
* [chore](kt-kernel): keep agent notes local
* [fix](kt-kernel): configure SFT OpenMP threads
* [perf](kt-kernel): optimize AMX Full-FT weight gradients
Coarsen base-weight gradient work from individual output tiles to fixed-intermediate strips so each task reuses packed panels across the hidden dimension.
Keep aligned thread-local BF16 panels across tasks and retain FP32 AMX accumulator tiles for the full K reduction. Gate and up run separate K passes while sharing the packed input panel.
On the matched Qwen3-30B-A3B 1-GPU test, stable Full-FT backward drops from 9.281s to 6.792s (-26.82%), step time drops from 19.252s to 16.140s, and TPS rises from 212.76 to 253.78. The LoRA-only backward control changes by -2.74%.
Validated with clang-format, the Release AMX/CUDA extension build, TP1/TP2 reference gradients across boundary token counts, and the 15-step Full-then-LoRA performance run.
* [feat](kt-kernel): add staged SFT profiling
* [fix](kt-kernel): reuse inference BF16 kernel for SFT
* [perf](kt-kernel): add fine-grained Full-FT profiling
* [perf](kt-kernel): batch BF16 Full-FT weight gradients
Use one expert-aggregated tile driver for AVX512-BF16 and AMX base-weight gradients, and pack updated full-precision weights directly into TP BufferB layouts without temporary partitions. Add worker-local profiling and focused dWeight/strided-repack coverage.
* [test](kt-kernel): benchmark BF16 dWeight AMX driver
* [fix](kt-kernel): label dWeight store as worker CPU time
* [docs](sft): record BF16 Full-FT performance
* [docs](sft): remove Qwen3 Full-FT performance report
* [perf](kt-kernel): reduce BF16 Full-FT checkpoint overhead
Retain the first CPU MoE forward state across non-reentrant checkpoint recomputation, write BF16 activations directly into the backward cache, and reduce dWeight packing and gradient-clear traffic. Extend staged profiling and cover checkpoint reuse plus AMX/AVX dWeight paths.
* [perf](kt-kernel): make SFT optimizer gradients authoritative
Bind Full-FT and LoRA Parameter.grad directly to the KT-managed BF16 gradient buffers, avoiding PyTorch duplicate accumulation.
Accumulate microbatch gradients in C++, lazily clear expert buffers between optimizer windows, preserve rank-0 distributed ownership, and add lifecycle and AMX dWeight coverage.
* [perf](kt-kernel): avoid eager Full-FT gradient zeroing
Allocate authoritative Full-FT gradient buffers with torch.empty. The C++ state machine performs the mandatory full clear before first use, avoiding redundant Python-side first touch.
* perf(sft): enable checkpoint forward reuse for LoRA
* fix(sft): serialize checkpoint recompute with async repack
* fix(sft): persist authoritative full weights
* fix(sft): normalize legacy distributed gradients
* [fix](sft): train gated shared experts
* fix(sft): preserve expert placeholders across state dict loads
* ci: publish ktransformers sdist in release workflow
* fix(sft): preserve router autograd for LoRA training
Determine routing graph tracking from the router's trainable parameters instead of the Full-FT mode flag, so PEFT LoRA adapters on MoE gates receive routing-weight gradients. Fail fast when a trainable router returns detached weights and cover frozen, TopK, checkpoint-reuse, and two-step optimizer behavior.
---------
Co-authored-by: illu <wubowen03@foxmail.com>
* [fix(loader)]: correct off-by-one expert-count guard in SafeTensorLoader.load_experts
After the discovery loop, max_experts_count is the highest expert index found
(expert count - 1), and is -1 only when the key has no experts. The guard
checked == 0, which falsely rejected single-expert layers and silently returned
empty weight lists for the zero-expert case. Check == -1 instead.
Adds a CPU regression test covering the single-, zero-, and multi-expert cases.
* [test(loader)]: import loader as a top-level module in expert-count guard test
Per review feedback: add python/utils to sys.path and import loader directly
instead of the importlib.util boilerplate. Still bypasses utils/__init__.py
(and the compiled kt_kernel_ext) while keeping the import idiomatic.
* [fix](cli): detect SGLANG_DSV4_2604_SUBMODE conflict before launch
* [fix](cli): tighten env-var validation per review feedback
doctor.py: skip SGLANG_DSV4_2604_SUBMODE row when value is empty string,
not just None, to avoid spurious noise in kt doctor output.
run.py: guard kt_method against None/empty before calling .upper() in
_check_conflicting_env_vars to prevent AttributeError.
* [feat](kt-kernel): AVX2 MXFP4 MoE MXFP4 dispatch
- Add AVX2 MXFP4 MoE kernel (mxfp4-moe.hpp) with 4-token M-blocking
- Wire AVX2MXFP4_MOE binding in ext_bindings.cpp
- Support TP_MOE down_proj slicing and multi-pool per-expert loading
- Add test_fp4_moe_avx2.py integration test
* [fix](kt-kernel): address PR #2010 review — memory leaks, alignment, dynamic expert update
- Track aligned_alloc pointers in AVX2_MOE_BASE::owned_aligned_allocs_ and
free them in the destructor (fixes BufferB backing memory leak on destroy).
- Track per-TP down_buf allocations in TP_MOE::tp_owned_down_bufs_ with
nullptr checks and size rounding to alignment boundary.
- Add nibble-alignment runtime check for per_tp_interm in MXFP4 TP K-split.
- Add write_weight_scale_to_buffer override to TP_MOE<AVX2_MXFP4_MOE_TP>,
enabling dynamic expert update with kt-threadpool-count>=2.
- Guard against ZeroDivisionError in test_fp4_moe_avx2.py.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* [fix](kt-kernel): add intermediate_size parity check in MXFP4 TP flat-buffer path
The per-expert path validates that intermediate_size is even (required for
nibble-aligned FP4 addressing), but the flat-buffer path was missing this
check — an odd value would silently truncate /2 divisions, corrupting
memcpy sizes and offsets.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix(avx2-moe): fix TP offset calculation and add safety checks
C1-C4: Fix incorrect TP offset calculations in load_weights()
- Per-expert mode used per_tp_interm instead of full_interm for offsets
- This caused segfault when TP > 1 due to invalid pointer arithmetic
H1-H3: Add safety checks
- H1: Validate source weight pointers are not null
- H2: Check lid index is within bounds
- H3: Check BufferB.b is not null in gemm_mxfp4
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* fix(avx2-moe): revert incorrect C2/C4 offset changes, keep safety checks
Reverts the incorrect offset calculation changes from previous commit.
The original per_tp_interm-based offsets were correct:
- gate/up weights are N-split (along intermediate dim)
- Each TP partition handles per_tp_interm rows
- Offset = i * per_tp_interm * hidden / 2 (not full_interm)
Keeps H1-H3 safety checks:
- H1: Validate source weight pointers are not null
- H2: Check lid index is within bounds
- H3: Check BufferB.b is not null in gemm_mxfp4
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* fix(avx2): copy weights to owned buffers in per-expert mode
Previously, AVX2 MXFP4 MoE per-expert mode directly pointed BufferB.b
into mmap'd safetensor data. This caused use-after-free when Python
layer releases the mmap after load_weights() returns.
Now AVX2 copies weights into owned buffers via memcpy/from_raw_mat(),
matching AMX behavior. This decouples the MoE weights from mmap lifecycle.
Changes:
- buffer_b_required_size_impl: always allocate full buffer (weights + scales)
- make_buffer_b_impl: always create full BufferB with owned storage
- Single-TP per-expert: use from_raw_mat() instead of direct pointer
- TP_MOE per-expert: add gate/up owned buffers with memcpy
- Destructor: free gate/up buffers alongside down
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* Revert "[fix] Add runtime AMX BF16 check to prevent SIGILL on pre-Sapphire Rapids CPUs (#2018)"
This reverts commit f1e2b82c74.
* Remove AMX tile MXFP4 kernel (GemmKernel224MXFP4)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Release the SafeTensor mmap loader singleton after each layer's
load_weights() completes. The C++ engine already holds a deep copy
(cpu_infer.sync() guarantees this), so releasing the mmap handles is
safe. The next layer recreates the loader on demand.
This halves peak memory usage during model loading (e.g. DSv3.2:
1.2T -> 613G).
Based on #1966 by @poryfly — adapted to v0.6.2.post3 codebase
(adds MXFP4 support missing from the original PR).
Co-authored-by: xiongchenhui <xiongchenhui@hisense.com>
* [feat](kt-kernel): add MXFP4 MoE operator with E2M1 weights × BF16 activations
Implements AMX_FP4_MOE_TP based on the RAWINT4 (k2-moe) CRTP pattern.
FP4 E2M1 weights are nibble-packed and decoded via PSHUFB LUT, then
computed with BF16 activations using _mm512_dpbf16_ps. Supports weight-only
per-kgroup scaling (group_size=32) and tensor parallelism.
Includes a Python validation test covering uniform, alternating, ramp,
and random weight patterns.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
* [feat](kt-kernel): adapt MXFP4 MoE backend for DeepSeek-V4-Flash (#1950)
V4-Flash routed experts ship as native MXFP4 (E2M1 nibble + ue8m0 group
scale). Expose AMXFP4_KGroup_MOE through NativeMoEWrapper, add a loader
that handles V4's `layers.{L}.ffn.experts.{i}.{w1,w3,w2}.{weight,scale}`
naming and converts ue8m0 → bf16 via a lossless bit-cast, register the
model entry, and ship an end-to-end numerical validation script.
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* [perf](kt-kernel): MXFP4 MoE add mat-mat 4×4 tile, refine mat-vec reduce (#1957)
mat_mul_kgroup previously aliased to fp4_mat_vec_kgroup, leaving large
batches stuck on the per-token path. Implement fp4_mat_mat_kgroup as a
4×4 register tile (MB=NB=4, 16 zmm accumulators) so each PSHUFB decode
of four weight rows is reused across four tokens.
Refactor fp4_mat_vec_kgroup to accumulate four N-rows in parallel and
flush them with a new reduce4 helper, removing per-row reduce_add_ps
calls from the hot loop. Mark mxfp4_to_bf16_32 always_inline.
Add bench/bench_fp4_moe.py with --routing {balanced,concentrated} and
a backend registry so future kernels can be added without changing the
runner.
Dispatch thresholds, derived_init, GeneralMOEConfig handling,
load_weights, write_weights_to_buffer and the TP_MOE specialization are
unchanged.
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(loader): avoid uint16 lshift in ue8m0->bf16 conversion
PyTorch CPU has no lshift kernel for UInt16, so the previous
`(scale_t.to(torch.uint16) << 7)` raised NotImplementedError when
loading any V4-Flash MXFP4 routed-expert scale tensor on the host.
Switch to int32 for the shift (kernel exists) and narrow to int16
afterwards. The shifted value max is 255<<7 = 32640, well within
int16 range, so the narrow is lossless. The .view(bfloat16) bit
pattern is identical (bf16 sign bit is always 0 for ue8m0 values).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* docs(v4-flash): hybrid CPU/GPU recipe + bump kt-sglang submodule
Bumps third_party/sglang to kvcache-ai/sglang main (3cbd49c29) which now
contains DeepSeek V4 Flash model support + consumer-GPU (SM_120) portable
Triton/TileLang fallbacks (kt-sglang PR #38).
Adds doc/en/DeepSeek-V4-Flash.md tutorial: 8x RTX 5090 hybrid recipe with
the full launch command, OpenAI-compatible /generate + /v1/chat/completions
examples, and the kt chat CLI client.
---------
Co-authored-by: ouqingliang <1692110604@qq.com>
Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
Add numa_nodes parameter to BaseMoEWrapper and all subclasses, allowing
users to explicitly specify which NUMA node IDs to use for subpool
mapping instead of always defaulting to sequential [0, 1, ..., N-1].
This enables running multiple KTransformers instances on different NUMA
nodes of the same machine, e.g. --kt-threadpool-count 1 --kt-numa-nodes 1
to bind to NUMA node 1. Previously this required external numactl
workarounds since subpool_numa_map was hardcoded to start from 0.
* 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
Octopus