* 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>
* docs(DSV4-Flash): AMXINT4 on Skylake (no AMX), dual-Xeon tuning + single-stream negative results
Adds a section based on a validated single-workstation deployment (1x RTX 5090 +
2x Xeon Gold 6138 / Skylake-SP): AMXINT4 runs on AVX512F+BW without an AMX tile;
dual-socket tuning notes; and a table of configs that reduced single-stream
throughput here (notably EAGLE going net-negative on a CPU-bandwidth-bound box,
vs the 8x-RTX-5090 speedup the guide cites).
* Fix command syntax in DeepSeek-V4-Flash.md
---------
Co-authored-by: yyj <159887351+yyj6666667@users.noreply.github.com>
* Add MACA backend support for kt-kernel
* Add MACA event API mappings
* Fix AMX build flags and CPU MoE tests
---------
Co-authored-by: <Engle_Chaveztih@sociologist.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>
- Add convert_cpu_weights_ds4.py: dequantizes MXFP4 routed experts
(E2M1 + ue8m0, group size 32) on GPU and re-quantizes to AMX-INT4 on CPU.
- Document the script as Step 2 in DeepSeek-V4-Flash.md so AMX users
can opt into AMXINT4 mode instead of the default MXFP4 CPU experts.
V4-Flash MXFP4 full-GPU prefill fallback now works:
- Previously crashed all TP schedulers with StopIteration/AttributeError
whenever --kt-gpu-prefill-token-threshold was low enough to actually
fire (path was hardcoded for FP8/INT4 layouts).
- Now detects MXFP4, re-runs the V4 swizzle on the 256-expert gpu_layer,
caches the load across prefill chunks.
- Measured on 8x RTX 5090 (threshold=1024, chunked=1024):
16k input -> 2011 tok/s, 65k -> 2798, 262k -> 2154 prefill TPS.
V4-Flash is incompatible with the transformers 5.x series. transformers
5.x adds default-valued fields to PretrainedConfig that make
DeepSeekV4Config's dataclass declaration crash at import time with:
TypeError: non-default argument 'quantization_config' follows default argument
Reproduced on a fresh venv with `pip install sglang-kt`: pip resolves
transformers to 5.7.0 (sglang-kt's pyproject does not pin transformers),
and `python -m sglang.launch_server --model .../DeepSeek-V4-Flash` fails
during import of sglang.srt.configs.deepseek_v4. Pinning to 4.57.1 fixes
it.
Add a Prerequisite #4 documenting the explicit pin alongside the existing
flashinfer override.
