20 commits

Author	SHA1	Message	Date
Benjamin F	c465557c23	docs(v4-flash): add optional AMXINT4 CPU-weight conversion path (#1986) ... - 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.	2026-05-08 15:35:05 +08:00
mrhaoxx	9544a8960d	feat(sft): AMX MoE SFT backend with LoRA support (#1936) ... * feat(sft): AMX MoE SFT backend with LoRA support Complete SFT (Supervised Fine-Tuning) backend for MoE models using AMX SIMD: Core C++ implementation: - sft_moe.hpp: Forward/backward with LoRA fused operations (~5500 lines) - moe-sft-tp.hpp: Tensor-parallel wrapper for multi-NUMA - amx/moe-sft-tp.hpp: AMX-specific TP implementation - avx_kernels.hpp: AVX512 SIMD kernels for LoRA GEMM - amx_kernels.hpp: AMX tile kernels for Panel5 rank-outer optimization - worker_pool: RDTSC profiling, Chrome trace output, SFT timer infrastructure - ext_bindings.cpp: SFT MOE pybind bindings (BF16/INT8/INT4 + SkipLoRA variants) Python sft/ submodule (kt_kernel.sft): - base.py: BaseSFTMoEWrapper with buffer management (template method pattern) - amx.py: AMXSFTMoEWrapper (weight loading, C++ task construction) - autograd.py: KTMoEFunction (torch.autograd.Function for distributed training) - layer.py: KTMoELayerWrapper (nn.Module replacing HF MoE layers) - arch.py: MOEArchConfig (Qwen3/DeepSeek/Mixtral architecture detection) - weights.py: Expert weight extraction and checkpoint loading - lora.py: PEFT LoRA adaptation (view buffers, grad buffers, save/load adapter) - wrapper.py: wrap_moe_layers_with_kt_wrapper, load_kt_model, build_kt_device_map - config.py: KTConfig dataclass (DeepSpeed-style opaque config passthrough) - dist_utils.py: Distributed gather/scatter, checkpoint-phase detection Design decisions: - Rank-0-only expert pattern: only rank 0 holds C++ wrapper and expert weights - DeepSpeed-style integration: accelerate keeps only KTransformersPlugin (framework interaction fields), all logic in kt_kernel.sft - Inference isolation: importing kt_kernel does not load sft/ submodule - Old field name compatibility: _get_kt_config() converts kt_xxx→xxx automatically Verified: Qwen3-235B-A22B 4GPU AMXBF16 training, loss converges normally. * refactor(sft): unify KTConfig field names with kt_ prefix, add share_cache_pool, remove dead code - 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> * refactor(sft): share_backward_bb default True, share_cache_pool auto-derived - 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> * fix: add missing gpu_experts_mask=None to KTMoEWrapper call in SFT wrapper 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> * feat(sft): support transformers v5 fused expert format 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> * feat(sft): add Qwen3.5 MoE support + fused checkpoint loading - arch.py: add Qwen3_5Moe arch match, read config from text_config, _get_layers_prefix returns model.language_model.layers for Qwen3.5, _get_model_container_and_layers searches language_model attr - weights.py: load_experts_from_checkpoint_files detects fused format (gate_up_proj in weight_map) and splits into gate/up/down - wrapper.py: hidden_size fallback to text_config Verified: Qwen3.5-35B-A3B (256 experts, fused format) E2E pass. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * [fix](sft): align Python API with C++ backend after v5 refactor - wrapper.py: pass gpu_experts_mask=None to KTMoEWrapper (required by C++ signature) - layer.py: rename submit_forward_sft/sync_forward_sft to submit_forward/sync_forward - autograd.py: rename sync_forward_sft to sync_forward The sft-v5 refactor (commits 58d7eab, dd1da65) renamed Python-side method calls but the C++ backend (AMXSFTMoEWrapper) still exposes the original method names. This caused AttributeError on Qwen3.5-35B and other models. * align sft branch with main: revert worker_pool, strip sft_timer, fix inference defaults - Revert worker_pool.cpp/.h to main (remove RDTSC timer, Chrome Trace, sft_timer namespace, ITT API, extended do_work_stealing_job API) - Strip all sft_timer instrumentation from sft-only files (sft_moe.hpp, moe-sft-tp.hpp, avx_kernels.hpp) - Restore pin_memory=True in KExpertsCPUBuffer (inference path) - Restore fused tensor transpose logic in convert_cpu_weights.py (main layout) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * revert CMakeLists.txt to main: remove debug flags and cpptrace dep Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * clean up dev artifacts: remove SFT design docs, debug examples, bench scripts Remove files not needed in the merge: - docs/SFT+KTWrapper/ (6 Chinese design docs) - docs/sft_moe_amx/ (21 dev/debug docs) - 12 debug/test example scripts - 6 SFT-specific bench scripts and report Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * remove dev version stamps from ext_bindings, sft_moe, moe-sft-tp Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com> Co-authored-by: JimmyPeilinLi <lipeilin@mail.nwpu.edu.cn>	2026-04-22 11:27:01 +08:00
Doctor Shotgun	24cd4fc055	feat(kt-kernel): Add utility script to merge loose layer weights to safetensors (#1886) ... * Add utility script to merge loose layer weights to safetensors * Send warnings and errors to stderr * Fix expert index parsing for MOE_INT4 and MOE_INT8	2026-03-31 10:41:07 +08:00
alin899992	9c18b60556	feat: CPU weight conversion for GLM-5 and MiniMax-M2.5 (#1853) ... * Support for GLM-5 and Minimax-M2.5 Add CPU weight conversion support for GLM-5 and Minimax-M2.5 * fix: remove overly restrictive MiniMax condition and deduplicate code - Remove `args.input_type == "fp8"` from MiniMaxConverter selection so bf16/fp16 MiniMax models no longer fall through to OnlineQuantConverter (which doesn't handle w1/w2/w3 naming and would fail). - Remove OnlineQuantConverter._find_expert_layers() which is identical to the inherited ConverterBase._find_expert_layers(). - Remove redundant expert_key_filter assignment (same as base default). --------- Co-authored-by: ErvinXie <ervinxie@foxmail.com>	2026-03-31 10:39:48 +08:00
Jianwei Dong	027832c590	[feat](kt-kernel): CPU-GPU experts sched (#1796)	2026-01-16 17:01:15 +08:00
Jiaqi Liao	46b0f36980	[feat](kt-kernel): Fix CPU instruction set variants for build & install (#1746) ... * [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	2025-12-24 18:57:45 +08:00
mrhaoxx	503295fc88	[feat](kt-kernel): refactor convert_cpu_weights.py to support conversation for GLM-4.6V (#1687) ... Signed-off-by: mrhaoxx <mr.haoxx@gmail.com>	2025-12-09 14:24:41 +08:00
Jianwei Dong	fd78fe520a	fix(scripts): resolve OOM when converting gpu weights and update README (#1640)	2025-12-01 14:15:14 +08:00
mrhaoxx	637c49c83f	[feat](kt-kernel): support qwen3-vl weights convert (#1648)	2025-11-27 22:29:09 +08:00
ZiWei Yuan	1374b98ee5	[feat](moe_kernel): add amd blis support (int8) (#1600) ... * [feat]: init amd adaption * [feat]: add blis support * [fix]: fix setup and moe kernel warpper * [fix](setup.py): support rebuild with cache and import kt_kernel works fine * [feat]: add moe_kernel converter for amd and implement the load method(haven't tested yet) * [feat](moe_kernel/moe.hpp): delete unused memory when using save * [fix](moe_kernel): update PLAIN for pack * [fix](moe_kernel): rm printf debug * [fix](moe_kernel): skip gpu experts * [fix](moe_kernel/moe.hpp): update include memory path * [feat](moe_kernel/moe.hpp): support expert deferral * [feat]: finish amd --------- Co-authored-by: mrhaoxx <mr.haoxx@gmail.com>	2025-11-27 12:08:53 +08:00
Jianwei Dong	51745a9ea1	add ci (#1642)	2025-11-25 20:52:08 +08:00
DocShotgun	e72a4fb880	[feat](kt-kernel): Add resume arg to CPU weight conversion (#1630) ... * [feat]: kt-kernel: Add resume arg to CPU weight conversion * [docs]: kt-kernel: Document resume arg for CPU weight conversion * [fix]: kt-kernel: Only print resume layer if in use * [fix]: kt-kernel: Don't log skipped layers when using resume_layer	2025-11-22 12:00:15 +08:00
ZiWei Yuan	aef6672dd8	[docs]: add contribuing guide and add hooks install (#1613) ... * [feat]: update kt-kernel hooks and add contribution guide * [docs]: add contributing guide * [style]: format the python file and cpp file in kt-kernel	2025-11-15 18:26:49 +08:00
Jiaqi Liao	13b8ddecd9	AMXMoEWrapper -> KTMoEWrapper (#1604) ... fix import KTMoEWrapper	2025-11-12 16:34:54 +08:00
Oql	34c71ba8bf	Merge pull request #1568 from kvcache-ai/add_bf16_scripts ... add convert_moe_to_bf16.py	2025-11-07 17:55:38 +08:00
ouqingliang	a18f007d45	add convert_moe_to_bf16.py	2025-11-07 09:53:19 +00:00
Peilin Li	d939e56646	add the convert from fp8 to bf16 for Kimi-K2 model	2025-11-06 17:20:28 +08:00
ovowei	f854d03bd7	update kt-kernel	2025-11-03 15:19:52 +08:00
ovowei	28d8663374	fix	2025-10-22 18:14:34 +08:00
Atream	4c5fcf9774	add kt-kernel	2025-10-12 05:13:00 +00:00