23 commits

Author	SHA1	Message	Date
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
callmegaga	a9411f1d72	Supports vnni-256 for GPTQ INT4 (#1926) ... * [feat](kt-kernel): support avx-vnni-256 for gptq int4	2026-04-13 17:59:59 +08:00
Oql	9e6484a538	[fix]: fix --numa-nodes handling (#1904) ... * [fix]: fix --numa-nodes handling	2026-03-31 17:50:22 +08:00
ErvinXie	3903c9afcc	(kt-kernel): add numa_nodes parameter for explicit NUMA node mapping (#1891) ... 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.	2026-03-31 10:27:50 +08:00
mrhaoxx	7a9daf0cd4	[feat](kt-kernel): support avx2 only inference for bf16 fp8 and gptq int4 (#1892) ... * feat: support avx2 bf16 fp8 inference * feat: support avx2 gptq int4 inference * fix: numeric issues in fp8 dequant * Tutorial avx2 (#1900) * fix: prevent injecting -DLLAMA_AVX512=ON on AVX2-only machines * docs: add AVX2 tutorial for running KTransformers on AVX2-only CPUs * Tutorial avx2 (#1901) * fix: prevent injecting -DLLAMA_AVX512=ON on AVX2-only machines * docs: add AVX2 tutorial for running KTransformers on AVX2-only CPUs * docs: update README.md --------- Co-authored-by: Benjamin F <159887351+yyj6666667@users.noreply.github.com>	2026-03-27 14:45:02 +08:00
Chen Hongtao	9e69fccb02	[feat]: add mistral moe loader compatibility (#1873) ... Co-authored-by: chenht2022 <chenht2022@users.noreply.github.com>	2026-02-28 17:50:23 +08:00
VYSE V.E.O	20262b2743	Fix Qwen3.5 FP8 load for VL detection (#1857) ... * Fix Qwen3.5 FP8 load for VL detection 1, for VL models(Qwen3.5), modify base_key: model.layers.{N} -> model.language_model.layers.{N} 2, clean DUPLICATED class BF16SafeTensorLoader(SafeTensorLoader) , only the first overrided one. * Indent type 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>	2026-02-26 15:47:22 +08:00
Jianwei Dong	16a8b98f3e	support qwen3.5 (#1846)	2026-02-16 15:48:14 +08:00
Jiaqi Liao	db82d99fa6	feat: add fallback expert prefix lookup in loader.py from kimi_k2.5 (#1822)	2026-01-30 14:09:38 +08:00
Jiaqi Liao	edc48aba37	[fix]: fix wrapper import issue (#1819)	2026-01-28 16:31:56 +08:00
Oql	bf4c8a690b	Add Native Precision Tutorial, update worker strategy and README.md (#1807)	2026-01-23 18:00:13 +08:00
Jianwei Dong	027832c590	[feat](kt-kernel): CPU-GPU experts sched (#1796)	2026-01-16 17:01:15 +08:00
Oql	6277da4c2b	support GLM 4.7 (#1791) ... support GLM 4.7	2026-01-13 17:36:25 +08:00
Oql	5edc456749	support Native BF16 format MoE. (#1788) ... support Native BF16 format MoE	2026-01-12 14:43:28 +08:00
ErvinXie	d8046e1bb4	Kt minimax (#1742) ... [feat]: fp8 kernel and kt-cli support	2025-12-24 15:39:44 +08:00
SCDESPERTATE	008de19e16	[fix](kt-kernel): drop the weights held in Python for loading weights operation in C++ (#1695)	2025-12-12 11:42:33 +08:00
Oql	8139c092bf	Reduce CPU memory usage during large chunk prefill (Fixes #1676) (#1683) ... * 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	2025-12-08 20:19:07 +08:00
ErvinXie	71f683acec	Support Native Kimi K2 Thinking (#1663) ... * [feat]: fix k2 prefill * Update Kimi-K2-Thinking.md * Create Kimi-K2-Thinking-Native.md * Update Kimi-K2-Thinking.md * Update Kimi-K2-Thinking.md * Update Kimi-K2-Thinking-Native.md * [perf] optimize K2 MoE weight loading with per-expert pointers - Avoid expensive torch.stack().contiguous() in Python (was ~6.6s) - Use per-expert pointer arrays (gate_projs) instead of contiguous memory - C++ worker pool performs parallel memcpy for TP slicing - Add LOAD_TIME_PROFILE for load_weights timing analysis 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> --------- Co-authored-by: ouqingliang <1692110604@qq.com> Co-authored-by: Claude <noreply@anthropic.com>	2025-12-05 21:53:05 +08:00
Jiaqi Liao	fcf8882075	[Feature] Add avx-based kimi-k2 support (#1656) ... * 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>	2025-12-02 16:01:07 +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
Jiaqi Liao	d483147307	Fix kt-kernel compile issue (#1595) ... * update install.sh * fix import issue * update README	2025-11-11 19:30:27 +08:00
Jiaqi Liao	94c25626dc	Fix kt-kernel for new wrapper (#1588) ... * 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	2025-11-10 21:47:34 +08:00
Jiaqi Liao	9bc00e587b	Refactor KTMoEWrapper backend (#1587) ... * universal backend for cpu inference * expert defer	2025-11-10 20:26:15 +08:00