* [feat](kt-kernel): add AVX2 RAWINT4 routed-expert LoRA SFT backend
* [fix](kt-kernel): fix RAWINT4 SFT wrapper and clamp gradients
Allow the RAWINT4 production wrapper on AVX2 runtimes and reject shared backward pools before construction. Preserve FP32 clamp decisions across BF16 caches, remove unused configuration state, and cover rectangular dimensions, multiblock dX, clamp boundaries, and production wrapper tasks with tighter LoRA gradient checks.
* [fix](kt-kernel): plumb swiglu_limit through the RAWINT4 SFT python path
The production wrapper hard-zeroed swiglu_limit for every format except MXFP4, and both SFT python gates rejected non-zero values for RAWINT4 even though MOESFTConfig carries the field and the AVX2 kernel implements the clamp in forward plus the gradient mask in backward. RAWINT4_SFT now accepts a finite non-negative swiglu_limit end to end, while the AMX K2 part (which computes plain SiLU) fails fast instead of silently dropping the clamp. The production-wrapper contract test is parametrized over swiglu_limit 0 and 2, a model-config plumb test covers plain and nested text_config shapes, the __main__ entry exercises both limits, and bench_rawint4_sft.py gains --swiglu-limit.
* [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.
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Co-authored-by: ouqingliang <1692110604@qq.com>
Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
* 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
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Co-authored-by: ouqingliang <1692110604@qq.com>
Co-authored-by: Claude <noreply@anthropic.com>
losewayy