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>
* 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>
* 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
