* [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
* [fix](kt-kernel): fix AVX512 cpu instruction set detection
* [feat](kt-kernel): AVX512 fallback kernel for RAW-INT4
* [fix](kt-kernel): fix setup version issue
* [fix](kt-kernel): update install for custom build
* [docs](kt-kernel): new installation guide for various cpu instruction set
* [fix](kt-kernel): fix _mm512_dpbusd_epi32_compat fallback implmentation
* [style](kt-kernel): clang format
* 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
* 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>
* [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
* [feat]: update kt-kernel hooks and add contribution guide
* [docs]: add contributing guide
* [style]: format the python file and cpp file in kt-kernel
