2 Commits

Include renames

Author	SHA1	Message	Date
ViperEkura	3439e3104e	perf: launch CUDA kernels on torch's current stream ... - Thread a cudaStream_t through attn dispatchers onto torch's current stream - Scope the device guard to the entry function so kernels run on tensor device - DISPATCH_HEAD_DIM now forwards varargs so stream reaches each dispatch - Parallelize CPU reference kernels with OpenMP (paged test 31s -> 7s) - Merge decode/prefill standalone tests into attn_test.cu with correctness tables - Drop bench error column (CPU ref too slow at large sizes) - Update cuda_kernels.md for the merged test layout	2026-08-02 13:20:14 +08:00
ViperEkura	41dcf0feb9	feat: SGLang-style paged attention kernels replace page-table path ... - PagedAttentionParams uses flat KV pool + req_to_token + kv_indptr/qo_indptr instead of page_table - MMA split-KV decode and split-Q prefill kernels with indirect ragged-batch addressing - Prefill kernel accepts 4D mask (causal-aware); decode kernel supports 2D mask - CudaBackend is inference-only: kv_cache=None raises, no torch fallback - benchmark.py: required --ckpt, --backend/--compare options - Parallel build isolates build-temp/build-lib per subprocess - Standalone test covers decode/prefill with mask, 27 cases pass	2026-08-01 15:41:25 +08:00