- update mla_prefill, mla_decode, gqa kernel definitions, workload, and solution
- re-collect all attention kernel to now larger than M=512 and S=512
It is shown that attention kernel reference is stronger than baseline, may have change on the workload again later
Definition+workload update for rmsnorm, and baseline, reference-scalar, and claude-sonnet-4-6 solution update
ArtificialRay
f55190a
- definitions+workload update for rmsnorm: reset dtype to fp32
- llama.cpp reference-scalar, baseline-llamacpp-arm solution update: change dtype to fp32
- claude-sonnet-4-6 solution for llamacpp update
- claude-sonnet-4-6 solution for ncnn update: add gemm solution
- add evluation trace for gemm bf16 definition and gemm_fp32_n1000_k2048 definition
Rewrite llama.cpp q8_0 reference-scalar solution signature, now compatible with bench/dataset/raw.py
ArtificialRay
6b3c7a1
Add kernel in different precision:
ArtificialRay
c09d00d
- CV models: FP32 kernels and int8 (w8a8ch) kernels
- LLM models: BF16 kernels and int4 (Q4_K) kernels
Full precision kernels (fp32 and bf16) are ready to benchmark while low-bit kernels have correctness issue that may need a new evaluator
