Adds baseline-sme2 for loop_101 and loop_128, and a sme2-kernel:za / sme2-kernel:streaming-sve tag on the 32 simd-loop definitions that have a baseline-sme2 (tags only; nothing else in those files changes). Exact paths only. See ArtificialRay/CPU-Kernel-Baseline#107 (issue #95).
- Delete all gemm kernel in ncnn as they are not using gemm but gemv, which hurts the performance of large batch size
ArtificialRay
650aa4b
- Update kleidiai's gemm to use both GEMV and GEMM: for small batch size (less than 4 or 8) use GEMV for large batch size use GEMM
Kleidiai kernel changes:
ArtificialRay
d7a6eaa
- workload for 7 kernels change, keep only 2-3 decode record for the kernel, all other has batch size of at least 256
- baseline binding change:
- baseline-kleidiai-arm/conv2d/conv2d_fp32_nhwc_kh3_kw3_s1_p1_c32_c64: add N as a var input
- baseline-kleidiai-arm/gemm/gemm_bf16_n512_k512: pack A at LHS every time, remove memcmp for A
- baseline-kleidiai-arm/gemm/gemm_qs4c32_n256_k512: dynamic quant A + pack A every time, remove memcmp for A
- baseline-kleidiai-arm/gemm/gemm_qs4cx_sme2_n256_k512: same as baseline-kleidiai-arm/gemm/gemm_qs4c32_n256_k512
- baseline-kleidiai-arm/conv2d/conv2d_f16_nhwc_kh3_kw3_valid_c32_c64: use NEON to convert f16 to f32, do LHS pack every time
baseline-sve (30): Arm's plain-SVE kernels selected by the preprocessor from the upstream simd-loops sources (asm fallbacks where the ACLE branch needs SVE2), -march=armv8.2-a+sve pinned, edge-size padding in the binding for 038 109 110 112 113 114 124 219. All 30 built and evaluated on a c7g.large against the Python reference; 29 pass (032's baseline is numerically off and is kept for continuity).
baseline-sve2 (36, new author): the previous baseline-sve files (validated on Graviton4, 2026-07-23) renamed with -march=native restored; 9 of them (038 102 103 104 105 109 120 123 124) regenerated because the old copies did not compile. Cross-compiled for armv9-a+sve2, not yet run on a c8g.
definitions: loop_126/127 mask the uint32 reference to 32 bits (numpy 2 raised on overflow); loop_114 wraps int32/int16 like the C reference (raised at n=4096).
No deletions. Generated by scripts/gen_simd_loop_harness.py on CPU-Kernel-Baseline feat/box-watchdog.
kieldAI kernels with sve baseline are enabled
ArtificialRay
49fa199
init kieldAI kernels
ArtificialRay
7724b7a
add hard-label to isa_features for every simd-loop solutions
ArtificialRay
1a19158
Attention kernel update:
ArtificialRay
ad52de0
- 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
Tag q4_k_m defs correctness:sqnr + stable SiLU (moe)
Update all w8a8ch definitions' correctness reference, aligns with what llama.cpp low-bit kernel correctness reference style: dequant input, then calculate in full precision
ArtificialRay
009b323
Stable SiLU in moe references (clip before exp; prevents q8_0 overflow-driven inf)
- 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
Definitions, solutions, and evaluation trace updates for ncnn kernels
ArtificialRay
07f5581
- update ncnn-arm baseline and reference-scalar solution and claude-sonnet-4-6 agent eval solution trace
- update evaluation traces for conv2d, conv2d depthwise, gemm, pooling
- update low-bit kernel definition for conv2d, conv2d_depthwise, gemm
add baseline solution source at all neural network kernel definitions
ArtificialRay
c211fb1
Update definition and workload for CV model kernels:
ArtificialRay
7e7beda
- only conv2d and conv2d depthwise are kept, remove others
- conv2d and conv2d depthwise have fp32 and int8 percision kernels
- add new maxpool2d definitions
- add workloads for new conv2d, conv2d depthwise definitions, pooling, lstm definitions.
Resolve conflict and recover nn kernel traces
ArtificialRay
a7c0f7e
local snapshot before syncing with HF
ArtificialRay
7502e01
Replace pre-PR#15 conv2d-only trace with current simd-loop set (definitions + workloads + reference/autovec solutions)
