KernelBench cuda · H100

Grid + MinGRU SPS Muse Spark 1.3

Isolated regrade 0.3641 on a quiet H100 SXM 2026-09-03 07:47Z (in-run 0.3592). Genuine multi-launch CUDA rollout - cuBLAS (at::linear_out) for the three gate GEMMs, four hand-written kernels for the MinGRU highway, action head, env step and LCG respawn, two lines of MUFU PTX (ex2/rcp.approx) for exp and sigmoid. Env order, RNG derivation and respawn semantics match reference.py; envA/envB are separate launches so there is no warp-visibility bug (the RTX muse cell's defect). Multi-seed GPU probe on the quiet H100 (probe_cuda04.py, 07:50Z): position-exact with logit max-abs 1.6e-6..2.3e-6 on all eight graded shape/env-seed cases at model seed 0 (the seed benchmark.py grades with) and at model seed 123; at model seed 42 positions diverge in 7 of 8 cases (2/4096 at 4096x32, 4/16384 at 16384x32, 18/65536 at 65536x16, 1/8192 at 8192x64, logit max-abs 3.1e-3). The gemini H100 cell shows the same model-seed-42 divergence (5 of 8 cases), so it is a property of that policy draw (argmax margins down to 1.4e-7 per a CPU emulation), not of this kernel. Weight overwrite changes the output (cos(old,new)=-0.737), returned buffers are not aliased. Worth flagging: the agent's own sweep (L3818) recorded the seed-42 position failures at 4096x16 and 8192x64, it then ran its 30-seed sweep only at 128x8 (L3843) and reported bit-for-bit exactness, and its final message describes an atomic-free warp ballot the shipped envA_kernel does not contain (it uses atomicOr). solution.py also flips allow_tf32 process-wide at import, so check.py's reference ran in TF32 too (measured effect 1.3e-6, positions unchanged).

20260903_054919_muse_muse-spark-1.3_04_grid_mingru_sps