Fifth machine. Two additions:
- M3 Air (10c) vs M5 (10c): same GPU width, 2 generations apart -> M5 is 7.7x
at seq 4096. Isolates architecture from width (the Ultras can't). head_dim-56
fallback reproduces here too (padding = 2.6x), so it now holds on every
Apple GPU generation shipping today.
- Fanless sustained load: NO meaningful throttle over 5 min (1.01x at 300s).
We predicted a decay curve and found none — reporting the negative, since it
validates that the burst numbers aren't short-run artifacts.
M5 MacBook Pro (10-core GPU, 16GB) reproduces the head_dim=56 silent fallback
and shows the fix matters far MORE on narrow GPUs: padding 56->64 gives 4.9x
(1,16,4096), 5.1x (1,16,2048), 3.2x (4096,2,64). Raw fused-vs-unfused at
4096/fp32 is 0.14x on M5 vs ~0.53x on M3 Ultra — an M5 laptop matches an M1
Ultra on fused attention but is ~4x slower unfused. This reframes the fix as a
laptop optimization, not a big-iron one.
Shape-spy on a real 2048 forward + exact-shape sweeps on M1/M3 Ultra:
fused==unfused at hdim 56 (silent fallback) on both machines, so use_sdpa
buys nothing and its transpose choreography costs ~4.5x on M1. Padding to
hdim 64 engages the fast kernel: 2.2x (M3) / 2.7x (M1) on the dominant
global-attention shape despite 14% extra FLOPs.
Ran scripts/bench_optimizations.py unmodified across three machines + an
engine-level tiled×compile extension with ground-truth alpha scoring.
Headlines: sdpa = 4.5x regression on M1 at 2048 (bf16 innocent — isolated
per-toggle on both Ultras); stage_gc harmful on Ultra-class; tiled 768/64
+ compile = best 2048 config on both Ultras; 24GB Macs swap-bound at 2048
full-frame (tiled required).
