The settings rows were verdicted one at a time against int8 qk alone, so
the table could assemble a tuple no row ever measured and contradict its
own stars: pv lost a solo 10 percent margin test while the starred best
config carried pv at four of nine shapes, and the emitted smooth-only
stack was strictly dominated by measured rows at three shapes.
- select the recommendation as the best measured quantized config by the
star rule (lowest error within 5 percent of the fastest, error cap vs
int8), then gate that one config against unquantized at a z adjusted
for having selected the best of the pool
- decompose the winner into the settings rows; each reason cites the
measured sibling that isolates its setting, replacing the blanket no
gain wording that hid an 8 percent measured win
- drop the additive stack estimate, composition check and float8 rescue,
which the joint selection covers by construction on measured rows
- name each shape's selected config in the per-shape verdict note and
compute sidak z exactly for any candidate count
Hoisting the function-local imports to module scope wrote
`import importlib.import_module`, which cannot resolve because
import_module is a function in importlib, not a submodule, so the
benchmark failed at startup with ModuleNotFoundError.
- import importlib, which is what the module-scope call sites need
The toggle changes no output, so the verdict is a symmetric
faster/slower test on the layer forward it gates, voted across every
measured dequant shape; the standalone kernel ratio stays in the notes.
Every dequant shape and block geometry comes from a real transformer config,
one source of truth per model.
- flux.1: attention linears 3072x3072, feed-forward 12288x3072, block at
3072 wide, 24 heads, 12288 ff, 4608 joint tokens
- krea 2: standalone wq 6144x6144, swiglu gate 16384x6144, block at 6144
wide, 48 heads, 4608 joint tokens
- the block section measures every geometry in block_geometries; buyback
costs are judged at the block matching the reference shape, with the
geometry named in the reason
- te shape lookup keys on its label instead of a list index
Krea 2 runs joint attention over one text plus image stream with a segment
mask: text is padded to a fixed 512 tokens and the padded tail is masked for
queries and keys both, so fully masked query rows yield nan under sdpa. The
preset carries the transformer's exact (B, 1, L, L) mask, nan-guards the
error path the way the model does, and uses the 48 kernel-level heads left
after gqa expansion at 4608 joint tokens.
- fix the drift fields crashing when save_report rebuilt the run info
Vladimir Mandic
CalamitousFelicitousness
Disty0