The score-space join removed the concat as intended and is numerically
identical (140.3 dB), but it cannot be quantized: replacing torch.softmax
with an explicit max/exp/sum/div made QNN's context-binary converter exit 14
under w4a16, while every fp16 build compiled. Two earlier shapes failed the
same way -- a broadcast multiply plus ReduceSum for the new token's score,
and a two-axis broadcast multiply for its value contribution.
The device profile says the win was never the concat anyway. Of a sliding
layer, Output was 9.9%, its quantise conversion 3.1% and the window Slice
7.4% -- all of it the cost of handing a 512-entry window back across the
graph boundary once per token. So keep roll's attention exactly as it was,
one fused Softmax over concatenated keys, and change only what the layer
returns: the single new K/V entry, which the runtime drops into the ring
slot. Ring order needs no fixing up, because softmax over keys is
order-independent and each cached key already carries its own rotary phase.
Measured on a real Galaxy S25, sliding layer, ms:
w4a16 0.917 -> 0.838 (-8.6%) <- now compiles
fp16 1.689 -> 1.486 (-12.0%)
Full-attention layers keep roll: ring gains them nothing (1.567 vs 1.559 at
fp16, the concat saved being spent on the extra score matmul), so the
default layout is now per layer type.
Parity against the reference is 140.3 dB for the mixed default with
identical top-1/top-5.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01L1ax5wYLA6XbqEL1i8L9gZ
