Loader-level attention option: mean-pool Q and a smoothed K into
blocks, score them with one small matmul, attend only the top
1-sparsity fraction of key blocks per query block, via a vendored
Triton kernel. Runs alongside the chosen attention backend rather than
replacing it (one override slot; the backend becomes SLA's dense
fall-through for the text refiner, masked calls, and trailing dense
steps), and refuses cleanly -- naming why -- on anything that is not
MiniMax H3 or a machine without CUDA+Triton.
modules/loaders/sla_block_map.py and sla_kernel.py are near-verbatim
ports of v4's vendored files (no FunPack-specific imports, so nothing
to adapt); sla_attention.py is the loader-facing glue (H3 gate,
override composition, per-step wrapper), same content as v4's with a
"not a module" note added to match gguf_support.py's convention.
Wired into FunPackDiffusionModelLoader as six new optional widgets,
following v4's loaders.py wiring but expressed in v5's io.Schema style.
The Triton kernel itself is unexercisable on this Mac (no CUDA) --
matches how real generation is verified in this project (rented GPU,
not local). Ported the CPU-testable half of v4's test suite (the
override contract, the H3 gate, the per-step wrapper); the three
kernel-correctness tests carry the same CUDA+Triton skip guard v4 used.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
