CI keeps the runtime path dependency-free, so the Python test job runs
without numpy and the new codec tests errored on import. Guard the import
and skip the class instead — verified both ways: 6/6 run where numpy is
present, 6/6 skip cleanly where it is not.
#453 settled the scheme; this produces the actual bytes. iq3_pack.encode/decode
implement the container layout so the converter, the engine and the decode
kernels can all be written against one spec:
98 bytes per 256 weights = 3.0625 bpw
[ 0..63] uint8 grid index per 4-dim magnitude block
[64..95] uint32 x8 — four 7-bit sign words + 4-bit sub-scale per 32
[96..97] fp16 super-scale
Signs use the published odd-parity trick: 7 of every 8 are stored and the 8th
is derived, so the encoder flips the smallest-magnitude weight of any block
whose true signs would violate parity — the cost the #453 ablation priced in,
now actually paid. Sub-scale is searched over all 16 codes against the stored
(fp16-rounded) super-scale, so encode-time and decode-time arithmetic agree.
GLM-5.2 routed experts under this container: 372.7 -> 281.2 GB (-24.6%), and a
176 GB VRAM tier holds ~12,180 experts instead of ~9,190 (+33%).
tests/test_iq3_pack.py: byte budget, deterministic encode, decode checked
value-by-value against an independent loop-based reader written straight from
the layout, sign-parity closure, and reconstruction quality in the band the
chosen scheme measured (rel-RMSE 0.195 vs the torch model's 0.195).
ZacharyZcR