slat_flow joins ss_flow: max abs diff 9.3e-6, 700/700 params, against upstream running
on CPU torch. All three SLAT checkpoints load clean and run (img2shape 512/1024 and
imgshape2tex, the last taking 64 in-channels since shape is concatenated).
The SLAT flows differ from ss_flow in two ways, both handled in the shared core:
tokens are a SparseTensor's voxels so attention runs per batch item, and RoPE phases
are NOT shipped - positions are the input's own coordinates, so they are derived at
call time by rope_phases_from_coords.
tests/oracle_slat.py keeps the CPU-torch oracle harness: it patches both the dense and
sparse flash-attn kernels with SDPA equivalents. Use it rather than reasoning about
correctness - it has already caught three bugs a perfect 700/700 key match did not.
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