v0.4.4: Perf roundup — one-shot norm check, shape-stable compiles, autotuned backwards

Fixed

• maxsim(normalize=False) ran a .item() norm-check device sync on every
  call; it now runs once per process, restoring CUDA-graph capture on the
  PyLate / colpali-engine hot path.

Changed

• PLAID centroid codes are handled as int32 end to end (any integer dtype is
  still accepted; out-of-range codes in plaid_approx_score clamp to
  centroid 0).
• Batch sizes (Nq, Nd) are runtime kernel arguments, not constexpr — no
  more recompiles per batch shape under dynamic batching.
• GPU family detection is keyed on compute capability, not the device name;
  Blackwell now gets first-class autotune configs.
• The varlen, packed-pairs and residual backward launches are autotuned like
  the dense backwards (up to 1.42× on H100 at training shapes).
• Backward gradient buffers that the kernels overwrite in full use
  torch.empty instead of torch.zeros (atomic-scatter buffers stay zeroed).
• The fp8 autotune key no longer splits on mask presence.

Full rationale and H100 measurements in #111.