v0.0.1: initial release

Initial release for late-interaction-kernels: fused Triton kernels for late-interaction (MaxSim) scoring, with a high-level PyTorch API and PyLate drop-in 🚀

Added

• Core MaxSim kernels — maxsim (autograd-aware) and maxsim_inference
  with fused L2-normalize, mask handling, and a unified / csr / atomic
  backward selector (set_backward_method, default auto).
• Ragged / packed batches — maxsim_varlen over cu_seqlens-indexed
  flat buffers, autograd-aware on both Q and D.
• Pair-list scoring — maxsim_inference_scatter scores arbitrary
  (query_index, doc_index) pairs from packed batches and returns
  [num_pairs] directly (vLLM-style reranker scheduling).
• Fused D-side head — maxsim_from_hidden (inference) and
  maxsim_from_hidden_train (closed-form backward) apply
  projection + L2-normalize + MaxSim in a single pass over raw
  [Nd, Ld, d_model] hidden states.
• PLAID / ColBERTv2 — plaid_approx_score (approximate scoring) and
  maxsim_residual / maxsim_residual_varlen (exact rerank with on-the-fly
  2/4/8-bit residual decompression + L2-normalize + MaxSim, forward-only on
  varlen).
• FP8 inference — maxsim_inference_fp8 with per-tensor / per-token
  e4m3 inputs, fp32 accumulator, and a score-tie fallback harness.
• High-level API — MaxSimScorer(nn.Module) and retrieve(Q, D, top_k),
  both with transparent pure-PyTorch CPU fallback so training and retrieval
  code is unit-testable on macOS / Windows / CPU-only CI.
• PyLate drop-in — patch_pylate / unpatch_pylate patch
  colbert_scores and colbert_kd_scores across Contrastive,
  CachedContrastive, and Distillation. LIK_DISABLE=1 is the
  process-wide kill switch.
• Experimental kernels — late_interaction_kernels.experimental ships
  soft_maxsim, smooth_maxsim, maxsim_xtr, and maxsim_matryoshka.
• FP8 helpers — late_interaction_kernels.fp8 exposes per-tensor /
  per-token quantize / dequantize utilities.
• Per-GPU autotune (Hopper / Ampere / Ada / generic) with shared-memory
  pruning; warp specialization on Triton ≥ 3.2 with transparent fallback.
• Pure-PyTorch reference (late_interaction_kernels.reference) used as
  ground truth in tests and as the CPU fallback path.
• Test suite covering forward / backward parity, varlen, soft/smooth,
  edge cases, PyLate compatibility, CPU fallback, and gradcheck on the
  high-level API.
• Benchmarks for every kernel, plus end-to-end PyLate / LateOn training
  and retrieval scripts under benchmarks/ and scripts/.