Adds varlen Flash-DM attention (CUDA) + autograd

Introduces CUDA custom ops and fake/meta registrations for variable-length attention forward/backward using cumulative sequence lengths, enabling efficient packed ragged batches.

Adds an autograd wrapper and public API exposing varlen attention with support for MQA/GQA, optional mask/bias, causal mode, softcap, deterministic backward, and optional attention probs/LSE for testing.

Pads head dim and key seqlen to multiples of 8 for 16‑bit–friendly allocations, rounds workspace shapes, and sanitizes outputs; also supports paged KV via a block table.

Improves performance and memory by avoiding per-sequence padding and aligning allocations to hardware-friendly sizes.

Author: algo-home