Block-sparse attention on a systolic array requires rethinking the data structure before touching the kernel. trnsparse v0.6.0 ships forward and backward NKI attention kernels, K-tiling for head_dim > 128, and — after a week fighting NKI 0.3.0's changed API — a simulator CI gate that actually tests the kernels rather than silently substituting PyTorch.
trnsparse v0.4.2 added a block-sparse attention primitive without writing a new kernel. The mechanism is straightforward in hindsight — BSRMatrix already stores a matrix as 128×128 blocks plus a block-level sparsity pattern, and the post-softmax attention weight matrix with a local-window mask is exactly that structure — but the implication took a moment to land: trnsparse, built as a quantum-chemistry cuSPARSE replacement, already spoke transformer.
trnsparse shipped its first hardware-validated NKI SpMM kernel in v0.2.0, and the benchmark table was publicly worse than scipy across every configuration. That's not a failure — it's the evidence that led to the reframe v0.3.0 ships on: Trainium's sparse primitive isn't CSR, it's the 128×128 Tensor Engine tile. The CUDA sparse playbook is the wrong starting point for this hardware.