March 2026 (2026-03) monthly summary for pytorch-labs/tritonbench: Delivered key improvements to the attention module with paged kernels and enhanced numerical stability. Implemented paged attention kernels to support variable-length sequences and added return_lse parameter to stabilize attention computations. Changes delivered via two commits (0c3ea3bc4f7bbd324355fdba37dbf87a150737b4; a4315792b0e2c1c7bab66e9c2a19a933987c388f) with differential revisions D95139767 and D96188376 and merged PRs 919 and 945. Result: increased flexibility, efficiency, and accuracy of TritonBench attention, enabling broader experimentation and more reliable benchmarks.

February 2026: Delivered a paged attention mechanism to optimize memory usage for large-sequence models in meta-pytorch/tritonbench, enabling larger contexts and improved throughput. Established benchmarking for the new feature to quantify memory and performance gains and prepared the related pull request (PR #859, different Revision D92727032). No major bugs fixed this month; minor issues were addressed during code review. Overall impact: improved scalability, memory efficiency, and maintainability for attention computations in long-sequence models.