Fast Autoscheduling for Sparse ML Frameworks (opens in new tab)

The rapid growth in the size of deep learning models strains the capabilities of dense computation paradigms. Leveraging sparse computation has become increasingly popular for training and deploying large-scale models, but existing deep learning frameworks lack extensive support for sparse operations. However, existing approaches either require manual scheduling expertise or rely on exhaustive search taking hours to days, which are both incompatible with the interactive development essential ...