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ML practitioners increasingly author bespoke kernels, but achieving portable performance demands low-level expertise and repeated manual tuning for each accelerator generation and type. We introduce Helion, a Python-embedded DSL with a “PyTorch with tiles” programming model that preserves familiar PyTorch APIs while giving developers lower-level control over the generated kernels. Helion integrates tightly with TorchInductor to reuse PyTorch operator lowerings, automatically manages host/device boundaries, and provides rich language constructs for tiling, memory movement, and synchronization. The language defines an implicit high-dimensional configuration space that our autotuner explores, shifting the tuning burden from developers to automated search.
In this session, I will cover both the language and what is new since PTC'25, as well as announcing the official GA launch. This session will be open for both experienced and beginner kernel authors.