  Today's auto-tuners (e.g., AutoTVM, Ansor) generate efficient tensor programs
by navigating a large search space to identify effective implementations, but
they do so with opaque hardware details. Thus, their performance could fall
behind that of hardware-native libraries (e.g., cuBLAS, cuDNN), which are
hand-optimized by device vendors to extract high performance. On the other
hand, these vendor libraries have a fixed set of supported functions and lack
the customization and automation support afforded by auto-tuners. Bolt is based
on the recent trend that vendor libraries are increasingly modularized and
reconfigurable via declarative control (e.g., CUTLASS). It enables a novel
approach that bridges this gap and achieves the best of both worlds, via
hardware-native templated search. Bolt provides new opportunities to rethink
end-to-end tensor optimizations at the graph, operator, and model levels. Bolt
demonstrates this concept by prototyping on a popular auto-tuner in TVM and a
class of widely-used platforms (i.e., NVIDIA GPUs) -- both in large deployment
in our production environment. Bolt improves the inference speed of common
convolutional neural networks by 2.5x on average over the state of the art, and
it auto-tunes these models within 20 minutes.
