Bolt: Bridging the Gap between Auto-tuners and Hardware-native Performance

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. Example auto-tuners like AutoTVM (Chen Ansor (Zheng et al., 2020a) only achieves 20% of cuBLAS et al., 2018b) and Ansor (Zheng et al., 2020a) infer hardware performance for FP16 GEMMs on NVIDIA Tesla T4 GPUs cost models from afar, by executing sample implementations (see Figure 1 for more details). Building on the inferred cost models, auto-tuners take tensor Related, opaque device models also lead to a prolonged programs as inputs, and navigates a large search space to auto-tuning time, as the search process is less informed by select effective transformations for high performance.