EVT: Accelerating Deep Learning Training with Epilogue Visitor Tree
Zhaodong Chen, Andrew Kerr, Richard Cai, Jack Kosaian, Haicheng Wu, Yufei Ding, Yuan Xie · 2024
As deep learning models become increasingly complex, the deep learning compilers are critical for enhancing the system efficiency and unlocking hidden optimization opportunities. Although excellent speedups have been achieved in inference workloads, existing compilers face significant limitations in training. Firstly, the training computation graph involves intricate operations challenging to fuse, such as normalization, loss functions, and reductions, which limit optimization opportunities like kernel fusion. Secondly, the training graph's additional edges connecting forward and backward operators pose challenges in finding optimal and feasible partitions for kernel fusion. More importantly, existing compilers cannot either generate kernels with state-of-the-art performance on modern GPUs or accommodate diverse fusion patterns.