Understanding LazyTensor System Performance with PyTorch/XLA on Cloud TPU

Ease of use, expressivity, and debuggability are among the core principles of PyTorch. One of the key drivers for the ease of use is that PyTorch execution is by default "eager, i.e. op by op execution preserves the imperative nature of the program. However, eager execution does not offer the compiler based optimization, for example, the optimizations when the computation can be expressed as a graph. LazyTensor [1], first introduced with PyTorch/XLA, helps combine these seemingly disparate approaches. While PyTorch eager execution is widely used, intuitive, and well understood, lazy execution is not as prevalent yet.