TorchOpt: An Efficient Library for Differentiable Optimization
Jie Ren*, Xidong Feng*, Bo Liu*, Xuehai Pan*, Yao Fu, Luo Mai, Yaodong Yang.
Year: 2023, Volume: 24, Issue: 367, Pages: 1−14
Abstract
Differentiable optimization algorithms often involve expensive computations of various meta-gradients. To address this, we design and implement TorchOpt, a new PyTorch-based differentiable optimization library. TorchOpt provides an expressive and unified programming interface that simplifies the implementation of explicit, implicit, and zero-order gradients. Moreover, TorchOpt has a distributed execution runtime capable of parallelizing diverse operations linked to differentiable optimization tasks across CPU and GPU devices. Experimental results demonstrate that TorchOpt achieves a 5.2× training time speedup in a cluster. TorchOpt is open-sourced at https://github.com/metaopt/torchopt and has become a PyTorch Ecosystem project.