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TorchOpt: An Efficient library for Differentiable Optimization
Jie Ren · Xidong Feng · Bo Liu · Xuehai Pan · Yao Fu · Luo Mai · Yaodong Yang

@
in OPT 2022: Optimization for Machine Learning »
Event URL: https://openreview.net/forum?id=skhQB3ALAP »
Recent years have witnessed the booming of various differentiable optimization algorithms. These algorithms exhibit different execution patterns, and their execution needs massive computational resources that go beyond a single CPU and GPU. Existing differentiable optimization libraries, however, cannot support efficient algorithm development and multi-CPU/GPU execution, making the development of differentiable optimization algorithms often cumbersome and expensive.This paper introduces TorchOpt, a PyTorch-based efficient library for differentiable optimization. TorchOpt provides a unified and expressive bi-level optimization programming abstraction. This abstraction allows users to efficiently declare and analyze various differentiable optimization programs with explicit gradients, implicit gradients, and zero-order gradients. TorchOpt further provides a high-performance distributed execution runtime. This runtime can fully parallelize computation-intensive differentiation operations (e.g. tensor tree flatten) on CPUs/GPUs and automatically distribute computation to distributed devices. Experimental results show that TorchOpt outperforms state-of-the-art libraries by $7\times$ on an 8-GPU server. TorchOpt will be open source with a permissive Apache-2.0 License.

Author Information

Jie Ren (University of Edinburgh, University of Edinburgh)
Xidong Feng (University College London)
Bo Liu (Peking University)
Xuehai Pan (Peking University)
Yao Fu (University of Edinburgh)
Luo Mai (University of Edinburgh, University of Edinburgh)
Yaodong Yang (AIG)

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