Skip to yearly menu bar Skip to main content

In-Person Poster presentation / poster accept

Distributed Extra-gradient with Optimal Complexity and Communication Guarantees

Ali Ramezani-Kebrya · Kimon Antonakopoulos · Igor Krawczuk · Justin Deschenaux · Volkan Cevher

MH1-2-3-4 #84

Keywords: [ General Machine Learning ] [ extra-gradient ] [ Unbiased Quantization ] [ Adaptive Sep-size ] [ variational inequality ]

Abstract: We consider monotone variational inequality (VI) problems in multi-GPU settings where multiple processors/workers/clients have access to local stochastic dual vectors. This setting includes a broad range of important problems from distributed convex minimization to min-max and games. Extra-gradient, which is a de facto algorithm for monotone VI problems, has not been designed to be communication-efficient. To this end, we propose a quantized generalized extra-gradient (Q-GenX), which is an unbiased and adaptive compression method tailored to solve VIs. We provide an adaptive step-size rule, which adapts to the respective noise profiles at hand and achieve a fast rate of ${\cal O}(1/T)$ under relative noise, and an order-optimal ${\cal O}(1/\sqrt{T})$ under absolute noise and show distributed training accelerates convergence. Finally, we validate our theoretical results by providing real-world experiments and training generative adversarial networks on multiple GPUs.

Chat is not available.