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Edouard Oyallon

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A²CiD²: Accelerating Asynchronous Communication in Decentralized Deep Learning
Adel Nabli
Eugene Belilovsky
Edouard Oyallon
Distributed training of Deep Learning models has been critical to many recent successes in the field. Current standard methods primarily rel… (voir plus)y on synchronous centralized algorithms which induce major communication bottlenecks and synchronization locks at scale. Decentralized asynchronous algorithms are emerging as a potential alternative but their practical applicability still lags. In order to mitigate the increase in communication cost that naturally comes with scaling the number of workers, we introduce a principled asynchronous, randomized, gossip-based optimization algorithm which works thanks to a continuous local momentum named
2022-12-31
NeurIPS (publié)
doi.org
openreview.net
Can Forward Gradient Match Backpropagation?
Louis Fournier
Stéphane Rivaud
Eugene Belilovsky
Michael Eickenberg
Edouard Oyallon
Forward Gradients - the idea of using directional derivatives in forward differentiation mode - have recently been shown to be utilizable fo… (voir plus)r neural network training while avoiding problems generally associated with backpropagation gradient computation, such as locking and memorization requirements. The cost is the requirement to guess the step direction, which is hard in high dimensions. While current solutions rely on weighted averages over isotropic guess vector distributions, we propose to strongly bias our gradient guesses in directions that are much more promising, such as feedback obtained from small, local auxiliary networks. For a standard computer vision neural network, we conduct a rigorous study systematically covering a variety of combinations of gradient targets and gradient guesses, including those previously presented in the literature. We find that using gradients obtained from a local loss as a candidate direction drastically improves on random noise in Forward Gradient methods.
2022-12-31
ICML (publié)
doi.org
proceedings.mlr.press