mL-BFGS: A Momentum-based L-BFGS for Distributed Large-scale Neural Network Optimization
Abstract: Quasi-Newton methods still face significant challenges in training large-scale neural networks due to additional compute costs in the Hessian related computations and instability issues in stochastic training.
A well-known method, L-BFGS that efficiently approximates the Hessian using history parameter and gradient changes, suffers convergence instability in stochastic training.
So far, attempts that adapt L-BFGS to large-scale stochastic training incur considerable extra overhead, which offsets its convergence benefits in wall-clock time.
In this paper, we propose mL-BFGS, a lightweight momentum-based L-BFGS algorithm that paves the way for quasi-Newton (QN) methods in large-scale distributed deep neural network (DNN) optimization.
mL-BFGS introduces a nearly cost-free momentum scheme into L-BFGS update and greatly reduces stochastic noise in the Hessian, therefore stabilizing convergence during stochastic optimization.
For model training at a large scale, mL-BFGS approximates a block-wise Hessian, thus enabling distributing compute and memory costs across all computing nodes.
We provide a supporting convergence analysis for mL-BFGS in stochastic settings.
To investigate mL-BFGS's potential in large-scale DNN training, we train benchmark neural models using mL-BFGS and compare performance with baselines (SGD, Adam, and other quasi-Newton methods).
Results show that mL-BFGS achieves both noticeable iteration-wise and wall-clock speedup.
Submission Length: Regular submission (no more than 12 pages of main content)
Assigned Action Editor: ~Murat_A_Erdogdu1
License: Creative Commons Attribution 4.0 International (CC BY 4.0)
Submission Number: 967
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