[Reproducibility Report] Rigging the Lottery: Making All Tickets Winners
This is an incremental reproducibility study that addresses the need for efficient sparse training methods in machine learning, benefiting researchers and practitioners by verifying and extending prior claims.
The paper tackles the problem of training sparse neural networks efficiently by reproducing and validating the RigL algorithm, showing it matches or exceeds existing methods like pruning on CIFAR-10/100 within 0.1% accuracy, with performance improvements under fixed parameter or FLOP constraints.
$\textit{RigL}$, a sparse training algorithm, claims to directly train sparse networks that match or exceed the performance of existing dense-to-sparse training techniques (such as pruning) for a fixed parameter count and compute budget. We implement $\textit{RigL}$ from scratch in Pytorch and reproduce its performance on CIFAR-10 within 0.1% of the reported value. On both CIFAR-10/100, the central claim holds -- given a fixed training budget, $\textit{RigL}$ surpasses existing dynamic-sparse training methods over a range of target sparsities. By training longer, the performance can match or exceed iterative pruning, while consuming constant FLOPs throughout training. We also show that there is little benefit in tuning $\textit{RigL}$'s hyper-parameters for every sparsity, initialization pair -- the reference choice of hyperparameters is often close to optimal performance. Going beyond the original paper, we find that the optimal initialization scheme depends on the training constraint. While the Erdos-Renyi-Kernel distribution outperforms the Uniform distribution for a fixed parameter count, for a fixed FLOP count, the latter performs better. Finally, redistributing layer-wise sparsity while training can bridge the performance gap between the two initialization schemes, but increases computational cost.