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XGrad: Boosting Gradient-Based Optimizers With Weight Prediction

Lei Guan, Dongsheng Li, Yanqi Shi, Jian Meng

TL;DR

XGrad tackles slow convergence and suboptimal generalization in gradient-based optimizers by predicting future weights and applying them to both forward and backward passes. It derives a general weight-prediction mechanism that works with SGDM, RMSprop, Adam, AdamW, AdaBelief, and AdaM3, and demonstrates its effectiveness across image classification, NLP, and generative tasks with modest overhead. The framework is validated through extensive experiments showing consistent accuracy and metric improvements over baseline optimizers, while incurring manageable increases in training time and memory. Overall, XGrad provides a practical, extensible approach to boost optimizer performance without altering core optimization dynamics.

Abstract

In this paper, we propose a general deep learning training framework XGrad which introduces weight prediction into the popular gradient-based optimizers to boost their convergence and generalization when training the deep neural network (DNN) models. In particular, ahead of each mini-batch training, the future weights are predicted according to the update rule of the used optimizer and are then applied to both the forward pass and backward propagation. In this way, during the whole training period, the optimizer always utilizes the gradients w.r.t. the future weights to update the DNN parameters, making the gradient-based optimizer achieve better convergence and generalization compared to the original optimizer without weight prediction. XGrad is rather straightforward to implement yet pretty effective in boosting the convergence of gradient-based optimizers and the accuracy of DNN models. Empirical results concerning five popular optimizers including SGD with momentum, Adam, AdamW, AdaBelief, and AdaM3 demonstrate the effectiveness of our proposal. The experimental results validate that XGrad can attain higher model accuracy than the baseline optimizers when training the DNN models. The code of XGrad will be available at: https://github.com/guanleics/XGrad.

XGrad: Boosting Gradient-Based Optimizers With Weight Prediction

TL;DR

XGrad tackles slow convergence and suboptimal generalization in gradient-based optimizers by predicting future weights and applying them to both forward and backward passes. It derives a general weight-prediction mechanism that works with SGDM, RMSprop, Adam, AdamW, AdaBelief, and AdaM3, and demonstrates its effectiveness across image classification, NLP, and generative tasks with modest overhead. The framework is validated through extensive experiments showing consistent accuracy and metric improvements over baseline optimizers, while incurring manageable increases in training time and memory. Overall, XGrad provides a practical, extensible approach to boost optimizer performance without altering core optimization dynamics.

Abstract

In this paper, we propose a general deep learning training framework XGrad which introduces weight prediction into the popular gradient-based optimizers to boost their convergence and generalization when training the deep neural network (DNN) models. In particular, ahead of each mini-batch training, the future weights are predicted according to the update rule of the used optimizer and are then applied to both the forward pass and backward propagation. In this way, during the whole training period, the optimizer always utilizes the gradients w.r.t. the future weights to update the DNN parameters, making the gradient-based optimizer achieve better convergence and generalization compared to the original optimizer without weight prediction. XGrad is rather straightforward to implement yet pretty effective in boosting the convergence of gradient-based optimizers and the accuracy of DNN models. Empirical results concerning five popular optimizers including SGD with momentum, Adam, AdamW, AdaBelief, and AdaM3 demonstrate the effectiveness of our proposal. The experimental results validate that XGrad can attain higher model accuracy than the baseline optimizers when training the DNN models. The code of XGrad will be available at: https://github.com/guanleics/XGrad.
Paper Structure (30 sections, 33 equations, 8 figures, 11 tables, 1 algorithm)

This paper contains 30 sections, 33 equations, 8 figures, 11 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Gradient-based Optimizers
  3. SGD with momentum
  4. RMSprop
  5. Adam
  6. AdamW
  7. AdaBelief
  8. AdaM3
  9. The XGrad framework
  10. Derivation of XGrad
  11. Workflow of XGrad
  12. Analysis of XGrad
  13. Experiments
  14. Experimental Settings
  15. Environments
  16. ...and 15 more sections

Figures (8)

  • Figure 1: The optimization of DNN weights.
  • Figure 2: Validation Top-1 accuracy (higher is better) vs. epochs when training CNNs and ViT on CIFAR-10.
  • Figure 3: Validation Top-1 accuracy (higher is better) vs. epochs of training CNNs and ViT on CIFAR-100.
  • Figure 4: Comparison of Adam and XGrad. Figures \ref{['comp-adam-lenet-acc']}, \ref{['comp-adam-resnet34-acc']}, \ref{['comp-adam-densenet-acc']}, and \ref{['comp-adam-inceptionv3-acc']}: Top-1 accuracy (higher is better) vs. Epochs; Figures \ref{['comp-adam-lstm1']}, \ref{['comp-adam-lstm2']}, and \ref{['comp-adam-lstm3']}: Perplexity (lower is better) vs. Epochs; Figure \ref{['comp-adam-gnmt8']}: BLEU score (higher is better) vs. Epochs; Figure \ref{['comp-adam-vae']}: Total loss (lower is better) vs. Epochs.
  • Figure 5: Comparison of AdamW and XGrad. Figures \ref{['comp-adamw-lenet-acc']}, \ref{['comp-adamw-resnet34-acc']}, \ref{['comp-adamw-densenet-acc']}, and \ref{['comp-adamw-inceptionv3-acc']}: Top-1 accuracy (higher is better) vs. Epochs; Figures \ref{['comp-adamw-lstm1']}, \ref{['comp-adamw-lstm2']}, and \ref{['comp-adamw-lstm3']}: Perplexity (lower is better) vs. Epochs; Figure \ref{['comp-adamw-gnmt8']}: BLEU score (higher is better) vs. epochs; Figure \ref{['comp-adamw-vae']}: Total loss (lower is better) vs. Epochs.
  • ...and 3 more figures