Is Integer Arithmetic Enough for Deep Learning Training?

The ever-increasing computational complexity of deep learning models makes their training and deployment difficult on various cloud and edge platforms. Replacing floating-point arithmetic with low-bit integer arithmetic is a promising approach to save energy, memory footprint, and latency of deep learning models. As such, quantization has attracted the attention of researchers in recent years. However, using integer numbers to form a fully functional integer training pipeline including forward pass, back-propagation, and stochastic gradient descent is not studied in detail. Our empirical and mathematical results reveal that integer arithmetic is enough to train deep learning models. Unlike recent proposals, instead of quantization, we directly switch the number representation of computations. Our novel training method forms a fully integer training pipeline that does not change the trajectory of the loss and accuracy compared to floating-point, nor does it need any special hyper-parameter tuning, distribution adjustment, or gradient clipping. Our experimental results show that our proposed method is effective in a wide variety of tasks such as classification (including vision transformers), object detection, and semantic segmentation

Efficient Fine-Tuning of Compressed Language Models with Learners

Fine-tuning BERT-based models is resource-intensive in memory, computation, and time. While many prior works aim to improve inference efficiency via compression techniques, e.g., pruning, these works do not explicitly address the computational challenges of training to downstream tasks. We introduce Learner modules and priming, novel methods for fine-tuning that exploit the overparameterization of pre-trained language models to gain benefits in convergence speed and resource utilization. Learner modules navigate the double bind of 1) training efficiently by fine-tuning a subset of parameters, and 2) training effectively by ensuring quick convergence and high metric scores. Our results on DistilBERT demonstrate that learners perform on par with or surpass the baselines. Learners train 7x fewer parameters than state-of-the-art methods on GLUE. On CoLA, learners fine-tune 20% faster, and have significantly lower resource utilization.Comment: 8 pages, 9 figures, 2 tables, presented at ICML 2022 workshop on Hardware-Aware Efficient Training (HAET 2022

Integer Fine-tuning of Transformer-based Models

Transformer based models are used to achieve state-of-the-art performance on various deep learning tasks. Since transformer-based models have large numbers of parameters, fine-tuning them on downstream tasks is computationally intensive and energy hungry. Automatic mixed-precision FP32/FP16 fine-tuning of such models has been previously used to lower the compute resource requirements. However, with the recent advances in the low-bit integer back-propagation, it is possible to further reduce the computation and memory foot-print. In this work, we explore a novel integer training method that uses integer arithmetic for both forward propagation and gradient computation of linear, convolutional, layer-norm, and embedding layers in transformer-based models. Furthermore, we study the effect of various integer bit-widths to find the minimum required bit-width for integer fine-tuning of transformer-based models. We fine-tune BERT and ViT models on popular downstream tasks using integer layers. We show that 16-bit integer models match the floating-point baseline performance. Reducing the bit-width to 10, we observe 0.5 average score drop. Finally, further reduction of the bit-width to 8 provides an average score drop of 1.7 points