Input Compression with Positional Consistency for Efficient Training and Inference of Transformer Neural Networks
This addresses efficiency and generalization issues for users of Transformer models across multiple domains, but it is incremental as it builds on existing data augmentation techniques.
The paper tackles the computational inefficiency and overfitting in Transformers by introducing Input Compression with Positional Consistency (ICPC), a data augmentation method that improves accuracy by up to 1% and accelerates training and inference by up to 2.9X and 2.6X across 9 tasks in 4 modalities.
Transformers have rapidly increased in popularity in recent years, achieving state-of-the-art performance in processing text, images, audio and video. However, Transformers present large computational requirements for both training and inference, and are prone to overfitting during training. To address these challenges, we present Input Compression with Positional Consistency (ICPC), a new data augmentation method that, unlike prior augmentation techniques, simultaneously improves both generalization and training efficiency. ICPC applies varying levels of compression to each training sample in each epoch. This leads to smaller input sequences being processed by the Transformer, and hence faster training, while also alleviating overfitting by presenting each input with different compression levels. We introduce a consistency-aware position selection method in ICPC that enables accurate processing of compressed inputs without any changes to the underlying Transformer architecture. We detail compression-based augmentation methods for four different modalities -- insignificant word pruning for text, resolution modulation for images, spatio-temporal resolution modulation for videos, and spectogram size modulation for audio. ICPC also enables efficient variable-effort inference, where samples are first inferred at high compression levels, and progressively re-evaluated with lower compression for more challenging inputs. On 9 diverse tasks spanning 4 different modalities, ICPC improves accuracy by up to 1%, while also accelerating training and inference by up to 2.9X and 2.6X, respectively. Code is available at https://github.com/amrnag/ICPC.