Huge embedding tables in modern Deep Learning Recommender Models (DLRM) require prohibitively large memory during training and inference. Aiming to reduce the memory footprint of training, this paper proposes FIne-grained In-Training Embedding Dimension optimization (FIITED). Given the observation that embedding vectors are not equally important, FIITED adjusts the dimension of each individual embedding vector continuously during training, assigning longer dimensions to more important embeddings while adapting to dynamic changes in data. A novel embedding storage system based on virtually-hashed physically-indexed hash tables is designed to efficiently implement the embedding dimension adjustment and effectively enable memory saving. Experiments on two industry models show that FIITED is able to reduce the size of embeddings by more than 65% while maintaining the trained model's quality, saving significantly more memory than a state-of-the-art in-training embedding pruning method. On public click-through rate prediction datasets, FIITED is able to prune up to 93.75%-99.75% embeddings without significant accuracy loss.

翻译：现代深度学习推荐模型中的庞大嵌入表在训练和推理过程中需要极大的内存。本文提出了一种细粒度的训练内嵌入维度优化方法（FIITED），旨在减少训练的内存占用。基于嵌入向量重要性不均的观察，FIITED在训练过程中持续调整每个嵌入向量的维度，为更重要的嵌入分配更长的维度，同时适应数据动态变化。我们设计了一种基于虚拟哈希物理索引哈希表的新型嵌入存储系统，以高效实现嵌入维度调整并有效节省内存。在两个工业模型上的实验表明，FIITED能够在保持训练模型质量的前提下减少超过65%的嵌入大小，相比最先进的训练内嵌入剪枝方法节省更多内存。在公开的点击率预测数据集上，FIITED能够剪枝高达93.75%-99.75%的嵌入而不会显著损失精度。