  Embedding tables are usually huge in click-through rate (CTR) prediction
models. To train and deploy the CTR models efficiently and economically, it is
necessary to compress their embedding tables at the training stage. To this
end, we formulate a novel quantization training paradigm to compress the
embeddings from the training stage, termed low-precision training (LPT). Also,
we provide theoretical analysis on its convergence. The results show that
stochastic weight quantization has a faster convergence rate and a smaller
convergence error than deterministic weight quantization in LPT. Further, to
reduce the accuracy degradation, we propose adaptive low-precision training
(ALPT) that learns the step size (i.e., the quantization resolution) through
gradient descent. Experiments on two real-world datasets confirm our analysis
and show that ALPT can significantly improve the prediction accuracy,
especially at extremely low bit widths. For the first time in CTR models, we
successfully train 8-bit embeddings without sacrificing prediction accuracy.
The code of ALPT is publicly available.
