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Continuous representations have been widely adopted in recommender systemswhere a large number of entities are represented using embedding vectors. Asthe cardinality of the entities increases, the embedding components can easilycontain millions of parameters and become the bottleneck in both storage andinference due to large memory consumption. This work focuses on post-training4-bit quantization on the continuous embeddings. We propose row-wise uniformquantization with greedy search and codebook-based quantization thatconsistently outperforms state-of-the-art quantization approaches on reducingaccuracy degradation. We deploy our uniform quantization technique on aproduction model in Facebook and demonstrate that it can reduce the model sizeto only 13.89% of the single-precision version while the model quality staysneutral.

Post-Training 4-bit Quantization on Embedding Tables