Tensorized Embedding Layers for Efficient Model Compression

Oleksii Hrinchuk, Valentin Khrulkov, Leyla Mirvakhabova, Ivan Oseledets

Keywords: Embedding layers compression, tensor networks, low-rank factorization

TL;DR: Embedding layers are factorized with Tensor Train decomposition to reduce their memory footprint.

Abstract: The embedding layers transforming input words into real vectors are the key components of deep neural networks used in natural language processing. However, when the vocabulary is large, the corresponding weight matrices can be enormous, which precludes their deployment in a limited resource setting. We introduce a novel way of parametrizing embedding layers based on the Tensor Train (TT) decomposition, which allows compressing the model significantly at the cost of a negligible drop or even a slight gain in performance. We evaluate our method on a wide range of benchmarks in natural language processing and analyze the trade-off between performance and compression ratios for a wide range of architectures, from MLPs to LSTMs and Transformers.

Code: https://github.com/tt-embedding/tt-embeddings

Community Implementations: [ 4 code implementations](https://www.catalyzex.com/paper/tensorized-embedding-layers-for-efficient/code)