@inproceedings{
devvrit2023matformer,
title={MatFormer: Nested Transformer for Elastic Inference},
author={Fnu Devvrit and Sneha Kudugunta and Aditya Kusupati and Tim Dettmers and Kaifeng Chen and Inderjit Dhillon and Yulia Tsvetkov and Hannaneh Hajishirzi and Sham Kakade and Ali Farhadi and Prateek Jain},
booktitle={Workshop on Advancing Neural Network Training: Computational Efficiency, Scalability, and Resource Optimization (WANT@NeurIPS 2023)},
year={2023},
url={https://openreview.net/forum?id=93BaEweoRg}
}
Transformer models are deployed in a wide range of settings, from multi-accelerator clusters to standalone mobile phones. The diverse inference constraints in these scenarios necessitate practitioners to train foundation models such as PaLM 2, Llama, & ViTs as a series of models of varying sizes. Due to significant training costs, only a select few model sizes are trained and supported, limiting more fine-grained control over relevant tradeoffs, including latency, cost, and accuracy. This work introduces MatFormer, a nested Transformer architecture designed to offer elasticity in a variety of deployment constraints. Each Feed Forward Network (FFN) block of a MatFormer model is jointly optimized with a few nested smaller FFN blocks. This training procedure allows for the Mix'n'Match of model granularities across layers -- i.e., a trained universal MatFormer model enables extraction of hundreds of accurate smaller models, which were never explicitly optimized. We empirically demonstrate MatFormer's effectiveness across different model classes (decoders & encoders), modalities (language & vision), and scales (up to 2.6B parameters). We find that a 2.6B decoder-only MatFormer language model (MatLM) allows us to extract smaller models spanning from 1.5B to 2.6B, each exhibiting comparable validation loss and one-shot downstream evaluations to their independently trained counterparts. Furthermore, we observe that smaller encoders extracted from a universal MatFormer-based ViT (MatViT) encoder preserve the metric-space structure for adaptive large-scale retrieval. Finally, we showcase that speculative decoding with the accurate and consistent submodels extracted from MatFormer can further reduce inference latency.
MatFormer: Nested Transformer for Elastic Inference
Fnu Devvrit Sneha Kudugunta Aditya Kusupati Tim Dettmers Kaifeng Chen Inderjit S Dhillon Yulia Tsvetkov Hannaneh Hajishirzi Sham M. Kakade Ali Farhadi Prateek Jain