Machine Learning

TL;DR: Organize your neurons into a tree to get 78x faster inference (theoretical limit is 341x).

This was demonstrated on BERT-base, where this change preserved 96% of its downstream GLUE performance. For a quick comparison, DistilBERT offers 1.6x acceleration while preserving 97% of GLUE performance.

This is a HuggingFace Featured Paper from 11/21/2023.

Paper: https://arxiv.org/abs/2311.10770

Code: https://github.com/pbelcak/UltraFastBERT

Model: https://huggingface.co/pbelcak/UltraFastBERT-1x11-long

Abstract:

Language models only really need to use an exponential fraction of their neurons for individual inferences.

As proof, we present UltraFastBERT, a BERT variant that uses 0.3% of its neurons during inference while performing on par with similar BERT models. UltraFastBERT selectively engages just 12 out of 4095 neurons for each layer inference. This is achieved by replacing feedforward networks with fast feedforward networks (FFFs).

While no truly efficient implementation currently exists to unlock the full acceleration potential of conditional neural execution, we provide high-level CPU code achieving 78x speedup over the optimized baseline feedforward implementation, and a PyTorch implementation delivering 40x speedup over the equivalent batched feedforward inference.

We publish our training code, benchmarking setup, and model weights.

This exponential acceleration was achieved on a 180mn BERT model. Just imagine how amazing the speedup would be on a multi-bn parameter model such as LLaMA if the tree trick (i.e. "fast feedforward networks") continues to scale up to larger layer sizes...