Automated Deep Learning Optimization via DSL-Based Source Code Transformation

TL;DR

Adopter is an automated tool that uses a domain-specific language to optimize deep learning model code by applying transformation rules, significantly improving training speed and memory efficiency.

Contribution

It introduces a DSL-based approach for automated deep learning model optimization through source code transformation, enhancing efficiency and accuracy.

Findings

Improves precision by 3% and recall by 56% over existing methods.

Increases training speed by 22.7% on average.

Reduces GPU memory usage by 10.5%.

Abstract

As deep learning models become increasingly bigger and more complex, it is critical to improve model training and inference efficiency. Though a variety of highly optimized libraries and packages (known as DL kernels) have been developed, it is tedious and time-consuming to figure out which kernel to use, where to use, and how to use them correctly. To address this challenge, we propose an Automated Deep learning OPTimization approach called Adopter. We design a Domain-Specific Language (DSL) to represent DL model architectures and leverage this DSL to specify model transformation rules required to integrate a DL kernel into a model. Given the source code of a DL model and the transformation rules for a set of kernels, Adopter first performs inter-procedural analysis to identify and express the model architecture in our DSL. Then, Adopter performs scope analysis and sub-sequence matching to identify locations in the model architecture where the transformation rules can be applied. Finally, Adopter proposes a synthesis-based code transformation method to apply the transformation rule. We curated a benchmark with 199 models from Hugging Face and a diverse set of DL kernels. We found that, compared to a state-of-the-art automated code transformation technique, Adopter helps improve the precision and recall by 3% and 56%, respectively. An in-depth analysis of 9 models revealed that on average, Adopter improved the training speed by 22.7% while decreasing the GPU memory usage by 10.5%.