Scope is all you need: Transforming LLMs for HPC Code

TL;DR

This paper introduces Tokompiler, a domain-specific tokenizer for HPC code, enabling smaller, more efficient LLMs like SPT-Code and Polycoder to outperform traditional models in HPC code understanding and completion tasks.

Contribution

The paper presents Tokompiler, a novel tokenizer designed for HPC code, and demonstrates its effectiveness in training smaller, domain-specific LLMs that outperform larger, general-purpose models.

Findings

Tokompiler improves code completion accuracy.

Domain-specific LLMs outperform general-purpose models.

Perplexity scores reduced to ~1 with Tokompiler.

Abstract

With easier access to powerful compute resources, there is a growing trend in the field of AI for software development to develop larger and larger language models (LLMs) to address a variety of programming tasks. Even LLMs applied to tasks from the high-performance computing (HPC) domain are huge in size (e.g., billions of parameters) and demand expensive compute resources for training. We found this design choice confusing - why do we need large LLMs trained on natural languages and programming languages unrelated to HPC for HPC-specific tasks? In this line of work, we aim to question design choices made by existing LLMs by developing smaller LLMs for specific domains - we call them domain-specific LLMs. Specifically, we start off with HPC as a domain and propose a novel tokenizer named Tokompiler, designed specifically for preprocessing code in HPC and compilation-centric tasks. Tokompiler leverages knowledge of language primitives to generate language-oriented tokens, providing a context-aware understanding of code structure while avoiding human semantics attributed to code structures completely. We applied Tokompiler to pre-train two state-of-the-art models, SPT-Code and Polycoder, for a Fortran code corpus mined from GitHub. We evaluate the performance of these models against the conventional LLMs. Results demonstrate that Tokompiler significantly enhances code completion accuracy and semantic understanding compared to traditional tokenizers in normalized-perplexity tests, down to ~1 perplexity score. This research opens avenues for further advancements in domain-specific LLMs, catering to the unique demands of HPC and compilation tasks.