SYSTEMS RESEARCH

Meta Large Language Model Compiler: Foundation Models of Compiler Optimization

June 27, 2024

Abstract

Large Language Models (LLMs) have demonstrated remarkable capabilities across a variety of software engineering and coding tasks. However, their application in the domain of code and compiler optimization remains underexplored. Training LLMs is resource-intensive, requiring substantial GPU hours and extensive data collection, which can be prohibitive. To address this gap, we introduce Meta Large Language Model Compiler (LLM Compiler), a suite of robust, openly available, pre-trained models specifically designed for code optimization tasks. Built on the foundation of Code Llama, LLM Compiler enhances the understanding of compiler intermediate representations (IRs), assembly language, and optimization techniques. The model has been trained on a vast corpus of 546 billion tokens of LLVM-IR and assembly code and has undergone instruction fine-tuning to interpret compiler behavior. LLM Compiler is released under a bespoke commercial license to allow wide reuse and is available in two sizes: 7 billion and 13 billion parameters. We also present fine-tuned versions of the model, demonstrating its enhanced capabilities in optimizing code size and disassembling from x86_64 and ARM assembly back into LLVM-IR. These achieve 77% of the optimising potential of an autotuning search, and 45% disassembly round trip (14% exact match). This release aims to provide a scalable, cost-effective foundation for further research and development in compiler optimization by both academic researchers and industry practitioners.

Download the Paper

AUTHORS

Written by

Chris Cummins

Volker Seeker

Dejan Grubisic

Baptiste Rozière

Jonas Gehring

Gabriel Synnaeve

Hugh Leather

Publisher

ArXiv

Research Topics

Systems Research

Related Publications

July 23, 2024

SYSTEMS RESEARCH

CYBERSECEVAL 3: Advancing the Evaluation of Cybersecurity Risks and Capabilities in Large Language Models

Shengye Wan, Cyrus Nikolaidis, Daniel Song, David Molnar, James Crnkovich, Jayson Grace, Manish Bhatt, Sahana Chennabasappa, Spencer Whitman, Stephanie Ding, Vlad Ionescu, Yue Li, Joshua Saxe

July 23, 2024

June 14, 2024

NLP

SYSTEMS RESEARCH

LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding

Mostafa Elhoushi, Akshat Shrivastava, Diana Liskovich, Basil Hosmer, Bram Wasti, Liangzhen Lai, Bilge Acun, Ahmed Aly, Beidi Chen, Carole-Jean Wu, Ahmed Roman, Nas Mahmoud, Saurabh Agarwal

June 14, 2024

June 07, 2024

CORE MACHINE LEARNING

SYSTEMS RESEARCH

Beyond Efficiency: Scaling AI Sustainably

Carole-Jean Wu, Bilge Acun, Ramya Raghavendra, Kim Hazelwood

June 07, 2024

November 07, 2023

NLP

COMPUTER VISION

The Framework Tax: Disparities Between Inference Efficiency in NLP Research and Deployment

Jared Fernandez, Jacob Kahn, Clara Na, Yonatan Bisk, Emma Strubell

November 07, 2023

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.