Compiler-Generated Feedback for Optimizing LLVM Assembly Code Size with Large Language Models

Core Concepts

Using Large Language Models to optimize LLVM assembly code size through compiler feedback.

Abstract

Introduction to Large Language Models (LLMs)

LLMs have shown success in various software engineering tasks.
Availability of open-source code datasets has enabled the development of models like CodeLlama, ChatGPT, and Codex.

Utilizing LLMs in Compilers

LLMs are used to suggest optimization strategies for LLVM IR without changing semantics.
Previous research has explored machine learning-driven code optimization techniques.

Feedback-Directed Model

The model generates optimization passes and instruction counts, providing feedback for improvement.
Three types of feedback models are evaluated: Short, Long, and Fast Feedback.

Model Training and Datasets

A 7B-parameter model is trained using the best checkpoint from previous work.
Training data sets are constructed for each feedback form.

Evaluation

Comparison between original model and feedback models in optimizing LLVM IR instruction count.
Sampling properties of the Fast Feedback model compared to the original model.

Additional Experiments

Evaluation of iterative algorithm for the Fast Feedback model.
Comparison with sampling techniques used by the original model.

Related Work

Progress in Large Language Models for software engineering tasks.

Limitations and Future Work

Potential improvements through training on sampled data or developing smarter sampling heuristics.

Conclusions

Introduction of compiler-generated feedback using Large Language Models for optimizing LLVM assembly code size.

Stats

This approach adds an extra 0.53% improvement over -Oz to the original model.
The approach demonstrated remarkable code reasoning abilities, outperforming default LLVM -Oz optimization by 2.87%.
The Fast Feedback model fails to improve the performance of the original model when there are larger than 10 samples.

Quotes

"Large language models have proven their ability in software engineering tasks."
"Sampling has great potential to improve the performance of the original model."

Key Insights Distilled From

Compiler generated feedback for Large Language Models

by Dejan Grubis... at arxiv.org 03-25-2024

https://arxiv.org/pdf/2403.14714.pdf

Deeper Inquiries

How can training on sampled data enhance the performance of the Fast Feedback model?

Training on sampled data can enhance the performance of the Fast Feedback model by providing a more diverse and comprehensive set of examples for learning. When the model is trained on sampled data generated by the original model with varying temperatures, it gains exposure to a wider range of potential solutions and decision-making processes. This exposure helps the Fast Feedback model better understand how to handle different scenarios and make more informed predictions during inference. By training on diverse samples, the model can learn to adapt its responses based on various contexts, leading to improved generalization and robustness in optimizing LLVM IR code.

What are some potential drawbacks of relying solely on sampling techniques for optimization?

While sampling techniques offer significant benefits in exploring a wide range of possible solutions and improving performance, there are also potential drawbacks to relying solely on them for optimization:

Computational Cost: Sampling multiple times can be computationally expensive, especially when dealing with large language models that require substantial resources.
Lack of Determinism: Sampling introduces randomness into the process, which may lead to inconsistent results or unpredictable behavior across different runs.
Limited Exploration: Depending only on sampling may limit exploration in certain areas of optimization space, potentially missing out on optimal solutions that could have been discovered through other methods.
Overfitting: Excessive reliance on sampling without proper regularization or control measures could lead to overfitting to specific patterns present in the sampled data rather than learning generalized optimization strategies.

How might advancements in large language models impact traditional compiler optimization methods?

Advancements in large language models have significant implications for traditional compiler optimization methods:

Improved Code Generation: Large language models can enhance traditional compiler optimizations by generating more efficient code structures based on learned patterns from vast amounts of training data.
Automated Optimization Strategies: These models enable automated generation and selection of optimization strategies tailored to specific code snippets or programs without manual intervention.
Enhanced Performance Tuning: Large language models provide sophisticated techniques for fine-tuning compiler parameters such as loop unrolling factors, vectorization thresholds, or inline decisions based on learned patterns from extensive datasets.
Complexity Handling: Advanced language models can handle complex program structures and dependencies more effectively than conventional compilers, leading to optimized code generation even for intricate software systems.

In conclusion, advancements in large language models have transformative effects on traditional compiler optimization methods by introducing automation, efficiency improvements, and enhanced capabilities in handling modern software development challenges efficiently.