Salesforce AI Research Proposes PerfCodeGen: A Training-Free Framework That Improves LLM-Generated Code Performance Through Usage Feedback

Large-scale Language Models (LLMs) have become important tools in software development, providing capabilities such as code snippet generation, automated unit testing, and debugging. However, these models often fail to produce code that is not only functionally inefficient but also efficient at runtime. Careless runtime efficiency can lead to inefficient software, increase operating costs, and impact user experience. This issue is particularly relevant for less experienced developers, who may rely on suggested AI code without fully understanding its implications. Salesforce Research addresses these challenges with PerfCodeGen, a framework that aims to improve both the correctness and performance of LLM-generated code.

PerfCodeGen for Salesforce AI is a free training framework designed to improve the runtime efficiency of LLM-generated code. It achieves this by using performance feedback in an iterative self-correction process. Unlike methods that require fine-tuning with extensive training data, PerfCodeGen uses a feedback loop that evaluates and refines code based on runtime metrics during test execution. The framework operates in two key phases: refining accuracy and improving efficiency. Initially, it ensures that the generated code meets the performance requirements by addressing the problems identified in the unit tests. Once accuracy is confirmed, the framework focuses on runtime efficiency, optimizing the code by identifying and refining resource-intensive test cases. This iterative process results in correct and efficient solutions.

Technical Details and Benefits

PerfCodeGen integrates with existing LLM workflows and starts by generating multiple candidate solutions using a sample nucleus. In the first phase, these students are assessed for accuracy through unit tests. Feedback from failed tests is used to refine solutions. Once the correctness of the operation has been confirmed, the framework moves to the second stage, analyzing the runtime metrics to identify bottlenecks. This information is then used to further improve the code, focusing on the most time-consuming test cases.

This two-stage process increases the chances of producing high-performance programs. PerfCodeGen's methodology shows methods for debugging and human optimization, making it both efficient and accurate. Additionally, the framework's reliance on feedback rather than retraining allows it to be accessible to a variety of LLMs and application domains. It has shown consistent improvements in runtime efficiency and accuracy across models such as Phi-3-mini, Llama 3, and GPT-4.

PerfCodeGen has been tested on benchmarks such as HumanEval, MBPP, and APPS, demonstrating its effectiveness:

1. Uptime Performance: In HumanEval, the improvement rate for GPT-4 (%Opt) increased from 24.54% to 28.83% with PERFCODEGEN, with similar improvements observed for all other models.
2. Development of Righteousness: For MBPP, the correctness rate of GPT-3.5 (%Correct) increased from 66.38% to 73.36% for one sample (Best@1).
3. Most Practical World Fact: PERFCODEGEN enabled LLMs to generate solutions that outperform the ground truth in approximately 55% of HumanEval tasks and 67% of MBPP tasks.
4. Scalability: Open models like Phi-3-mini and Mixtral achieved performance compared to closed models like GPT-3.5 and GPT-4.

These results highlight PERFCODEGEN's ability to measure correctness and runtime efficiency, making it a valuable addition to LLM-driven code generation workflows.

Conclusion:

PerfCodeGen provides an effective solution to the main limitation of current LLMs: their focus on accuracy at the expense of runtime efficiency. By integrating performance feedback into an iterative development process, PerfCodeGen enables code generation that is both accurate and efficient. This approach improves the usability of LLMs in software development, providing developers with the tools to produce high-quality code without extensive retraining. The framework's success across various benchmarks demonstrates its potential as a step forward in creating efficient, reliable and affordable AI-driven programming solutions.

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