Performance-Aligned LLMs for Generating Fast Code
- URL: http://arxiv.org/abs/2404.18864v1
- Date: Mon, 29 Apr 2024 16:52:38 GMT
- Title: Performance-Aligned LLMs for Generating Fast Code
- Authors: Daniel Nichols, Pranav Polasam, Harshitha Menon, Aniruddha Marathe, Todd Gamblin, Abhinav Bhatele,
- Abstract summary: We introduce a reinforcement learning based methodology to align the outputs of code LLMs with performance.
We demonstrate that our fine-tuned model improves the expected speedup of generated code over base models for a set of benchmark tasks.
- Score: 2.180216161965907
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Optimizing scientific software is a difficult task because codebases are often large and complex, and performance can depend upon several factors including the algorithm, its implementation, and hardware among others. Causes of poor performance can originate from disparate sources and be difficult to diagnose. Recent years have seen a multitude of work that use large language models (LLMs) to assist in software development tasks. However, these tools are trained to model the distribution of code as text, and are not specifically designed to understand performance aspects of code. In this work, we introduce a reinforcement learning based methodology to align the outputs of code LLMs with performance. This allows us to build upon the current code modeling capabilities of LLMs and extend them to generate better performing code. We demonstrate that our fine-tuned model improves the expected speedup of generated code over base models for a set of benchmark tasks from 0.9 to 1.6 for serial code and 1.9 to 4.5 for OpenMP code.
Related papers
- VersiCode: Towards Version-controllable Code Generation [58.82709231906735]
We introduce VersiCode, the first comprehensive dataset designed to assess the ability of large language models to generate verifiable code for specific library versions.
We design two dedicated evaluation tasks: version-specific code completion (VSCC) and version-aware code editing (VACE)
Comprehensive experiments are conducted to benchmark the performance of LLMs, revealing the challenging nature of these tasks and VersiCode.
arXiv Detail & Related papers (2024-06-11T16:15:06Z) - DolphCoder: Echo-Locating Code Large Language Models with Diverse and
Multi-Objective Instruction Tuning [36.78560777629329]
We introduce a diverse instruction model (DolphCoder) with self-evaluating for code generation.
It learns diverse instruction targets and combines a code evaluation objective to enhance its code generation ability.
Our model achieves superior performance on the HumanEval and MBPP benchmarks.
arXiv Detail & Related papers (2024-02-14T12:34:58Z) - CodePori: Large Scale Model for Autonomous Software Development by Using
Multi-Agents [3.8066447473175304]
Large Language Models (LLMs) and Generative Pre-trained Transformers (GPTs) are reshaping the field of Software Engineering (SE)
This paper introduces CodePori, a novel model designed to automate code generation for extensive and complex software projects based on natural language prompts.
We show in the paper that CodePori is able to generate running code for large-scale projects, completing the entire software development process in minutes rather than hours, and at a cost of a few dollars.
arXiv Detail & Related papers (2024-02-02T13:42:50Z) - StepCoder: Improve Code Generation with Reinforcement Learning from
Compiler Feedback [58.20547418182074]
We introduce StepCoder, a novel framework for code generation, consisting of two main components.
CCCS addresses the exploration challenge by breaking the long sequences code generation task into a Curriculum of Code Completion Subtasks.
FGO only optimize the model by masking the unexecuted code segments to provide Fine-Grained Optimization.
Our method improves the ability to explore the output space and outperforms state-of-the-art approaches in corresponding benchmarks.
arXiv Detail & Related papers (2024-02-02T13:14:31Z) - LLM-Assisted Code Cleaning For Training Accurate Code Generators [53.087019724256606]
We investigate data quality for code and find that making the code more structured and readable leads to improved code generation performance of the system.
We build a novel data-cleaning pipeline that uses these principles to transform existing programs.
We evaluate our approach on two challenging algorithmic code generation benchmarks and find that fine-tuning CodeLLaMa-7B improves the performance by up to 30% compared to fine-tuning on the original dataset.
arXiv Detail & Related papers (2023-11-25T02:45:50Z) - CRAFT: Customizing LLMs by Creating and Retrieving from Specialized
Toolsets [75.64181719386497]
We present CRAFT, a tool creation and retrieval framework for large language models (LLMs)
It creates toolsets specifically curated for the tasks and equips LLMs with a component that retrieves tools from these sets to enhance their capability to solve complex tasks.
Our method is designed to be flexible and offers a plug-and-play approach to adapt off-the-shelf LLMs to unseen domains and modalities, without any finetuning.
arXiv Detail & Related papers (2023-09-29T17:40:26Z) - CodeTF: One-stop Transformer Library for State-of-the-art Code LLM [72.1638273937025]
We present CodeTF, an open-source Transformer-based library for state-of-the-art Code LLMs and code intelligence.
Our library supports a collection of pretrained Code LLM models and popular code benchmarks.
We hope CodeTF is able to bridge the gap between machine learning/generative AI and software engineering.
arXiv Detail & Related papers (2023-05-31T05:24:48Z) - CodeT5+: Open Code Large Language Models for Code Understanding and
Generation [72.1638273937025]
Large language models (LLMs) pretrained on vast source code have achieved prominent progress in code intelligence.
CodeT5+ is a family of encoder-decoder LLMs for code in which component modules can be flexibly combined to suit a wide range of downstream code tasks.
We extensively evaluate CodeT5+ on over 20 code-related benchmarks in different settings, including zero-shot, finetuning, and instruction-tuning.
arXiv Detail & Related papers (2023-05-13T14:23:07Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.