When to Stop? Towards Efficient Code Generation in LLMs with Excess Token Prevention Article Swipe
Lianghong Guo
,
Yanlin Wang
,
Ensheng Shi
,
Wanjun Zhong
,
Hongyu Zhang
,
Jiachi Chen
,
Ruikai Zhang
,
Yuchi Ma
,
Zibin Zheng
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.20042
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.20042
Code generation aims to automatically generate code snippets that meet given natural language requirements and plays an important role in software development. Although Code LLMs have shown excellent performance in this domain, their long generation time poses a signification limitation in practice use. In this paper, we first conduct an in-depth preliminary study with different Code LLMs on code generation tasks and identify a significant efficiency issue, i.e., continual generation of excess tokens. It harms the developer productivity and leads to huge computational wastes. To address it, we introduce CodeFast, an inference acceleration approach for Code LLMs on code generation. The key idea of CodeFast is to terminate the inference process in time when unnecessary excess tokens are detected. First, we propose an automatic data construction framework to obtain training data. Then, we train a unified lightweight model GenGuard applicable to multiple programming languages to predict whether to terminate inference at the current step. Finally, we enhance Code LLM with GenGuard to accelerate its inference in code generation tasks. We conduct extensive experiments with CodeFast on five representative Code LLMs across four widely used code generation datasets. Experimental results show that (1) CodeFast can significantly improve the inference speed of various Code LLMs in code generation, ranging form 34% to 452%, without compromising the quality of generated code. (2) CodeFast is stable across different parameter settings and can generalize to untrained datasets. Our code and data are available at https://github.com/DeepSoftwareAnalytics/CodeFast
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2407.20042
- https://arxiv.org/pdf/2407.20042
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401202470
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4401202470Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2407.20042Digital Object Identifier
- Title
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When to Stop? Towards Efficient Code Generation in LLMs with Excess Token PreventionWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-07-29Full publication date if available
- Authors
-
Lianghong Guo, Yanlin Wang, Ensheng Shi, Wanjun Zhong, Hongyu Zhang, Jiachi Chen, Ruikai Zhang, Yuchi Ma, Zibin ZhengList of authors in order
- Landing page
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https://arxiv.org/abs/2407.20042Publisher landing page
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https://arxiv.org/pdf/2407.20042Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2407.20042Direct OA link when available
- Concepts
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Security token, Code (set theory), Business, Psychology, Computer science, Computer security, Programming language, Set (abstract data type)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.untrained | 230 |
| abstract_inverted_index.accelerate | 162 |
| abstract_inverted_index.applicable | 139 |
| abstract_inverted_index.efficiency | 65 |
| abstract_inverted_index.generalize | 228 |
| abstract_inverted_index.generation | 1, 34, 59, 69, 167, 185 |
| abstract_inverted_index.limitation | 39 |
| abstract_inverted_index.experiments | 172 |
| abstract_inverted_index.generation, | 205 |
| abstract_inverted_index.generation. | 99 |
| abstract_inverted_index.lightweight | 136 |
| abstract_inverted_index.performance | 28 |
| abstract_inverted_index.preliminary | 51 |
| abstract_inverted_index.programming | 142 |
| abstract_inverted_index.significant | 64 |
| abstract_inverted_index.unnecessary | 114 |
| abstract_inverted_index.Experimental | 187 |
| abstract_inverted_index.acceleration | 92 |
| abstract_inverted_index.compromising | 212 |
| abstract_inverted_index.construction | 125 |
| abstract_inverted_index.development. | 21 |
| abstract_inverted_index.productivity | 77 |
| abstract_inverted_index.requirements | 13 |
| abstract_inverted_index.automatically | 4 |
| abstract_inverted_index.computational | 82 |
| abstract_inverted_index.significantly | 194 |
| abstract_inverted_index.signification | 38 |
| abstract_inverted_index.representative | 177 |
| abstract_inverted_index.https://github.com/DeepSoftwareAnalytics/CodeFast | 239 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 9 |
| citation_normalized_percentile |