RECORD
ALPINE: An adaptive language-agnostic pruning method for language models for code
Software Engineering · Submitted 4 Jul 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim These findings highlight the potential of ALPINE in making language models of code more resource-efficient and accessible while preserving their performance, contributing to the overall sustainability of adopting language models in software development.
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The abstract, sentence by sentence
Language models of code have demonstrated state-of-the-art performance across various software engineering and source code analysis tasks. However, their demanding computational resource requirements and consequential environmental footprint remain as significant challenges. This work introduces ALPINE, an adaptive programming language-agnostic pruning technique designed to substantially reduce these models' computational overhead. The proposed method offers a pluggable layer that can be integrated with all Transformer-based models. With ALPINE, input sequences undergo adaptive compression throughout the pipeline, reaching a size up to $\times 3$ less their initial size, resulting in significantly reduced computational load. Our experiments on two software engineering tasks, defect prediction and code clone detection across three language models CodeBERT, GraphCodeBERT and UniXCoder show that ALPINE achieves up to a 50% reduction in FLOPs, a 58.1% decrease in memory footprint, and a 28.1% improvement in throughput on average. This led to a reduction in CO2 by up to $44.85$%. Importantly, it achieves the reduction in computation resources while maintaining up to 98.1% of the original predictive performance. These findings highlight the potential of ALPINE in making language models of code more resource-efficient and accessible while preserving their performance, contributing to the overall sustainability of adopting language models in software development. Also, it sheds light on redundant and noisy information in source code analysis corpora, as shown by the substantial sequence compression achieved by ALPINE.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Language models of code have demonstrated state-of-the-art performance across various software engineering and source code analysis tasks. | background 1.00 | 0.06 | 0.02 | asserted 1.00 |
| s1 | However, their demanding computational resource requirements and consequential environmental footprint remain as significant challenges. | background 0.89 | 0.05 | 0.02 | asserted 1.00 |
| s2 | This work introduces ALPINE, an adaptive programming language-agnostic pruning technique designed to substantially reduce these models' computational overhead. | contribution 0.99 | 0.77 | 0.11 | asserted 0.98 |
| s3 | The proposed method offers a pluggable layer that can be integrated with all Transformer-based models. | method 0.86 | 0.58 | 0.03 | asserted 0.98 |
| s4 | With ALPINE, input sequences undergo adaptive compression throughout the pipeline, reaching a size up to $\times 3$ less their initial size, resulting in significantly reduced computational load. | method 0.91 | 0.78 | 0.09 | asserted 0.98 |
| s5 | Our experiments on two software engineering tasks, defect prediction and code clone detection across three language models CodeBERT, GraphCodeBERT and UniXCoder show that ALPINE achieves up to a 50% reduction in FLOPs, a 58.1% decrease in memory footprint, and a 28.1% improvement in throughput on average. | result 1.00 | 0.97 | 0.24 | asserted 1.00 |
| s6 | This led to a reduction in CO2 by up to $44.85$%. | result 1.00 | 0.90 | 0.08 | asserted 1.00 |
| s7 | Importantly, it achieves the reduction in computation resources while maintaining up to 98.1% of the original predictive performance. | result 1.00 | 0.95 | 0.29 | asserted 1.00 |
| s8 | These findings highlight the potential of ALPINE in making language models of code more resource-efficient and accessible while preserving their performance, contributing to the overall sustainability of adopting language models in software development. | interpretation 1.00 | 0.51 | 0.81 | hedged possibility 0.62 |
| s9 | Also, it sheds light on redundant and noisy information in source code analysis corpora, as shown by the substantial sequence compression achieved by ALPINE. | interpretation 0.99 | 0.86 | 0.06 | asserted 0.89 |
What was examined, and what was not
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The paper
Original abstract
Language models of code have demonstrated state-of-the-art performance across various software engineering and source code analysis tasks. However, their demanding computational resource requirements and consequential environmental footprint remain as significant challenges. This work introduces ALPINE, an adaptive programming language-agnostic pruning technique designed to substantially reduce these models' computational overhead. The proposed method offers a pluggable layer that can be integrated with all Transformer-based models. With ALPINE, input sequences undergo adaptive compression throughout the pipeline, reaching a size up to $\times 3$ less their initial size, resulting in significantly reduced computational load. Our experiments on two software engineering tasks, defect prediction and code clone detection across three language models CodeBERT, GraphCodeBERT and UniXCoder show that ALPINE achieves up to a 50% reduction in FLOPs, a 58.1% decrease in memory footprint, and a 28.1% improvement in throughput on average. This led to a reduction in CO2 by up to $44.85$%. Importantly, it achieves the reduction in computation resources while maintaining up to 98.1% of the original predictive performance. These findings highlight the potential of ALPINE in making language models of code more resource-efficient and accessible while preserving their performance, contributing to the overall sustainability of adopting language models in software development. Also, it sheds light on redundant and noisy information in source code analysis corpora, as shown by the substantial sequence compression achieved by ALPINE.
Human review
How the literature uses it
3 arXiv papers cite this in 5 sentences
Papers citing it · 3 sentences without a confident label
Cites it as background 2 papers
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Saad et al. [ref] developed ALPINE, a programming language-agnostic pruning technique that significantly lowers the computational overhead of language models for code.
Model Compression vs. Adversarial Robustness: An Empirical Study on Language Models for Code · Related Work · 0.86
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Saad et al. [ref] introduced ALPINE, a pruning method that is independent of programming language and effectively reduces the computational cost of code-language models.
A Metamorphic Testing Perspective on Knowledge Distillation for Language Models of Code: Does the Student Deeply Mimic the Teacher? · Related Work · 0.81
What it cites · 10 sentences without a confident label
Uses the method or tool of 1 paper
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The measurements were taken using the tool provided by Lacoste [ref] .
Quantifying the Carbon Emissions of Machine Learning · RQ4: Impact of Computing Environment · 1.00
Uses data or values from 1 paper
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Wang [ref] have shown that the carbon footprint of pre-training the bert model [ref] on a machine with one NVIDIA Quadro RTX$8000$ is $199$ kg CO2.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding · Impact on Energy Consumption · 0.86
Cites as support 2 papers
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In addition to the financial impact, the carbon footprint of training and maintaining these models is a growing environmental concern as they contribute to higher CO2 emissions [ref] .
Quantifying the Carbon Emissions of Machine Learning · Introduction · 0.99
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In addition to the financial impact, the carbon footprint of training and maintaining these models is a growing environmental concern as they contribute to higher CO2 emissions [ref] .
Power Hungry Processing: Watts Driving the Cost of AI Deployment? · Introduction · 0.99
Cites as background 6 papers
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The techniques to enhance the effectiveness of a transformer model while maintaining its performance fall into three categories—knowledge distillation, quantization, and pruning [ref] .
A Survey on Model Compression for Large Language Models · Optimized transformers · 0.93
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Sample architectures include CodeT5 [ref] , CodeT5+ [ref] , and plbart [ref] .
Unified Pre-training for Program Understanding and Generation · Pre-trained language models of code · 0.97
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Sample architectures include CodeT5 [ref] , CodeT5+ [ref] , and plbart [ref] .
CodeT5+: Open Code Large Language Models for Code Understanding and Generation · Pre-trained language models of code · 0.97
Discussion
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