Summary Outlier-Weighed Layerwise Sparsity for Pruning Large Language Models arxiv.org
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Researchers have developed OWL, a pruning method for Large Language Models (LLMs) that improves performance by incorporating non-uniform layerwise sparsity ratios, outperforming previous methods and efficiently pruning a 65B LLaMA model in 4 seconds.
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Key Points
- Large Language Models (LLMs) have gained popularity for their impressive performance in various applications.
- Researchers have explored network pruning techniques to reduce the size of LLMs without sacrificing performance.
- Non-uniform layerwise sparsity can yield improved results in LLM pruning compared to uniformly pruning all layers.
- The paper introduces a novel LLM pruning methodology called Outlier Weighed Layerwise Sparsity (OWL) that incorporates non-uniform layerwise sparsity ratios based on the distribution of outliers within LLMs.
- OWL outperforms previous state-of-the-art methods, such as Wanda and SparseGPT, at high sparsity levels.
- OWL and uniform sparsity methods perform better than other layerwise sparsity methods, while the ERK family of methods is less suitable for LLM pruning.
- OWL has comparable computational complexity to other methods and efficiently prunes large LLM models within seconds.
- OWL highlights the importance of layerwise sparsity ratios in LLM pruning and opens up possibilities for specialized sparse algorithms and optimized deployment of LLMs in practical applications.
Summaries
52 word summary
Researchers introduce OWL, a pruning method for Large Language Models (LLMs) that improves performance by incorporating non-uniform layerwise sparsity ratios. OWL outperforms previous methods, achieving higher perplexity reductions, with comparable computational complexity. It efficiently prunes a 65B LLaMA model in 4 seconds, opening new possibilities for specialized sparse algorithms and LLM optimization.
74 word summary
Researchers have developed OWL, a pruning methodology for reducing the size of Large Language Models (LLMs) without sacrificing performance. OWL incorporates non-uniform layerwise sparsity ratios based on outlier ratios within each layer, resulting in improved performance. Empirical evaluations show that OWL outperforms previous methods, achieving higher perplexity reductions. It offers comparable computational complexity and efficiently prunes a 65B LLaMA model within 4 seconds, introducing new possibilities for specialized sparse algorithms and optimizing LLM deployment.
138 word summary
Researchers have developed a novel pruning methodology called Outlier Weighed Layerwise Sparsity (OWL) to reduce the size of Large Language Models (LLMs) without compromising performance. The authors conducted a comprehensive analysis of token features within LLMs and discovered a strong correlation with the emergence of outliers. OWL incorporates non-uniform layerwise sparsity ratios that align with the outlier ratios observed within each layer, resulting in improved performance. Empirical evaluations demonstrate that OWL outperforms previous methods, achieving higher perplexity reductions. Comparisons with other layerwise sparsity methods show that OWL and uniform sparsity perform better, while the ERK family of methods is less suitable for LLM pruning. OWL also offers comparable computational complexity and efficiently prunes a 65B LLaMA model within 4 seconds. Overall, OWL introduces new possibilities for specialized sparse algorithms and optimizes the deployment of LLMs in practical applications.
378 word summary
Large Language Models (LLMs) have gained popularity for their impressive performance in various applications. However, their large size poses challenges in terms of practical deployment. To address this issue, researchers have explored network pruning techniques to reduce the size of LLMs without sacrificing performance. Previous pruning strategies for LLMs have focused on uniformly pruning all layers at equivalent sparsity levels. However, recent trends in vision models have shown that non-uniform layerwise sparsity can yield improved results. This paper investigates the reasons behind this disparity and proposes a novel LLM pruning methodology called Outlier Weighed Layerwise Sparsity (OWL).
The authors conduct a comprehensive analysis of the distribution of token features within LLMs and discover a strong correlation with the emergence of outliers, which are features with significantly greater magnitudes compared to others. Inspired by this finding, they introduce OWL, which incorporates a tailored set of non-uniform layerwise sparsity ratios specifically designed for LLM pruning. The sparsity ratio of OWL is directly proportional to the outlier ratio observed within each layer, allowing for a more effective alignment between layerwise weight sparsity and outlier ratios.
The empirical evaluation of OWL across different LLM models demonstrates its advantages over previous methods. For instance, OWL outperforms the state-of-the-art Wanda and SparseGPT by 61.22 and 6.80 perplexity points, respectively, at a high sparsity level of 70%. The results show that OWL consistently improves performance and achieves higher perplexity reductions compared to other methods.
The paper also compares OWL with other layerwise sparsity methods, such as global pruning, uniform sparsity, and Erdo?s-Re?nyi Kernel (ERK). The results indicate that OWL and uniform sparsity perform better than other methods, while the ERK family of methods is less suitable for LLM pruning.
In terms of efficiency, OWL has a comparable computational complexity to Wanda and SparseGPT. The pruning time of OWL is slightly higher when applied with Wanda, but it efficiently prunes a 65B LLaMA model within 4 seconds.
In conclusion, this work highlights the importance of layerwise sparsity ratios in LLM pruning and introduces OWL as an effective pruning methodology. OWL takes into account the distribution of outliers within LLMs and achieves better performance compared to existing methods. The findings open up new possibilities for specialized sparse algorithms and optimize the deployment of LLMs in practical applications.