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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2404.10933 |
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| _version_ | 1866910413073940480 |
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| author | Kim, Taeho Wang, Yanming Chaturvedi, Vatshank Gupta, Lokesh Kim, Seyeon Kwon, Yongin Ha, Sangtae |
| author_facet | Kim, Taeho Wang, Yanming Chaturvedi, Vatshank Gupta, Lokesh Kim, Seyeon Kwon, Yongin Ha, Sangtae |
| contents | Fine-tuning pre-trained large language models (LLMs) with limited hardware presents challenges due to GPU memory constraints. Various distributed fine-tuning methods have been proposed to alleviate memory constraints on GPU. However, determining the most effective method for achieving rapid fine-tuning while preventing GPU out-of-memory issues in a given environment remains unclear. To address this challenge, we introduce LLMem, a solution that estimates the GPU memory consumption when applying distributed fine-tuning methods across multiple GPUs and identifies the optimal method. We conduct GPU memory usage estimation prior to fine-tuning, leveraging the fundamental structure of transformer-based decoder models and the memory usage distribution of each method. Experimental results show that LLMem accurately estimates peak GPU memory usage on a single GPU, with error rates of up to 1.6%. Additionally, it shows an average error rate of 3.0% when applying distributed fine-tuning methods to LLMs with more than a billion parameters on multi-GPU setups. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_10933 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LLMem: Estimating GPU Memory Usage for Fine-Tuning Pre-Trained LLMs Kim, Taeho Wang, Yanming Chaturvedi, Vatshank Gupta, Lokesh Kim, Seyeon Kwon, Yongin Ha, Sangtae Artificial Intelligence Computation and Language Machine Learning Fine-tuning pre-trained large language models (LLMs) with limited hardware presents challenges due to GPU memory constraints. Various distributed fine-tuning methods have been proposed to alleviate memory constraints on GPU. However, determining the most effective method for achieving rapid fine-tuning while preventing GPU out-of-memory issues in a given environment remains unclear. To address this challenge, we introduce LLMem, a solution that estimates the GPU memory consumption when applying distributed fine-tuning methods across multiple GPUs and identifies the optimal method. We conduct GPU memory usage estimation prior to fine-tuning, leveraging the fundamental structure of transformer-based decoder models and the memory usage distribution of each method. Experimental results show that LLMem accurately estimates peak GPU memory usage on a single GPU, with error rates of up to 1.6%. Additionally, it shows an average error rate of 3.0% when applying distributed fine-tuning methods to LLMs with more than a billion parameters on multi-GPU setups. |
| title | LLMem: Estimating GPU Memory Usage for Fine-Tuning Pre-Trained LLMs |
| topic | Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2404.10933 |