LLaMA-NAS: Efficient Neural Architecture Search for Large Language Models

• URL: http://arxiv.org/abs/2405.18377v1
• Date: Tue, 28 May 2024 17:20:44 GMT
• Title: LLaMA-NAS: Efficient Neural Architecture Search for Large Language Models
• Authors: Anthony Sarah, Sharath Nittur Sridhar, Maciej Szankin, Sairam Sundaresan,
• Abstract summary: Large language models (LLMs) solve natural language processing, complex reasoning, sentiment analysis and other tasks. These abilities come with very high memory and computational costs which precludes the use of LLMs on most hardware platforms. We propose an effective method of finding Pareto-optimal network architectures based on LLaMA2-7B using one-shot NAS. We show that, for certain standard benchmark tasks, the pre-trained LLaMA2-7B network is unnecessarily large and complex.
• Score: 3.4070166891274263
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The abilities of modern large language models (LLMs) in solving natural language processing, complex reasoning, sentiment analysis and other tasks have been extraordinary which has prompted their extensive adoption. Unfortunately, these abilities come with very high memory and computational costs which precludes the use of LLMs on most hardware platforms. To mitigate this, we propose an effective method of finding Pareto-optimal network architectures based on LLaMA2-7B using one-shot NAS. In particular, we fine-tune LLaMA2-7B only once and then apply genetic algorithm-based search to find smaller, less computationally complex network architectures. We show that, for certain standard benchmark tasks, the pre-trained LLaMA2-7B network is unnecessarily large and complex. More specifically, we demonstrate a 1.5x reduction in model size and 1.3x speedup in throughput for certain tasks with negligible drop in accuracy. In addition to finding smaller, higher-performing network architectures, our method does so more effectively and efficiently than certain pruning or sparsification techniques. Finally, we demonstrate how quantization is complementary to our method and that the size and complexity of the networks we find can be further decreased using quantization. We believe that our work provides a way to automatically create LLMs which can be used on less expensive and more readily available hardware platforms.

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