JORA: JAX Tensor-Parallel LoRA Library for Retrieval Augmented Fine-Tuning

• URL: http://arxiv.org/abs/2403.11366v2
• Date: Tue, 19 Mar 2024 16:19:49 GMT
• Title: JORA: JAX Tensor-Parallel LoRA Library for Retrieval Augmented Fine-Tuning
• Authors: Anique Tahir, Lu Cheng, Huan Liu,
• Abstract summary: We introduce a novel framework for PEFT-compatible fine-tuning of Llama-2 models, leveraging distributed training. Our framework uniquely utilizes JAX's just-in-time (JIT) compilation and tensor-sharding for efficient resource management. Our experiments show more than 12x improvement in runtime compared to Hugging Face/DeepSpeed implementation with four GPU while consuming less than half the VRAM per GPU.
• Score: 16.86356520836045
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The scaling of Large Language Models (LLMs) for retrieval-based tasks, particularly in Retrieval Augmented Generation (RAG), faces significant memory constraints, especially when fine-tuning extensive prompt sequences. Current open-source libraries support full-model inference and fine-tuning across multiple GPUs but fall short of accommodating the efficient parameter distribution required for retrieved context. Addressing this gap, we introduce a novel framework for PEFT-compatible fine-tuning of Llama-2 models, leveraging distributed training. Our framework uniquely utilizes JAX's just-in-time (JIT) compilation and tensor-sharding for efficient resource management, thereby enabling accelerated fine-tuning with reduced memory requirements. This advancement significantly improves the scalability and feasibility of fine-tuning LLMs for complex RAG applications, even on systems with limited GPU resources. Our experiments show more than 12x improvement in runtime compared to Hugging Face/DeepSpeed implementation with four GPUs while consuming less than half the VRAM per GPU.

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