GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection
<|reference_start|>GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection: Training Large Language Models (LLMs) presents significant memory challenges, predominantly due to the growing size of weights and optimizer states. Common memory-reduction approaches, such as low-rank adaptation (LoRA), add a trainable low-rank matrix to the frozen pre-trained weight in each layer, reducing trainable parameters and optimizer states. However, such approaches typically underperform training with full-rank weights in both pre-training and fine-tuning stages since they limit the parameter search to a low-rank subspace and alter the training dynamics, and further, may require full-rank warm start. In this work, we propose Gradient Low-Rank Projection (GaLore), a training strategy that allows full-parameter learning but is more memory-efficient than common low-rank adaptation methods such as LoRA. Our approach reduces memory usage by up to 65.5% in optimizer states while maintaining both efficiency and performance for pre-training on LLaMA 1B and 7B architectures with C4 dataset with up to 19.7B tokens, and on fine-tuning RoBERTa on GLUE tasks. Our 8-bit GaLore further reduces optimizer memory by up to 82.5% and total training memory by 63.3%, compared to a BF16 baseline. Notably, we demonstrate, for the first time, the feasibility of pre-training a 7B model on consumer GPUs with 24GB memory (e.g., NVIDIA RTX 4090) without model parallel, checkpointing, or offloading strategies.<|reference_end|>
@article{zhao2024galore:,
    title={GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection},
    author={Jiawei Zhao, Zhenyu Zhang, Beidi Chen, Zhangyang Wang, Anima
  Anandkumar, Yuandong Tian},
    journal={arXiv preprint arXiv:2403.03507},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.03507},
    primaryClass={cs.LG}
}