GaLore：通過梯度低秩投影實現記憶效率的LLM訓練

GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection

March 6, 2024

作者: Jiawei Zhao, Zhenyu Zhang, Beidi Chen, Zhangyang Wang, Anima Anandkumar, Yuandong Tian

cs.AI

摘要

訓練大型語言模型（LLMs）存在著重大的記憶挑戰，主要是由於權重和優化器狀態的增加導致的。常見的記憶減少方法，如低秩適應（LoRA），在每一層中將一個可訓練的低秩矩陣添加到凍結的預訓練權重中，減少可訓練參數和優化器狀態。然而，這些方法通常在預訓練和微調階段的訓練中表現不佳，因為它們將參數搜索限制在低秩子空間並改變訓練動態，進一步可能需要完整秩的熱啟動。在這項工作中，我們提出了梯度低秩投影（GaLore），這是一種訓練策略，允許完全參數學習，但比LoRA等常見的低秩適應方法更節省記憶體。我們的方法在優化器狀態中將記憶體使用量降低了高達65.5％，同時在使用C4數據集的LLaMA 1B和7B架構進行預訓練以及在GLUE任務上對RoBERTa進行微調時，保持了效率和性能。我們的8位GaLore進一步將優化器記憶體降低了高達82.5％，總訓練記憶體降低了63.3％，與BF16基線相比。值得注意的是，我們首次展示了在具有24GB記憶體的消費級GPU（例如NVIDIA RTX 4090）上，無需模型並行、檢查點或卸載策略即可進行7B模型的預訓練的可行性。

English

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.