Q-GaLore: Quantized GaLore with INT4 Projection and Layer-Adaptive Low-Rank Gradients

• URL: http://arxiv.org/abs/2407.08296v1
• Date: Thu, 11 Jul 2024 08:42:58 GMT
• Title: Q-GaLore: Quantized GaLore with INT4 Projection and Layer-Adaptive Low-Rank Gradients
• Authors: Zhenyu Zhang, Ajay Jaiswal, Lu Yin, Shiwei Liu, Jiawei Zhao, Yuandong Tian, Zhangyang Wang,
• Abstract summary: We introduce Q-Galore, a novel approach that substantially reduces memory usage by combining quantization and low-rank projection. We demonstrate that Q-Galore achieves highly competitive performance with exceptional memory efficiency.
• Score: 86.40635601953446
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Training Large Language Models (LLMs) is memory-intensive due to the large number of parameters and associated optimization states. GaLore, a recent method, reduces memory usage by projecting weight gradients into a low-rank subspace without compromising performance. However, GaLore relies on time-consuming Singular Value Decomposition (SVD) operations to identify the subspace, and the frequent subspace updates lead to significant training time overhead. Moreover, GaLore offers minimal improvements in accuracy and efficiency compared to LoRA in more accessible fine-tuning scenarios. To address these limitations, we introduce Q-Galore, a novel approach that substantially reduces memory usage by combining quantization and low-rank projection, surpassing the benefits of GaLore. Our method is based on two key observations: (i) the gradient subspace exhibits diverse properties, with some layers converging early in training while others are subject to frequent changes; (ii) the projection matrices are highly resilient to low-bit quantization. Leveraging these insights, Q-GaLore adaptively updates the gradient subspace based on its convergence statistics, achieving comparable performance while significantly reducing the number of SVD operations. We maintain the projection matrices in INT4 format and weights in INT8 format, incorporating stochastic rounding to capture accumulated gradient information. This approach enables a high-precision training trajectory using only low-precision weights. We demonstrate that Q-GaLore achieves highly competitive performance with exceptional memory efficiency. At pre-training, Q-GaLore facilitates training a LLaMA-7B model from scratch on a single NVIDIA RTX 4060 Ti with only 16 GB memory. At fine-tuning, it reduces memory consumption by up to 50% compared to LoRA and GaLore, while consistently outperforming QLoRA at the same memory cost.

Related papers

• Natural GaLore: Accelerating GaLore for memory-efficient LLM Training and Fine-tuning [1.3597551064547502]
  GaLore allows full-supervised learning while being more memory-efficient. This work introduces Natural GaLore, which efficiently applies the inverse Empirical Fisher Information Matrix to low-rank gradients.
  arXiv  Detail & Related papers  (2024-10-21T14:05:06Z)
• From GaLore to WeLore: How Low-Rank Weights Non-uniformly Emerge from Low-Rank Gradients [86.40635601953446]
  We study the emergence of low-rank structures across different layers of Modern Large Language Models. We present Weight Low-Rank Projection (WeLore) that unifies weight compression and memory-efficient fine-tuning as ONE.
  arXiv  Detail & Related papers  (2024-07-15T21:05:20Z)
• BlockLLM: Memory-Efficient Adaptation of LLMs by Selecting and Optimizing the Right Coordinate Blocks [19.007090250576585]
  BlockLLM is an approach inspired by block coordinate descent. It achieves state-of-the-art performance in both finetuning and pretraining tasks.
  arXiv  Detail & Related papers  (2024-06-25T05:45:12Z)
• VeLoRA: Memory Efficient Training using Rank-1 Sub-Token Projections [35.133698935322634]
  Large language models (LLMs) have recently emerged as powerful tools for tackling many language-processing tasks. We identify and characterise the important components needed for effective model convergence using gradient descent. This result leads us to a cheap and memory-efficient algorithm for both fine-tuning and pre-training LLMs.
  arXiv  Detail & Related papers  (2024-05-28T09:23:14Z)
• GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection [133.45193150403537]
  Training Large Language Models (LLMs) presents significant memory challenges due to the growing size of weights and GPU states. In this work, we propose Gradient Low-Rank Projection (GaLore) as a memory-efficient training strategy. Our 8-bit GaLore further reduces memory by up to 82.5% and total training memory by 63.3%, compared to a BF16 baseline.
  arXiv  Detail & Related papers  (2024-03-06T07:29:57Z)
• Winner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language Model [89.8764435351222]
  We propose a new family of unbiased estimators called WTA-CRS, for matrix production with reduced variance. Our work provides both theoretical and experimental evidence that, in the context of tuning transformers, our proposed estimators exhibit lower variance compared to existing ones.
  arXiv  Detail & Related papers  (2023-05-24T15:52:08Z)
• QLoRA: Efficient Finetuning of Quantized LLMs [66.58009990713134]
  We present QLoRA, an efficient finetuning approach that reduces memory usage enough to finetune a 65B parameter model on a single 48GB GPU. QLoRA backpropagates through a frozen, 4-bit quantized pretrained language model into Low Rank Adapters(LoRA) Our best model family, which we name Guanaco, outperforms all previous openly released models on the Vicuna benchmark.
  arXiv  Detail & Related papers  (2023-05-23T17:50:33Z)
• Quantized Neural Networks for Low-Precision Accumulation with Guaranteed Overflow Avoidance [68.8204255655161]
  We introduce a quantization-aware training algorithm that guarantees avoiding numerical overflow when reducing the precision of accumulators during inference. We evaluate our algorithm across multiple quantized models that we train for different tasks, showing that our approach can reduce the precision of accumulators while maintaining model accuracy with respect to a floating-point baseline.
  arXiv  Detail & Related papers  (2023-01-31T02:46:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.