title: Q-GaLore: Quantized GaLore with INT4 Projection and Layer-Adaptive
    Low-Rank Gradients
summary: 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.
paper_authors._id: 6690b32d21b5557342304704
paper_authors.name: Zhenyu Zhang
paper_authors.status: claimed_verified
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paper_authors.fullname: Zhenyu Zhang
paper_authors.type: user
paper_authors.user: Kyriection
paper_authors._id: 6690b32d21b5557342304705
paper_authors.name: Ajay Jaiswal
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-12T12:34:39.957Z
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paper_authors.fullname: Ajay Jaiswal
paper_authors.type: user
paper_authors.user: Ajay1994
paper_authors._id: 6690b32d21b5557342304706
paper_authors.name: Lu Yin
paper_authors._id: 6690b32d21b5557342304707
paper_authors.name: Shiwei Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-03T11:15:34.304Z
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paper_authors.fullname: Liu
paper_authors.type: user
paper_authors.user: Shiweiliuiiiiiii
paper_authors._id: 6690b32d21b5557342304708
paper_authors.name: Jiawei Zhao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-12T12:34:53.760Z
paper_authors._id: 64dd8355573d067c9e858262
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64dd8355573d067c9e858262/Bu2kFs6-lcYc93A_SE8WU.jpeg
paper_authors.fullname: Jiawei Zhao
paper_authors.type: user
paper_authors.user: jiaweizhao
paper_authors._id: 6690b32d21b5557342304709
paper_authors.name: Yuandong Tian
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-12T12:35:06.852Z
paper_authors._id: 6344cf73ee1504dbcd5bdfe7
paper_authors.avatarUrl: /avatars/6dd2bf1f9c5679e5c8c85d62c9836aac.svg
paper_authors.fullname: Yuandong Tian
paper_authors.type: user
paper_authors.user: tydsh
paper_authors._id: 6690b32d21b555734230470a
paper_authors.name: Zhangyang Wang
paper_title: Q-GaLore: Quantized GaLore with INT4 Projection and Layer-Adaptive
    Low-Rank Gradients
paper_summary: 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.
paper_ai_summary: Q-GaLore reduces memory usage for training large language models by combining quantization and low-rank projection, surpassing GaLore and LoRA in terms of efficiency and performance.
paper_ai_keywords: Training Large Language Models LLMs GaLore Singular Value Decomposition SVD gradient subspace low-rank projection quantization gradient convergence INT4 INT8 stochastic rounding Q-GaLore QLoRA pre-training fine-tuning LLaMA-7B