AdaLomo: Low-memory Optimization with Adaptive Learning Rate

• URL: http://arxiv.org/abs/2310.10195v3
• Date: Thu, 6 Jun 2024 13:22:25 GMT
• Title: AdaLomo: Low-memory Optimization with Adaptive Learning Rate
• Authors: Kai Lv, Hang Yan, Qipeng Guo, Haijun Lv, Xipeng Qiu,
• Abstract summary: We introduce low-memory optimization with adaptive learning rate (AdaLomo) for large language models. AdaLomo results on par with AdamW, while significantly reducing memory requirements, thereby lowering the hardware barrier to training large language models.
• Score: 59.64965955386855
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models have achieved remarkable success, but their extensive parameter size necessitates substantial memory for training, thereby setting a high threshold. While the recently proposed low-memory optimization (LOMO) reduces memory footprint, its optimization technique, akin to stochastic gradient descent, is sensitive to hyper-parameters and exhibits suboptimal convergence, failing to match the performance of the prevailing optimizer for large language models, AdamW. Through empirical analysis of the Adam optimizer, we found that, compared to momentum, the adaptive learning rate is more critical for bridging the gap. Building on this insight, we introduce the low-memory optimization with adaptive learning rate (AdaLomo), which offers an adaptive learning rate for each parameter. To maintain memory efficiency, we employ non-negative matrix factorization for the second-order moment estimation in the optimizer state. Additionally, we suggest the use of a grouped update normalization to stabilize convergence. Our experiments with instruction-tuning and further pre-training demonstrate that AdaLomo achieves results on par with AdamW, while significantly reducing memory requirements, thereby lowering the hardware barrier to training large language models. The code is accessible at https://github.com/OpenLMLab/LOMO.

Related papers

• LDAdam: Adaptive Optimization from Low-Dimensional Gradient Statistics [37.21593513802284]
  We introduce LDAdam, a memory-efficient gradient for training large models. We show that LDAdam allows for accurate and efficient fine-tuning and pre-training of language models.
  arXiv  Detail & Related papers  (2024-10-21T15:31:06Z)
• Simultaneous Computation and Memory Efficient Zeroth-Order Optimizer for Fine-Tuning Large Language Models [33.911521719528686]
  Fine-tuning is powerful for adapting large language models to downstream tasks, but it often results in huge memory usages. A promising approach is using Zeroth-Order (ZO) gradients, which estimates to replace First-Order (FO) gradients. We introduce a novel layer-wise sparse computation and memory efficient ZO, named LeZO.
  arXiv  Detail & Related papers  (2024-10-13T12:47:37Z)
• Zeroth-Order Fine-Tuning of LLMs in Random Subspaces [66.27334633749734]
  As language models grow in size, memory demands for backpropagation increase. Zeroth-order (ZOZO) optimization methods offer a memory-efficient alternative. We show that SubZero enhances fine-tuning and achieves faster results compared to standard ZOZO approaches.
  arXiv  Detail & Related papers  (2024-10-11T17:01:43Z)
• Surge Phenomenon in Optimal Learning Rate and Batch Size Scaling [27.058009599819012]
  We study the connection between optimal learning rates and batch sizes for Adam styles. We prove that the optimal learning rate first rises and then falls as the batch size increases.
  arXiv  Detail & Related papers  (2024-05-23T13:52:36Z)
• Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark [166.40879020706151]
  This paper proposes a shift towards BP-free, zeroth-order (ZO) optimization as a solution for reducing memory costs during fine-tuning. Unlike traditional ZO-SGD methods, our work expands the exploration to a wider array of ZO optimization techniques. Our study unveils previously overlooked optimization principles, highlighting the importance of task alignment, the role of the forward gradient method, and the balance between algorithm complexity and fine-tuning performance.
  arXiv  Detail & Related papers  (2024-02-18T14:08:48Z)
• CAME: Confidence-guided Adaptive Memory Efficient Optimization [20.009302737137787]
  Adaptive gradient methods have demonstrated excellent performance in the training of large language models. The need for maintaining second-moment estimates requires maintaining a high cost of extra memory overheads. Several memory-efficients have been proposed to obtain a drastic reduction in auxiliary memory usage, but with a performance penalty. We propose CAME to simultaneously achieve two goals: fast convergence as in traditional adaptive methods, and low memory usage as in memory-efficient methods.
  arXiv  Detail & Related papers  (2023-07-05T06:05:36Z)
• EMO: Episodic Memory Optimization for Few-Shot Meta-Learning [69.50380510879697]
  episodic memory optimization for meta-learning, we call EMO, is inspired by the human ability to recall past learning experiences from the brain's memory. EMO nudges parameter updates in the right direction, even when the gradients provided by a limited number of examples are uninformative. EMO scales well with most few-shot classification benchmarks and improves the performance of optimization-based meta-learning methods.
  arXiv  Detail & Related papers  (2023-06-08T13:39:08Z)
• 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)
• VeLO: Training Versatile Learned Optimizers by Scaling Up [67.90237498659397]
  We leverage the same scaling approach behind the success of deep learning to learn versatiles. We train an ingest for deep learning which is itself a small neural network that ingests and outputs parameter updates. We open source our learned, meta-training code, the associated train test data, and an extensive benchmark suite with baselines at velo-code.io.
  arXiv  Detail & Related papers  (2022-11-17T18:39:07Z)
• Practical tradeoffs between memory, compute, and performance in learned optimizers [46.04132441790654]
  We identify and quantify the memory, compute, and performance trade-offs for many learned and hand-designeds features. We leverage our analysis to construct a learned is both faster and more efficient than previous work.
  arXiv  Detail & Related papers  (2022-03-22T16:36:36Z)

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.