CoLA: Compute-Efficient Pre-Training of LLMs via Low-Rank Activation

• URL: http://arxiv.org/abs/2502.10940v1
• Date: Sun, 16 Feb 2025 01:05:16 GMT
• Title: CoLA: Compute-Efficient Pre-Training of LLMs via Low-Rank Activation
• Authors: Ziyue Liu, Ruijie Zhang, Zhengyang Wang, Zi Yang, Paul Hovland, Bogdan Nicolae, Franck Cappello, Zheng Zhang,
• Abstract summary: We introduce CoLA and its memory-efficient implementation, CoLA-M. We leverage the low-rank structure observed widely in model activations to reduce model size, boost model capacity and training efficiency. Experiments on LLaMA models with 60 million to 7 billion parameters show that CoLA reduces the computing cost by $bf 2pmbtimes$ and improves training throughput by $bf 1.86pmbtimes$ while maintaining full-rank level performance.
• Score: 17.807249890437767
• License:
• Abstract: Large language models (LLMs) are revolutionizing many science and engineering fields. However, their huge model sizes impose extremely demanding needs of computational resources in the pre-training stage. Although low-rank factorizations can reduce model parameters, their direct application in LLM pre-training often lead to non-negligible performance loss. To address this fundamental challenge, we introduce CoLA and its memory-efficient implementation, CoLA-M. We leverage the low-rank structure observed widely in model activations, enforcing non-linear transformations between factorized weight matrices to reduce model size, boost model capacity and training efficiency. Experiments on LLaMA models with 60 million to 7 billion parameters show that CoLA reduces the computing cost by $\bf 2\pmb{\times}$ and improves training throughput by $\bf 1.86\pmb{\times}$ while maintaining full-rank level performance. CoLA-M further squeezes memory cost without sacrificing throughput, offering a pre-training approach with collectively superior parameter, computing, and memory efficiency. The LLMs produced are also $\bf 2\pmb{\times}$ smaller, enabling faster inference with lower memory cost on resource-constrained platforms

Related papers

• LESA: Learnable LLM Layer Scaling-Up [57.0510934286449]
  Training Large Language Models (LLMs) from scratch requires immense computational resources, making it prohibitively expensive. Model scaling-up offers a promising solution by leveraging the parameters of smaller models to create larger ones. We propose textbfLESA, a novel learnable method for depth scaling-up.
  arXiv  Detail & Related papers  (2025-02-19T14:58:48Z)
• Save It All: Enabling Full Parameter Tuning for Federated Large Language Models via Cycle Block Gradient Descent [15.463595798992621]
  Large language models (LLMs) have revolutionized the deep learning paradigm, yielding impressive results across a wide array of tasks. Existing solutions make the unrealistic assumption that the entire model is exchanged for training. We introduce a novel method for the efficient training and fine-tuning of LLMs in FL, with minimal resource consumption.
  arXiv  Detail & Related papers  (2024-06-17T03:49:44Z)
• ShiftAddLLM: Accelerating Pretrained LLMs via Post-Training Multiplication-Less Reparameterization [13.622268474310918]
  ShiftAddLLM is an efficient multiplication-free model for large language models. It achieves perplexity improvements of 5.6 and 22.7 points at comparable or lower latency. Experiments on five LLM families and eight tasks consistently validate the effectiveness of ShiftAddLLM.
  arXiv  Detail & Related papers  (2024-06-10T02:47:55Z)
• SLTrain: a sparse plus low-rank approach for parameter and memory efficient pretraining [39.56934385513862]
  Training large language models (LLMs) from scratch requires significant computational power and extensive memory capacity. Recent studies have explored low-rank structures on weights for efficient fine-tuning in terms of parameters and memory. We propose to parameterize the weights as a sum of low-rank and sparse matrices for pretraining, which we call SLTrain.
  arXiv  Detail & Related papers  (2024-06-04T11:14:21Z)
• MobiLlama: Towards Accurate and Lightweight Fully Transparent GPT [87.4910758026772]
  "Bigger the better" has been the predominant trend in recent Large Language Models (LLMs) development. This paper explores the "less is more" paradigm by addressing the challenge of designing accurate yet efficient Small Language Models (SLMs) for resource constrained devices.
  arXiv  Detail & Related papers  (2024-02-26T18:59:03Z)
• FinGPT-HPC: Efficient Pretraining and Finetuning Large Language Models for Financial Applications with High-Performance Computing [10.47214968497857]
  We present high-performance methods that exploit low-rank structures to pretrain and finetune large language models. Our methods achieve a speedup of 1.3X and a model compression ratio of 2.64X for pretaining without accuracy drop. For finetuning, our methods achieve an average accuracy increase of 6.3% and 24.0% in general tasks and financial tasks.
  arXiv  Detail & Related papers  (2024-02-21T05:03:17Z)
• BiLLM: Pushing the Limit of Post-Training Quantization for LLMs [53.31402059062365]
  BiLLM is a groundbreaking 1-bit post-training quantization scheme tailored for pretrained large language models. It achieves for the first time high-accuracy inference (e.g. 8.41 perplexity on LLaMA2-70B) with only 1.08-bit weights across various LLMs families.
  arXiv  Detail & Related papers  (2024-02-06T09:26:34Z)
• Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMs [67.38165028487242]
  We introduce Dynamic Sparse No Training (DSnoT), a training-free fine-tuning approach to fine-tune large language models (LLMs) Inspired by the Dynamic Sparse Training, DSnoT minimizes the reconstruction error between the dense and sparse LLMs. Our paper offers fresh insights into how to fine-tune sparse LLMs in an efficient training-free manner and open new venues to scale the great potential of sparsity to LLMs.
  arXiv  Detail & Related papers  (2023-10-13T07:38:52Z)
• LLM-Pruner: On the Structural Pruning of Large Language Models [65.02607075556742]
  Large language models (LLMs) have shown remarkable capabilities in language understanding and generation. We tackle the compression of LLMs within the bound of two constraints: being task-agnostic and minimizing the reliance on the original training dataset. Our method, named LLM-Pruner, adopts structural pruning that selectively removes non-critical coupled structures.
  arXiv  Detail & Related papers  (2023-05-19T12:10:53Z)
• CPM-2: Large-scale Cost-effective Pre-trained Language Models [71.59893315671997]
  We present a suite of cost-effective techniques for the use of PLMs to deal with the efficiency issues of pre-training, fine-tuning, and inference. We introduce knowledge inheritance to accelerate the pre-training process by exploiting existing PLMs instead of training models from scratch. We implement a new inference toolkit, namely InfMoE, for using large-scale PLMs with limited computational resources.
  arXiv  Detail & Related papers  (2021-06-20T15:43:54Z)

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.