VeLoRA: Memory Efficient Training using Rank-1 Sub-Token Projections

Roy Miles, Pradyumna Reddy, Ismail Elezi, Jiankang Deng·May 28, 2024

Summary

VeLoRA is a memory-efficient training method for large language models that addresses the issue of memory-intensive training by compressing intermediate activations without compromising performance. It achieves this by dividing tokens into sub-tokens, projecting them onto a low-dimensional subspace during the forward pass, and reconstructing them coarsely during backpropagation. VeLoRA outperforms QLoRA in fine-tuning LLaMA and shows competitive results on the C4 dataset, making it a complementary technique to state-of-the-art parameter-efficient fine-tuning methods. The method is computationally efficient, avoiding expensive operations like SVD, and is compatible with first-order optimizers. It reduces memory footprint, enabling larger models to be trained on devices with limited memory, and can be combined with quantization for further memory reduction. Experiments across various models and tasks demonstrate VeLoRA's effectiveness in improving memory efficiency and accuracy compared to existing methods like GaLore, LoRA, and full fine-tuning.

Key findings

1

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper "VeLoRA: Memory Efficient Training using Rank-1 Sub-Token Projections" aims to address the challenge of memory efficiency in training deep neural networks, particularly large language models (LLMs) . This problem is not entirely new, as the exponential growth in LLM sizes necessitates the development of more efficient and scalable methods to utilize compute power and training data effectively . The paper introduces a novel approach called VeLoRA, which focuses on compressing intermediate activations during forward propagation and reconstructing them during backpropagation to reduce memory requirements while maintaining performance .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis that the proposed framework, VeLoRA, enables the training of networks, including large language models, in a highly memory-efficient manner by compressing intermediate activations during the forward pass and reconstructing them during backpropagation, thereby reducing memory requirements while improving performance .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "VeLoRA: Memory Efficient Training using Rank-1 Sub-Token Projections" introduces a novel framework called VeLoRA that focuses on enabling the training of networks, including large language models, in a highly memory-efficient manner . This approach compresses intermediate activations during the forward pass and reconstructs them coarsely during backpropagation, significantly reducing memory requirements while improving performance . VeLoRA is effective for both moderately-sized vision transformers and large language models, outperforming state-of-the-art methods like LoRA, QLoRA, or GaLore on benchmarks such as VTAB-1K, MMLU, GLUE, and C4 .

The paper addresses the limitations and broader impact of the proposed method. In terms of limitations, the experiments were conducted on Transformer models, and it remains unclear whether the methods can be extended to non-Transformer-based models . Additionally, while VeLoRA is computationally more efficient and reduces GPU memory substantially, training time remains an issue . In terms of broader impact, the paper aims to democratize AI research, especially in large language models, by reducing the memory needed for training, enabling researchers with limited compute resources to contribute to their research . However, the democratization of AI is controversial due to concerns about the potential risks of large language models in the wrong hands .

VeLoRA is distinct from other methods in the field. Unlike some techniques that introduce a substantial computational overhead or are limited in memory savings, VeLoRA significantly reduces memory requirements without introducing large computation overhead . Furthermore, VeLoRA is independent of the underlying optimizer, making it a versatile approach for memory-efficient training .

The paper also discusses Parameter-Efficient Fine-Tuning (PEFT), which focuses on fine-tuning large models with a minimal number of trainable parameters . PEFT involves freezing the original model and augmenting it with adapter modules. VeLoRA complements PEFT methods by providing additional memory efficiency in the fine-tuning process . Additionally, the paper mentions subspace training techniques that optimize model weights within a lower-dimensional subspace, as well as gradient sparsification methods that store only a sparse subset of gradient vector components to reduce memory usage . VeLoRA, a novel framework proposed in the paper, offers several key characteristics and advantages compared to previous methods :

  1. Memory Efficiency: VeLoRA focuses on highly memory-efficient training by compressing intermediate activations during the forward pass and reconstructing them coarsely during backpropagation. This approach significantly reduces GPU memory requirements, making it advantageous for training large language models and moderately-sized vision transformers .

  2. Performance Improvement: VeLoRA outperforms state-of-the-art methods like LoRA, QLoRA, and GaLore on benchmarks such as VTAB-1K, MMLU, GLUE, and C4. It achieves lower perplexity values and higher accuracy, showcasing its effectiveness in enhancing model performance .

  3. Computation Efficiency: Unlike some methods that introduce computational overhead or are limited in memory savings, VeLoRA significantly reduces memory requirements without imposing a large computation burden. This makes it a computationally efficient approach for memory-efficient training .

  4. Versatility: VeLoRA is independent of the underlying optimizer, making it a versatile framework that can be applied across different tasks and models. This flexibility enhances its usability and applicability in various training scenarios .

  5. Complementary to PEFT Methods: VeLoRA complements Parameter-Efficient Fine-Tuning (PEFT) methods by providing additional memory efficiency in the fine-tuning process. It reduces memory requirements while maintaining or improving model performance, making it a valuable addition to the PEFT framework .

  6. Gradient Sparsity and Sub-Token Size Optimization: VeLoRA encourages higher levels of gradient sparsity, which does not hinder model convergence. It optimizes the size of sub-tokens to find a balance between memory compression and model performance, ensuring efficient training without sacrificing accuracy .

  7. Initialisation Strategy: The paper explores various ways of initializing vectors for each group, showcasing the importance of an effective initialization strategy in optimizing model performance during training .

  8. Scalability to Large Language Models: VeLoRA demonstrates its effectiveness in fine-tuning large language models, outperforming methods like LoRA and QLoRA while significantly reducing memory consumption. This scalability to large models highlights its potential for training complex models efficiently .

In summary, VeLoRA stands out for its memory efficiency, performance improvement, computational efficiency, versatility, and compatibility with PEFT methods, making it a promising framework for training networks, especially large language models, in a highly efficient manner.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research works exist in the field of memory-efficient training and parameter-efficient fine-tuning. Noteworthy researchers in this area include R. Anil, S. Borgeaud, Y. Wu, J. Alayrac, J. Yu, R. Soricut, J. Schalkwyk, A. M. Dai, A. Hauth, K. Millican, D. Silver, S. Petrov, M. Johnson, I. Antonoglou, J. Schrittwieser, A. Glaese, J. Chen, E. Pitler, T. P. Lillicrap, A. Lazaridou, O. Firat, J. Molloy, M. Isard, P. R. Barham, T. Hennigan, B. Lee, F. Viola, M. Reynolds, Y. Xu, R. Doherty, E. Collins, C. Meyer, E. Rutherford, E. Moreira, K. Ayoub, M. Goel, G. Tucker, E. Piqueras, M. Krikun, I. Barr, N. Savinov, I. Danihelka, B. Roelofs, A. White, A. Andreassen, T. von Glehn, L. Yagati, M. Kazemi, L. Gonzalez, M. Khalman, J. Sygnowski, N. Shazeer, M. Stern, Y. Sheng, S. Cao, D. Li, C. Hooper, N. Lee, S. Yang, C. Chou, B. Zhu, L. Zheng, K. Keutzer, J. E. Gonzalez, I. Stoica, S. Shi, X. Chu, K. C. Cheung, S. See, and many others .

The key to the solution mentioned in the paper "VeLoRA: Memory Efficient Training using Rank-1 Sub-Token Projections" is the development of a memory-efficient algorithm called VeLoRA. VeLoRA significantly reduces memory requirements during model training without introducing large computation overhead. It achieves this by utilizing a rank-1 decomposition of sub-tokens, which allows for a substantial memory reduction while maintaining performance. Additionally, VeLoRA is designed to be independent of the underlying optimizer, making it a versatile and effective solution for memory-efficient training and parameter-efficient fine-tuning .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the performance of VeLoRA, a novel framework for training networks in a memory-efficient manner, and its complementarity with other Parameter-Efficient Fine-Tuning (PEFT) methods . The experiments were conducted on a variety of benchmarks, including VTAB-1K, MMLU, GLUE, and C4, to showcase the effectiveness of VeLoRA across different tasks and models . The experiments focused on demonstrating how VeLoRA significantly reduces memory requirements while improving performance, particularly in moderately-sized vision transformers and large language models . The paper also highlighted the importance of maintaining a fair comparison by using the authors' provided implementations for adapters and integrating them into the same training framework . Additionally, the experiments aimed to show the impact of different design choices and parameters on memory consumption, with a focus on intermediate activations .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is VTAB-1k benchmark . The code for the study is not explicitly mentioned to be open source in the provided context.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The paper introduces VeLoRA, a framework designed to enable the training of networks, including large language models, in a highly memory-efficient manner . The experiments conducted demonstrate that VeLoRA significantly reduces memory requirements while improving performance, making it effective for both moderately-sized vision transformers and large language models . The method was tested on various benchmarks such as VTAB-1K, MMLU, GLUE, and C4, outperforming state-of-the-art methods like LoRA, QLoRA, and GaLore .

Furthermore, the experiments conducted with VeLoRA in conjunction with other PEFT methods show promising results. VeLoRA was able to lower the memory requirements of SSF by 16% with only a minor degradation in accuracy, reduce Hydra's memory requirements by 7% while improving accuracy, and decrease LoRA's memory requirements by 4% while enhancing accuracy . These results indicate that VeLoRA, when combined with PEFT methods, can lead to improvements in both memory efficiency and performance .

Moreover, the ablation studies conducted in the paper further support the effectiveness of VeLoRA. The experiments on convergence properties showed that VeLoRA and QLoRA improved at the same rate, with VeLoRA outperforming QLoRA by 0.3pp at the end of the first epoch and maintaining this improvement rate throughout training . Additionally, the impact of sub-token size on model performance was analyzed, revealing a sweet spot for sub-token size that is effective in terms of memory and accuracy . These ablation studies provide valuable insights into the factors influencing the performance of VeLoRA and validate its effectiveness in achieving the desired scientific hypotheses .


What are the contributions of this paper?

The contributions of the paper "VeLoRA: Memory Efficient Training using Rank-1 Sub-Token Projections" include:

  • Memory Efficient Training: The paper focuses on memory-efficient training methods using rank-1 sub-token projections .
  • Optimization Techniques: It introduces memory-efficient adaptive optimization techniques .
  • Model Development: The paper presents the development of VeLoRA, a method for high-rank training through low-rank updates .
  • Efficient Parameter Tuning: It proposes a method for full parameter fine-tuning for large language models with limited resources .
  • Algorithm Enhancements: The paper discusses the enhancement of parameter efficiency in LoRA through weight tying .
  • Research on Gradient Sparsification: It contributes to the research on gradient sparsification for communication-efficient distributed optimization .
  • Experimental Setup: The experiments were conducted using NVIDIA GPUs with the fp16 data type, ensuring efficient implementation .

What work can be continued in depth?

Further research in the field of memory-efficient training using rank-1 sub-token projections can be expanded in several directions. One potential avenue is to explore the scalability and efficiency of the proposed VeLoRA method across a wider range of tasks and datasets to assess its generalizability and robustness . Additionally, investigating the adaptability of VeLoRA to different types of deep learning models beyond Transformers, such as CNNs, RNNs, and SSMs, would be valuable to understand its applicability across various network architectures . Furthermore, exploring the extension of VeLoRA to non-Transformer-based models and devising strategies to address training time challenges while maintaining computational efficiency would be a promising area for future research .

Tables

2

Introduction
Background
Memory challenges in training large language models
Importance of memory efficiency in scaling models
Objective
To develop a method that compresses activations without sacrificing performance
Aim to improve memory efficiency and compatibility with existing techniques
Method
Data Compressing Strategy
Token Subdivision
Dividing tokens into smaller sub-tokens
Low-Dimensional Projection
Forward pass: Projecting sub-tokens onto a low-dimensional subspace
Reconstruction during Backpropagation
Coarse reconstruction of sub-tokens for backward pass
Computational Efficiency
Avoidance of expensive operations like SVD
Compatibility with first-order optimizers
Memory Reduction
Impact on memory footprint
Integration with quantization for additional memory savings
Performance Evaluation
Comparison with QLoRA
Fine-tuning LLaMA: VeLoRA's superiority
C4 Dataset Results
Competitive performance with state-of-the-art methods
Experimental Results
Experiments across various models
Analysis of memory efficiency and accuracy improvements
Comparison with GaLore, LoRA, and full fine-tuning
Applications and Benefits
Enabling larger models on memory-constrained devices
Enhancing training scalability and accessibility
Conclusion
Summary of VeLoRA's contributions
Potential future directions and implications for the field
Basic info
papers
computer vision and pattern recognition
artificial intelligence
Advanced features
Insights
What is VeLoRA designed to address in the context of training large language models?
What are the performance improvements VeLoRA shows compared to QLoRA in fine-tuning LLaMA?
How does VeLoRA contribute to memory efficiency in training large models, and what are its compatibility options?
How does VeLoRA compress intermediate activations during the training process?

VeLoRA: Memory Efficient Training using Rank-1 Sub-Token Projections

Roy Miles, Pradyumna Reddy, Ismail Elezi, Jiankang Deng·May 28, 2024

Summary

VeLoRA is a memory-efficient training method for large language models that addresses the issue of memory-intensive training by compressing intermediate activations without compromising performance. It achieves this by dividing tokens into sub-tokens, projecting them onto a low-dimensional subspace during the forward pass, and reconstructing them coarsely during backpropagation. VeLoRA outperforms QLoRA in fine-tuning LLaMA and shows competitive results on the C4 dataset, making it a complementary technique to state-of-the-art parameter-efficient fine-tuning methods. The method is computationally efficient, avoiding expensive operations like SVD, and is compatible with first-order optimizers. It reduces memory footprint, enabling larger models to be trained on devices with limited memory, and can be combined with quantization for further memory reduction. Experiments across various models and tasks demonstrate VeLoRA's effectiveness in improving memory efficiency and accuracy compared to existing methods like GaLore, LoRA, and full fine-tuning.
Mind map
Competitive performance with state-of-the-art methods
Fine-tuning LLaMA: VeLoRA's superiority
Coarse reconstruction of sub-tokens for backward pass
Forward pass: Projecting sub-tokens onto a low-dimensional subspace
Dividing tokens into smaller sub-tokens
Comparison with GaLore, LoRA, and full fine-tuning
Analysis of memory efficiency and accuracy improvements
Experiments across various models
C4 Dataset Results
Comparison with QLoRA
Integration with quantization for additional memory savings
Impact on memory footprint
Compatibility with first-order optimizers
Avoidance of expensive operations like SVD
Reconstruction during Backpropagation
Low-Dimensional Projection
Token Subdivision
Aim to improve memory efficiency and compatibility with existing techniques
To develop a method that compresses activations without sacrificing performance
Importance of memory efficiency in scaling models
Memory challenges in training large language models
Potential future directions and implications for the field
Summary of VeLoRA's contributions
Enhancing training scalability and accessibility
Enabling larger models on memory-constrained devices
Experimental Results
Performance Evaluation
Memory Reduction
Computational Efficiency
Data Compressing Strategy
Objective
Background
Conclusion
Applications and Benefits
Method
Introduction
Outline
Introduction
Background
Memory challenges in training large language models
Importance of memory efficiency in scaling models
Objective
To develop a method that compresses activations without sacrificing performance
Aim to improve memory efficiency and compatibility with existing techniques
Method
Data Compressing Strategy
Token Subdivision
Dividing tokens into smaller sub-tokens
Low-Dimensional Projection
Forward pass: Projecting sub-tokens onto a low-dimensional subspace
Reconstruction during Backpropagation
Coarse reconstruction of sub-tokens for backward pass
Computational Efficiency
Avoidance of expensive operations like SVD
Compatibility with first-order optimizers
Memory Reduction
Impact on memory footprint
Integration with quantization for additional memory savings
Performance Evaluation
Comparison with QLoRA
Fine-tuning LLaMA: VeLoRA's superiority
C4 Dataset Results
Competitive performance with state-of-the-art methods
Experimental Results
Experiments across various models
Analysis of memory efficiency and accuracy improvements
Comparison with GaLore, LoRA, and full fine-tuning
Applications and Benefits
Enabling larger models on memory-constrained devices
Enhancing training scalability and accessibility
Conclusion
Summary of VeLoRA's contributions
Potential future directions and implications for the field
Key findings
1

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper "VeLoRA: Memory Efficient Training using Rank-1 Sub-Token Projections" aims to address the challenge of memory efficiency in training deep neural networks, particularly large language models (LLMs) . This problem is not entirely new, as the exponential growth in LLM sizes necessitates the development of more efficient and scalable methods to utilize compute power and training data effectively . The paper introduces a novel approach called VeLoRA, which focuses on compressing intermediate activations during forward propagation and reconstructing them during backpropagation to reduce memory requirements while maintaining performance .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis that the proposed framework, VeLoRA, enables the training of networks, including large language models, in a highly memory-efficient manner by compressing intermediate activations during the forward pass and reconstructing them during backpropagation, thereby reducing memory requirements while improving performance .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "VeLoRA: Memory Efficient Training using Rank-1 Sub-Token Projections" introduces a novel framework called VeLoRA that focuses on enabling the training of networks, including large language models, in a highly memory-efficient manner . This approach compresses intermediate activations during the forward pass and reconstructs them coarsely during backpropagation, significantly reducing memory requirements while improving performance . VeLoRA is effective for both moderately-sized vision transformers and large language models, outperforming state-of-the-art methods like LoRA, QLoRA, or GaLore on benchmarks such as VTAB-1K, MMLU, GLUE, and C4 .

The paper addresses the limitations and broader impact of the proposed method. In terms of limitations, the experiments were conducted on Transformer models, and it remains unclear whether the methods can be extended to non-Transformer-based models . Additionally, while VeLoRA is computationally more efficient and reduces GPU memory substantially, training time remains an issue . In terms of broader impact, the paper aims to democratize AI research, especially in large language models, by reducing the memory needed for training, enabling researchers with limited compute resources to contribute to their research . However, the democratization of AI is controversial due to concerns about the potential risks of large language models in the wrong hands .

VeLoRA is distinct from other methods in the field. Unlike some techniques that introduce a substantial computational overhead or are limited in memory savings, VeLoRA significantly reduces memory requirements without introducing large computation overhead . Furthermore, VeLoRA is independent of the underlying optimizer, making it a versatile approach for memory-efficient training .

The paper also discusses Parameter-Efficient Fine-Tuning (PEFT), which focuses on fine-tuning large models with a minimal number of trainable parameters . PEFT involves freezing the original model and augmenting it with adapter modules. VeLoRA complements PEFT methods by providing additional memory efficiency in the fine-tuning process . Additionally, the paper mentions subspace training techniques that optimize model weights within a lower-dimensional subspace, as well as gradient sparsification methods that store only a sparse subset of gradient vector components to reduce memory usage . VeLoRA, a novel framework proposed in the paper, offers several key characteristics and advantages compared to previous methods :

  1. Memory Efficiency: VeLoRA focuses on highly memory-efficient training by compressing intermediate activations during the forward pass and reconstructing them coarsely during backpropagation. This approach significantly reduces GPU memory requirements, making it advantageous for training large language models and moderately-sized vision transformers .

  2. Performance Improvement: VeLoRA outperforms state-of-the-art methods like LoRA, QLoRA, and GaLore on benchmarks such as VTAB-1K, MMLU, GLUE, and C4. It achieves lower perplexity values and higher accuracy, showcasing its effectiveness in enhancing model performance .

  3. Computation Efficiency: Unlike some methods that introduce computational overhead or are limited in memory savings, VeLoRA significantly reduces memory requirements without imposing a large computation burden. This makes it a computationally efficient approach for memory-efficient training .

  4. Versatility: VeLoRA is independent of the underlying optimizer, making it a versatile framework that can be applied across different tasks and models. This flexibility enhances its usability and applicability in various training scenarios .

  5. Complementary to PEFT Methods: VeLoRA complements Parameter-Efficient Fine-Tuning (PEFT) methods by providing additional memory efficiency in the fine-tuning process. It reduces memory requirements while maintaining or improving model performance, making it a valuable addition to the PEFT framework .

  6. Gradient Sparsity and Sub-Token Size Optimization: VeLoRA encourages higher levels of gradient sparsity, which does not hinder model convergence. It optimizes the size of sub-tokens to find a balance between memory compression and model performance, ensuring efficient training without sacrificing accuracy .

  7. Initialisation Strategy: The paper explores various ways of initializing vectors for each group, showcasing the importance of an effective initialization strategy in optimizing model performance during training .

  8. Scalability to Large Language Models: VeLoRA demonstrates its effectiveness in fine-tuning large language models, outperforming methods like LoRA and QLoRA while significantly reducing memory consumption. This scalability to large models highlights its potential for training complex models efficiently .

In summary, VeLoRA stands out for its memory efficiency, performance improvement, computational efficiency, versatility, and compatibility with PEFT methods, making it a promising framework for training networks, especially large language models, in a highly efficient manner.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research works exist in the field of memory-efficient training and parameter-efficient fine-tuning. Noteworthy researchers in this area include R. Anil, S. Borgeaud, Y. Wu, J. Alayrac, J. Yu, R. Soricut, J. Schalkwyk, A. M. Dai, A. Hauth, K. Millican, D. Silver, S. Petrov, M. Johnson, I. Antonoglou, J. Schrittwieser, A. Glaese, J. Chen, E. Pitler, T. P. Lillicrap, A. Lazaridou, O. Firat, J. Molloy, M. Isard, P. R. Barham, T. Hennigan, B. Lee, F. Viola, M. Reynolds, Y. Xu, R. Doherty, E. Collins, C. Meyer, E. Rutherford, E. Moreira, K. Ayoub, M. Goel, G. Tucker, E. Piqueras, M. Krikun, I. Barr, N. Savinov, I. Danihelka, B. Roelofs, A. White, A. Andreassen, T. von Glehn, L. Yagati, M. Kazemi, L. Gonzalez, M. Khalman, J. Sygnowski, N. Shazeer, M. Stern, Y. Sheng, S. Cao, D. Li, C. Hooper, N. Lee, S. Yang, C. Chou, B. Zhu, L. Zheng, K. Keutzer, J. E. Gonzalez, I. Stoica, S. Shi, X. Chu, K. C. Cheung, S. See, and many others .

The key to the solution mentioned in the paper "VeLoRA: Memory Efficient Training using Rank-1 Sub-Token Projections" is the development of a memory-efficient algorithm called VeLoRA. VeLoRA significantly reduces memory requirements during model training without introducing large computation overhead. It achieves this by utilizing a rank-1 decomposition of sub-tokens, which allows for a substantial memory reduction while maintaining performance. Additionally, VeLoRA is designed to be independent of the underlying optimizer, making it a versatile and effective solution for memory-efficient training and parameter-efficient fine-tuning .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the performance of VeLoRA, a novel framework for training networks in a memory-efficient manner, and its complementarity with other Parameter-Efficient Fine-Tuning (PEFT) methods . The experiments were conducted on a variety of benchmarks, including VTAB-1K, MMLU, GLUE, and C4, to showcase the effectiveness of VeLoRA across different tasks and models . The experiments focused on demonstrating how VeLoRA significantly reduces memory requirements while improving performance, particularly in moderately-sized vision transformers and large language models . The paper also highlighted the importance of maintaining a fair comparison by using the authors' provided implementations for adapters and integrating them into the same training framework . Additionally, the experiments aimed to show the impact of different design choices and parameters on memory consumption, with a focus on intermediate activations .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is VTAB-1k benchmark . The code for the study is not explicitly mentioned to be open source in the provided context.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The paper introduces VeLoRA, a framework designed to enable the training of networks, including large language models, in a highly memory-efficient manner . The experiments conducted demonstrate that VeLoRA significantly reduces memory requirements while improving performance, making it effective for both moderately-sized vision transformers and large language models . The method was tested on various benchmarks such as VTAB-1K, MMLU, GLUE, and C4, outperforming state-of-the-art methods like LoRA, QLoRA, and GaLore .

Furthermore, the experiments conducted with VeLoRA in conjunction with other PEFT methods show promising results. VeLoRA was able to lower the memory requirements of SSF by 16% with only a minor degradation in accuracy, reduce Hydra's memory requirements by 7% while improving accuracy, and decrease LoRA's memory requirements by 4% while enhancing accuracy . These results indicate that VeLoRA, when combined with PEFT methods, can lead to improvements in both memory efficiency and performance .

Moreover, the ablation studies conducted in the paper further support the effectiveness of VeLoRA. The experiments on convergence properties showed that VeLoRA and QLoRA improved at the same rate, with VeLoRA outperforming QLoRA by 0.3pp at the end of the first epoch and maintaining this improvement rate throughout training . Additionally, the impact of sub-token size on model performance was analyzed, revealing a sweet spot for sub-token size that is effective in terms of memory and accuracy . These ablation studies provide valuable insights into the factors influencing the performance of VeLoRA and validate its effectiveness in achieving the desired scientific hypotheses .


What are the contributions of this paper?

The contributions of the paper "VeLoRA: Memory Efficient Training using Rank-1 Sub-Token Projections" include:

  • Memory Efficient Training: The paper focuses on memory-efficient training methods using rank-1 sub-token projections .
  • Optimization Techniques: It introduces memory-efficient adaptive optimization techniques .
  • Model Development: The paper presents the development of VeLoRA, a method for high-rank training through low-rank updates .
  • Efficient Parameter Tuning: It proposes a method for full parameter fine-tuning for large language models with limited resources .
  • Algorithm Enhancements: The paper discusses the enhancement of parameter efficiency in LoRA through weight tying .
  • Research on Gradient Sparsification: It contributes to the research on gradient sparsification for communication-efficient distributed optimization .
  • Experimental Setup: The experiments were conducted using NVIDIA GPUs with the fp16 data type, ensuring efficient implementation .

What work can be continued in depth?

Further research in the field of memory-efficient training using rank-1 sub-token projections can be expanded in several directions. One potential avenue is to explore the scalability and efficiency of the proposed VeLoRA method across a wider range of tasks and datasets to assess its generalizability and robustness . Additionally, investigating the adaptability of VeLoRA to different types of deep learning models beyond Transformers, such as CNNs, RNNs, and SSMs, would be valuable to understand its applicability across various network architectures . Furthermore, exploring the extension of VeLoRA to non-Transformer-based models and devising strategies to address training time challenges while maintaining computational efficiency would be a promising area for future research .

Tables
2
Scan the QR code to ask more questions about the paper
© 2025 Powerdrill. All rights reserved.