Adam-mini: Use Fewer Learning Rates To Gain More

Yushun Zhang, Congliang Chen, Ziniu Li, Tian Ding, Chenwei Wu, Yinyu Ye, Zhi-Quan Luo, Ruoyu Sun·June 24, 2024

Summary

Adam-mini is a memory-efficient optimizer that reduces memory usage by up to 50% compared to AdamW, without compromising performance. It leverages the Hessian structure of Transformer models to assign fewer, high-quality learning rates per block, resulting in fewer resources. The optimizer is effective across various language models for pre-training, fine-tuning, and RLHF, improving throughput and alleviating communication overhead. It outperforms or matches AdamW in tasks like Llama2-7B's 49.6% higher throughput on A800 GPUs. The study also explores the impact of different learning rate methods and Hessian structures on optimization, with Adam-mini showing promise in reducing memory demands for large-scale models.

Key findings

15

Paper digest

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Tables

6

Introduction
Background
Memory efficiency in deep learning
Challenges with AdamW and large-scale models
Objective
To develop and evaluate Adam-mini
Reduce memory usage without sacrificing performance
Investigate Hessian structure and learning rate assignment
Method
Data Collection
Model Architecture Analysis
Focus on Transformer models
Selection of benchmark models (Llama2-7B, etc.)
Performance Evaluation
Comparison with AdamW
Throughput and communication overhead measurements
A800 GPU experiments
Optimization Techniques
Hessian Structure Exploitation
Understanding the role of Hessian in optimization
Block-wise learning rate assignment
Learning Rate Methods
Different LR schedules tested
Impact on model convergence and efficiency
Memory Efficiency Study
Memory usage reduction in practice
Memory footprint analysis during pre-training, fine-tuning, and RLHF
Results and Discussion
Performance Benchmarks
Quantitative comparison of Adam-mini vs. AdamW
Improved throughput and efficiency figures
Memory Savings
Percentage memory reduction achieved by Adam-mini
Real-world implications for resource-constrained environments
Optimization Insights
Lessons learned from the study
Advantages of using Adam-mini for large-scale models
Conclusion
Summary of findings
Practical recommendations for using Adam-mini
Future research directions in memory-efficient optimizers
References
Cited works on AdamW, Hessian optimization, and Transformer models
Basic info
papers
machine learning
artificial intelligence
Advanced features
Insights
What is the primary mechanism of Adam-mini in reducing memory consumption?
In which scenarios does Adam-mini demonstrate improved throughput and reduced communication overhead?
Can you provide an example of a task where Adam-mini outperforms AdamW, as mentioned in the user input?
How does Adam-mini compare to AdamW in terms of memory usage?

Adam-mini: Use Fewer Learning Rates To Gain More

Yushun Zhang, Congliang Chen, Ziniu Li, Tian Ding, Chenwei Wu, Yinyu Ye, Zhi-Quan Luo, Ruoyu Sun·June 24, 2024

Summary

Adam-mini is a memory-efficient optimizer that reduces memory usage by up to 50% compared to AdamW, without compromising performance. It leverages the Hessian structure of Transformer models to assign fewer, high-quality learning rates per block, resulting in fewer resources. The optimizer is effective across various language models for pre-training, fine-tuning, and RLHF, improving throughput and alleviating communication overhead. It outperforms or matches AdamW in tasks like Llama2-7B's 49.6% higher throughput on A800 GPUs. The study also explores the impact of different learning rate methods and Hessian structures on optimization, with Adam-mini showing promise in reducing memory demands for large-scale models.
Mind map
Impact on model convergence and efficiency
Different LR schedules tested
Block-wise learning rate assignment
Understanding the role of Hessian in optimization
Selection of benchmark models (Llama2-7B, etc.)
Focus on Transformer models
Advantages of using Adam-mini for large-scale models
Lessons learned from the study
Real-world implications for resource-constrained environments
Percentage memory reduction achieved by Adam-mini
Improved throughput and efficiency figures
Quantitative comparison of Adam-mini vs. AdamW
Memory footprint analysis during pre-training, fine-tuning, and RLHF
Memory usage reduction in practice
Learning Rate Methods
Hessian Structure Exploitation
A800 GPU experiments
Throughput and communication overhead measurements
Comparison with AdamW
Model Architecture Analysis
Investigate Hessian structure and learning rate assignment
Reduce memory usage without sacrificing performance
To develop and evaluate Adam-mini
Challenges with AdamW and large-scale models
Memory efficiency in deep learning
Cited works on AdamW, Hessian optimization, and Transformer models
Future research directions in memory-efficient optimizers
Practical recommendations for using Adam-mini
Summary of findings
Optimization Insights
Memory Savings
Performance Benchmarks
Memory Efficiency Study
Optimization Techniques
Performance Evaluation
Data Collection
Objective
Background
References
Conclusion
Results and Discussion
Method
Introduction
Outline
Introduction
Background
Memory efficiency in deep learning
Challenges with AdamW and large-scale models
Objective
To develop and evaluate Adam-mini
Reduce memory usage without sacrificing performance
Investigate Hessian structure and learning rate assignment
Method
Data Collection
Model Architecture Analysis
Focus on Transformer models
Selection of benchmark models (Llama2-7B, etc.)
Performance Evaluation
Comparison with AdamW
Throughput and communication overhead measurements
A800 GPU experiments
Optimization Techniques
Hessian Structure Exploitation
Understanding the role of Hessian in optimization
Block-wise learning rate assignment
Learning Rate Methods
Different LR schedules tested
Impact on model convergence and efficiency
Memory Efficiency Study
Memory usage reduction in practice
Memory footprint analysis during pre-training, fine-tuning, and RLHF
Results and Discussion
Performance Benchmarks
Quantitative comparison of Adam-mini vs. AdamW
Improved throughput and efficiency figures
Memory Savings
Percentage memory reduction achieved by Adam-mini
Real-world implications for resource-constrained environments
Optimization Insights
Lessons learned from the study
Advantages of using Adam-mini for large-scale models
Conclusion
Summary of findings
Practical recommendations for using Adam-mini
Future research directions in memory-efficient optimizers
References
Cited works on AdamW, Hessian optimization, and Transformer models
Key findings
15

Paper digest

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

Could you please provide more specific information or context about the paper you are referring to? This will help me better understand the problem it aims to solve and whether it is a new problem or not.


What scientific hypothesis does this paper seek to validate?

I would be happy to help you with that. Please provide me with the title of the paper or a brief summary so I can understand the scientific hypothesis it aims to validate.


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

I appreciate your question, but I need more specific details or context about the paper you are referring to in order to provide a detailed analysis of the new ideas, methods, or models proposed in it. Could you please provide more information or share some key points from the paper so that I can assist you better? I appreciate your question, but I need more specific details or context about the paper you are referring to in order to provide a detailed analysis of the characteristics and advantages of the new methods compared to previous ones. Could you please provide more information or share some key points from the paper so that I can assist you better?


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?

Could you please specify the topic or field you are referring to so I can provide you with more accurate information?


How were the experiments in the paper designed?

To provide you with a detailed answer, I would need more specific information about the paper you are referring to. Could you please provide me with the title of the paper or some key details about the experiments so I can assist you better?


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

To provide you with accurate information, I need more details about the specific dataset and code you are referring to for quantitative evaluation. Please specify the dataset and code you are interested in so I can assist you better.


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

To provide an accurate analysis, I would need more specific information about the paper, such as the title, authors, research question, methodology, and key findings. Without these details, it is challenging to assess the quality of support for the scientific hypotheses presented in the paper. If you can provide more context or details, I would be happy to help analyze the experiments and results for you.


What are the contributions of this paper?

To provide a more accurate answer, could you please specify which paper you are referring to?


What work can be continued in depth?

Work that can be continued in depth typically involves projects or tasks that require further analysis, research, or development. This could include:

  1. Research projects that require more data collection, analysis, and interpretation.
  2. Complex problem-solving tasks that need further exploration and experimentation.
  3. Creative projects that can be expanded upon with more ideas and iterations.
  4. Skill development activities that require continuous practice and improvement.
  5. Long-term goals that need consistent effort and dedication to achieve.

If you have a specific area of work in mind, feel free to provide more details so I can give you a more tailored response.

Tables
6
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