SUBLLM: A Novel Efficient Architecture with Token Sequence Subsampling for LLM

Quandong Wang, Yuxuan Yuan, Xiaoyu Yang, Ruike Zhang, Kang Zhao, Wei Liu, Jian Luan, Daniel Povey, Bin Wang·June 03, 2024

Summary

SUBLLM is a novel architecture for large language models that addresses efficiency challenges by incorporating subsampling, upsampling, and bypass modules. It dynamically allocates resources based on token importance, reducing computational costs and improving convergence. The architecture outperforms LLaMA in terms of training and inference speed, memory usage, and maintains competitive few-shot performance. SUBLLM achieves speed-ups of 26% and 37% in training and inference, respectively, with memory reductions and improved computational efficiency. The design is based on the U-Net architecture and balances sequence compression and restoration for efficient sequential generation. Experiments show that SUBLLM maintains performance while enhancing memory management and speed, making it an attractive choice for large-scale text processing and resource-constrained environments.

Key findings

1

Paper digest

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

The paper "SUBLLM: A Novel Efficient Architecture with Token Sequence Subsampling for LLM" aims to address the challenge of efficiency in training and inference processes of Large Language Models (LLMs) by proposing a new architecture called SUBLLM, which incorporates subsampling, upsampling, and a bypass module to allocate resources dynamically, reduce computational costs, accelerate model convergence, and enhance performance . This problem of efficiency in LLMs is not new, as previous studies have also focused on compressing LLMs through techniques like knowledge distillation, quantization, and pruning to reduce memory requirements and improve inference speed . The SUBLLM paper introduces innovative methods to tackle this ongoing challenge in the field of natural language processing.


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the scientific hypothesis that for language models, the semantics at equivalent depths should be similar . The study compares the attention distribution within the pre-subsampling block and the index distribution after the first subsampling in the SUBLLM model to analyze the preservation of important semantic information through the subsampling process .


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

The paper "SUBLLM: A Novel Efficient Architecture with Token Sequence Subsampling for LLM" proposes several innovative ideas, methods, and models to enhance model efficiency and performance . Here are the key contributions outlined in the paper:

  1. Novel Architecture - SUBLLM: The paper introduces a novel architecture called SUBLLM, which integrates subsampling, upsampling, and a bypass module. This design dynamically allocates resources to important tokens, reducing computational costs related to token redundancy and accelerating model convergence through the bypass connection .

  2. Token Sequence Subsampling Approach: The paper presents a new approach to token sequence subsampling that effectively measures token importance scores and controls the distribution of score values to achieve the desired subsampling retention ratio during inference .

  3. Experimental Results: Experimental results demonstrate that SUBLLM achieves a 26% speed-up on training and a 37% speed-up on inference compared to the LLaMA model. This speed-up is accompanied by a significant reduction in memory cost while maintaining performance .

  4. Integration with Existing Techniques: The SUBLLM architecture is not mutually exclusive with existing inference acceleration methods. It can leverage strategies such as knowledge distillation, quantization, and pruning to expedite the inference process and reduce memory cost .

  5. Dynamic Resource Allocation: SUBLLM dynamically allocates computational resources for tokens based on their importance, inspired by the finding that some tokens are more crucial than others in natural language. This selective removal of less important tokens significantly reduces computational demands, enhances training stability, accelerates convergence, and potentially improves modeling outcomes .

In summary, the SUBLLM paper introduces a comprehensive approach to enhancing the efficiency and performance of Large Language Models through a novel architecture, token sequence subsampling, and dynamic resource allocation based on token importance, while also integrating with existing techniques for further optimization . The SUBLLM architecture proposed in the paper introduces several key characteristics and advantages compared to previous methods, as detailed in the document :

  1. Innovative Architecture: SUBLLM integrates subsampling, upsampling, and a bypass module within the core decoder-only framework of Large Language Models (LLMs). This design dynamically allocates resources to important tokens, reducing computational costs associated with token redundancy and accelerating model convergence through the bypass connection .

  2. Token Sequence Subsampling: SUBLLM introduces a novel approach to token sequence subsampling that effectively measures token importance scores and controls the distribution of score values to achieve the desired subsampling retention ratio during inference. This method optimizes the sequence length and enhances computational efficiency .

  3. Enhanced Efficiency: Experimental results demonstrate significant enhancements in both training and inference speeds, along with reduced memory usage, when compared to the LLaMA model. During training, SUBLLM achieves a 26% speed-up and a memory reduction of 10GB per GPU. In inference, it boosts speeds by up to 37% and reduces memory by 1GB per GPU. These improvements are further amplified when the context window is expanded .

  4. Dynamic Resource Allocation: SUBLLM dynamically allocates computational resources for tokens based on their importance, allowing for selective removal of less important tokens. This selective removal significantly reduces computational demands, enhances training stability, accelerates convergence, and potentially improves modeling outcomes .

  5. Compatibility and Flexibility: Unlike some previous methods that may not be compatible with existing decoder-only LLMs, SUBLLM's placement of subsampling and upsampling modules between Transformer blocks ensures compatibility with current LLMs while reducing token sequence length. This characteristic enhances the model's adaptability and applicability in various settings .

In summary, the SUBLLM architecture stands out for its innovative design, efficient token sequence subsampling approach, enhanced computational efficiency, dynamic resource allocation based on token importance, and compatibility with existing LLM frameworks, positioning it as a promising option for environments requiring optimal performance and effective computational resource management .


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 studies exist in the field of large language models (LLMs) and efficient architectures. Noteworthy researchers in this field include R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Y. Ng, and C. Potts , J. Su, M. Ahmed, Y. Lu, S. Pan, W. Bo, and Y. Liu , H. Touvron, T. Lavril, G. Izacard, X. Martinet, Lachaux, et al. , and many others.

The key solution mentioned in the paper is the proposal of a novel and efficient architecture called Subsampling-Upsampling-Bypass Large Language Model (SUBLLM). This architecture dynamically allocates computational resources for tokens based on their importance, integrating subsampling and upsampling modules symmetrically between Transformer blocks to reduce computational costs while preserving the input sequence's semantics. The subsampling module calculates each token's importance for token subsampling, while the upsampling module recovers the subsampled sequences for token prediction in language modeling. Additionally, a bypass module is integrated to perform a weighted sum of the upsampled token sequence and the original one to improve training stability and accelerate convergence speed .


How were the experiments in the paper designed?

The experiments in the paper were designed to analyze various aspects of the proposed SUBLLM model, focusing on its efficiency, performance maintenance, and optimization strategies . The main contributions of the work include:

  • Proposing a novel architecture, SUBLLM, incorporating subsampling, upsampling, and a bypass module to allocate resources dynamically, reduce computational costs, and accelerate model convergence .
  • Introducing a novel approach to token sequence subsampling that effectively measures token importance scores, controls score distribution, and achieves the desired subsampling retention ratio during inference .
  • Demonstrating through experimental results that SUBLLM achieves speed-up on both training and inference compared to the LLaMA model, with significant memory cost reduction while maintaining performance .

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

The dataset used for quantitative evaluation in the study is the Fairseq framework . The code for the research is open source as it was conducted using the Fairseq framework, which is an open-source toolkit for sequence modeling .


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 substantial support for the scientific hypotheses that needed verification. The study conducted experiments on different aspects of the proposed architecture, SUBLLM, and analyzed various performance metrics to evaluate its efficiency and effectiveness . The experiments included assessing the impact of different optimizers on model performance, analyzing the validity of subsampling, and exploring inference acceleration techniques . These experiments were crucial in testing the hypotheses related to the optimization of Large Language Models (LLMs) for enhanced performance and computational efficiency.

The analysis of different subsampling setups, including the number of subsampling modules and retention ratio, provided valuable insights into the model's performance in terms of valid loss and training speed-up ratio . By examining the distribution of indexes after subsampling and comparing different configurations, the study effectively evaluated the impact of subsampling on the model's efficiency and training process . This analysis contributed to verifying the hypotheses regarding the effectiveness of subsampling in improving pre-training efficiency.

Furthermore, the study explored inference acceleration techniques such as speculative decoding, knowledge distillation, quantization, and pruning, highlighting their importance in optimizing the decoding process of Large Language Models . By discussing how the proposed architecture, SUBLLM, can leverage these strategies to expedite the inference process and reduce memory costs, the paper provided a comprehensive analysis supporting the hypotheses related to inference speed enhancement and memory efficiency .

Overall, the experiments and results presented in the paper offer strong support for the scientific hypotheses under investigation. The detailed analyses of different aspects of the SUBLLM architecture, including subsampling, inference acceleration, and optimizer impact, provide valuable insights into the efficiency and effectiveness of the proposed model, confirming the validity of the scientific hypotheses addressed in the study.


What are the contributions of this paper?

The contributions of the paper "SUBLLM: A Novel Efficient Architecture with Token Sequence Subsampling for LLM" are as follows:

  • Proposing a novel architecture, SUBLLM, which incorporates subsampling, upsampling, and a bypass module to dynamically allocate resources to important tokens, reducing computational costs associated with token redundancy and accelerating model convergence through the bypass connection .
  • Introducing a novel approach to token sequence subsampling that effectively measures token importance scores and controls the distribution of score values to achieve the desired subsampling retention ratio during inference .
  • Demonstrating through experimental results that SUBLLM achieves a 26% speed-up on training and a 37% speed-up on inference compared to the LLaMA model, with a significant reduction in memory cost, while maintaining performance .

What work can be continued in depth?

Further work that can be continued in depth based on the provided context includes exploring optimal configurations for the proposed SUBLLM architecture. This exploration involves investigating various factors such as the number of continuous subsampling modules and retention ratio to achieve an appropriate speed-up ratio and improved performance that can be universally applied . Additionally, research can focus on analyzing the index distribution after the first subsampling in the SUBLLM model and comparing it with the attention distribution within the pre-subsampling block to understand how important tokens are preserved through the subsampling process . This in-depth analysis can provide insights into the effectiveness of the subsampling mechanism in retaining crucial semantic information during model training and decoding.


Introduction
Background
Overview of large language models and efficiency challenges
Current limitations of existing architectures like LLaMA
Objective
To develop a novel architecture that improves efficiency
Aim to enhance speed, memory usage, and few-shot performance
Method
Architecture Design
U-Net Inspired Subsampling and Upsampling Modules
Sequence compression and restoration process
Dynamic resource allocation based on token importance
Bypass Modules
Accelerating convergence and computational efficiency
Data Collection
Data selection and preprocessing for model training
Comparison with LLaMA's data requirements
Data Preprocessing
Techniques used for cleaning, formatting, and tokenization
Handling of variable-length sequences
Training and Inference
Speed Comparison
Training speedup: 26% improvement over LLaMA
Inference speedup: 37% improvement over LLaMA
Memory Efficiency
Memory reduction strategies and their impact
Memory footprint during training and inference
Computational Efficiency
Analysis of computational savings and bottlenecks
Optimizations for resource-constrained environments
Performance Evaluation
Few-Shot Learning
SUBLLM's performance in few-shot scenarios
Comparison with LLaMA's few-shot results
Evaluation Metrics
Accuracy, perplexity, and other relevant metrics
Experiment Results
Demonstrating improved performance and efficiency
Real-world application scenarios
Conclusion
Summary of SUBLLM's advantages
Potential for large-scale text processing and resource-limited applications
Future directions and research possibilities
Basic info
papers
computation and language
artificial intelligence
Advanced features
Insights
What is the primary advantage of SUBLLM over LLaMA?
How does SUBLLM manage computational resources in large language models?
How does SUBLLM's design contribute to efficient sequential generation?
What are the specific speed improvements achieved by SUBLLM in training and inference compared to LLaMA?

SUBLLM: A Novel Efficient Architecture with Token Sequence Subsampling for LLM

Quandong Wang, Yuxuan Yuan, Xiaoyu Yang, Ruike Zhang, Kang Zhao, Wei Liu, Jian Luan, Daniel Povey, Bin Wang·June 03, 2024

Summary

SUBLLM is a novel architecture for large language models that addresses efficiency challenges by incorporating subsampling, upsampling, and bypass modules. It dynamically allocates resources based on token importance, reducing computational costs and improving convergence. The architecture outperforms LLaMA in terms of training and inference speed, memory usage, and maintains competitive few-shot performance. SUBLLM achieves speed-ups of 26% and 37% in training and inference, respectively, with memory reductions and improved computational efficiency. The design is based on the U-Net architecture and balances sequence compression and restoration for efficient sequential generation. Experiments show that SUBLLM maintains performance while enhancing memory management and speed, making it an attractive choice for large-scale text processing and resource-constrained environments.
Mind map
Accelerating convergence and computational efficiency
Dynamic resource allocation based on token importance
Sequence compression and restoration process
Real-world application scenarios
Demonstrating improved performance and efficiency
Accuracy, perplexity, and other relevant metrics
Comparison with LLaMA's few-shot results
SUBLLM's performance in few-shot scenarios
Optimizations for resource-constrained environments
Analysis of computational savings and bottlenecks
Memory footprint during training and inference
Memory reduction strategies and their impact
Inference speedup: 37% improvement over LLaMA
Training speedup: 26% improvement over LLaMA
Handling of variable-length sequences
Techniques used for cleaning, formatting, and tokenization
Comparison with LLaMA's data requirements
Data selection and preprocessing for model training
Bypass Modules
U-Net Inspired Subsampling and Upsampling Modules
Aim to enhance speed, memory usage, and few-shot performance
To develop a novel architecture that improves efficiency
Current limitations of existing architectures like LLaMA
Overview of large language models and efficiency challenges
Future directions and research possibilities
Potential for large-scale text processing and resource-limited applications
Summary of SUBLLM's advantages
Experiment Results
Evaluation Metrics
Few-Shot Learning
Computational Efficiency
Memory Efficiency
Speed Comparison
Data Preprocessing
Data Collection
Architecture Design
Objective
Background
Conclusion
Performance Evaluation
Training and Inference
Method
Introduction
Outline
Introduction
Background
Overview of large language models and efficiency challenges
Current limitations of existing architectures like LLaMA
Objective
To develop a novel architecture that improves efficiency
Aim to enhance speed, memory usage, and few-shot performance
Method
Architecture Design
U-Net Inspired Subsampling and Upsampling Modules
Sequence compression and restoration process
Dynamic resource allocation based on token importance
Bypass Modules
Accelerating convergence and computational efficiency
Data Collection
Data selection and preprocessing for model training
Comparison with LLaMA's data requirements
Data Preprocessing
Techniques used for cleaning, formatting, and tokenization
Handling of variable-length sequences
Training and Inference
Speed Comparison
Training speedup: 26% improvement over LLaMA
Inference speedup: 37% improvement over LLaMA
Memory Efficiency
Memory reduction strategies and their impact
Memory footprint during training and inference
Computational Efficiency
Analysis of computational savings and bottlenecks
Optimizations for resource-constrained environments
Performance Evaluation
Few-Shot Learning
SUBLLM's performance in few-shot scenarios
Comparison with LLaMA's few-shot results
Evaluation Metrics
Accuracy, perplexity, and other relevant metrics
Experiment Results
Demonstrating improved performance and efficiency
Real-world application scenarios
Conclusion
Summary of SUBLLM's advantages
Potential for large-scale text processing and resource-limited applications
Future directions and research possibilities
Key findings
1

Paper digest

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

The paper "SUBLLM: A Novel Efficient Architecture with Token Sequence Subsampling for LLM" aims to address the challenge of efficiency in training and inference processes of Large Language Models (LLMs) by proposing a new architecture called SUBLLM, which incorporates subsampling, upsampling, and a bypass module to allocate resources dynamically, reduce computational costs, accelerate model convergence, and enhance performance . This problem of efficiency in LLMs is not new, as previous studies have also focused on compressing LLMs through techniques like knowledge distillation, quantization, and pruning to reduce memory requirements and improve inference speed . The SUBLLM paper introduces innovative methods to tackle this ongoing challenge in the field of natural language processing.


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the scientific hypothesis that for language models, the semantics at equivalent depths should be similar . The study compares the attention distribution within the pre-subsampling block and the index distribution after the first subsampling in the SUBLLM model to analyze the preservation of important semantic information through the subsampling process .


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

The paper "SUBLLM: A Novel Efficient Architecture with Token Sequence Subsampling for LLM" proposes several innovative ideas, methods, and models to enhance model efficiency and performance . Here are the key contributions outlined in the paper:

  1. Novel Architecture - SUBLLM: The paper introduces a novel architecture called SUBLLM, which integrates subsampling, upsampling, and a bypass module. This design dynamically allocates resources to important tokens, reducing computational costs related to token redundancy and accelerating model convergence through the bypass connection .

  2. Token Sequence Subsampling Approach: The paper presents a new approach to token sequence subsampling that effectively measures token importance scores and controls the distribution of score values to achieve the desired subsampling retention ratio during inference .

  3. Experimental Results: Experimental results demonstrate that SUBLLM achieves a 26% speed-up on training and a 37% speed-up on inference compared to the LLaMA model. This speed-up is accompanied by a significant reduction in memory cost while maintaining performance .

  4. Integration with Existing Techniques: The SUBLLM architecture is not mutually exclusive with existing inference acceleration methods. It can leverage strategies such as knowledge distillation, quantization, and pruning to expedite the inference process and reduce memory cost .

  5. Dynamic Resource Allocation: SUBLLM dynamically allocates computational resources for tokens based on their importance, inspired by the finding that some tokens are more crucial than others in natural language. This selective removal of less important tokens significantly reduces computational demands, enhances training stability, accelerates convergence, and potentially improves modeling outcomes .

In summary, the SUBLLM paper introduces a comprehensive approach to enhancing the efficiency and performance of Large Language Models through a novel architecture, token sequence subsampling, and dynamic resource allocation based on token importance, while also integrating with existing techniques for further optimization . The SUBLLM architecture proposed in the paper introduces several key characteristics and advantages compared to previous methods, as detailed in the document :

  1. Innovative Architecture: SUBLLM integrates subsampling, upsampling, and a bypass module within the core decoder-only framework of Large Language Models (LLMs). This design dynamically allocates resources to important tokens, reducing computational costs associated with token redundancy and accelerating model convergence through the bypass connection .

  2. Token Sequence Subsampling: SUBLLM introduces a novel approach to token sequence subsampling that effectively measures token importance scores and controls the distribution of score values to achieve the desired subsampling retention ratio during inference. This method optimizes the sequence length and enhances computational efficiency .

  3. Enhanced Efficiency: Experimental results demonstrate significant enhancements in both training and inference speeds, along with reduced memory usage, when compared to the LLaMA model. During training, SUBLLM achieves a 26% speed-up and a memory reduction of 10GB per GPU. In inference, it boosts speeds by up to 37% and reduces memory by 1GB per GPU. These improvements are further amplified when the context window is expanded .

  4. Dynamic Resource Allocation: SUBLLM dynamically allocates computational resources for tokens based on their importance, allowing for selective removal of less important tokens. This selective removal significantly reduces computational demands, enhances training stability, accelerates convergence, and potentially improves modeling outcomes .

  5. Compatibility and Flexibility: Unlike some previous methods that may not be compatible with existing decoder-only LLMs, SUBLLM's placement of subsampling and upsampling modules between Transformer blocks ensures compatibility with current LLMs while reducing token sequence length. This characteristic enhances the model's adaptability and applicability in various settings .

In summary, the SUBLLM architecture stands out for its innovative design, efficient token sequence subsampling approach, enhanced computational efficiency, dynamic resource allocation based on token importance, and compatibility with existing LLM frameworks, positioning it as a promising option for environments requiring optimal performance and effective computational resource management .


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 studies exist in the field of large language models (LLMs) and efficient architectures. Noteworthy researchers in this field include R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Y. Ng, and C. Potts , J. Su, M. Ahmed, Y. Lu, S. Pan, W. Bo, and Y. Liu , H. Touvron, T. Lavril, G. Izacard, X. Martinet, Lachaux, et al. , and many others.

The key solution mentioned in the paper is the proposal of a novel and efficient architecture called Subsampling-Upsampling-Bypass Large Language Model (SUBLLM). This architecture dynamically allocates computational resources for tokens based on their importance, integrating subsampling and upsampling modules symmetrically between Transformer blocks to reduce computational costs while preserving the input sequence's semantics. The subsampling module calculates each token's importance for token subsampling, while the upsampling module recovers the subsampled sequences for token prediction in language modeling. Additionally, a bypass module is integrated to perform a weighted sum of the upsampled token sequence and the original one to improve training stability and accelerate convergence speed .


How were the experiments in the paper designed?

The experiments in the paper were designed to analyze various aspects of the proposed SUBLLM model, focusing on its efficiency, performance maintenance, and optimization strategies . The main contributions of the work include:

  • Proposing a novel architecture, SUBLLM, incorporating subsampling, upsampling, and a bypass module to allocate resources dynamically, reduce computational costs, and accelerate model convergence .
  • Introducing a novel approach to token sequence subsampling that effectively measures token importance scores, controls score distribution, and achieves the desired subsampling retention ratio during inference .
  • Demonstrating through experimental results that SUBLLM achieves speed-up on both training and inference compared to the LLaMA model, with significant memory cost reduction while maintaining performance .

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

The dataset used for quantitative evaluation in the study is the Fairseq framework . The code for the research is open source as it was conducted using the Fairseq framework, which is an open-source toolkit for sequence modeling .


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 substantial support for the scientific hypotheses that needed verification. The study conducted experiments on different aspects of the proposed architecture, SUBLLM, and analyzed various performance metrics to evaluate its efficiency and effectiveness . The experiments included assessing the impact of different optimizers on model performance, analyzing the validity of subsampling, and exploring inference acceleration techniques . These experiments were crucial in testing the hypotheses related to the optimization of Large Language Models (LLMs) for enhanced performance and computational efficiency.

The analysis of different subsampling setups, including the number of subsampling modules and retention ratio, provided valuable insights into the model's performance in terms of valid loss and training speed-up ratio . By examining the distribution of indexes after subsampling and comparing different configurations, the study effectively evaluated the impact of subsampling on the model's efficiency and training process . This analysis contributed to verifying the hypotheses regarding the effectiveness of subsampling in improving pre-training efficiency.

Furthermore, the study explored inference acceleration techniques such as speculative decoding, knowledge distillation, quantization, and pruning, highlighting their importance in optimizing the decoding process of Large Language Models . By discussing how the proposed architecture, SUBLLM, can leverage these strategies to expedite the inference process and reduce memory costs, the paper provided a comprehensive analysis supporting the hypotheses related to inference speed enhancement and memory efficiency .

Overall, the experiments and results presented in the paper offer strong support for the scientific hypotheses under investigation. The detailed analyses of different aspects of the SUBLLM architecture, including subsampling, inference acceleration, and optimizer impact, provide valuable insights into the efficiency and effectiveness of the proposed model, confirming the validity of the scientific hypotheses addressed in the study.


What are the contributions of this paper?

The contributions of the paper "SUBLLM: A Novel Efficient Architecture with Token Sequence Subsampling for LLM" are as follows:

  • Proposing a novel architecture, SUBLLM, which incorporates subsampling, upsampling, and a bypass module to dynamically allocate resources to important tokens, reducing computational costs associated with token redundancy and accelerating model convergence through the bypass connection .
  • Introducing a novel approach to token sequence subsampling that effectively measures token importance scores and controls the distribution of score values to achieve the desired subsampling retention ratio during inference .
  • Demonstrating through experimental results that SUBLLM achieves a 26% speed-up on training and a 37% speed-up on inference compared to the LLaMA model, with a significant reduction in memory cost, while maintaining performance .

What work can be continued in depth?

Further work that can be continued in depth based on the provided context includes exploring optimal configurations for the proposed SUBLLM architecture. This exploration involves investigating various factors such as the number of continuous subsampling modules and retention ratio to achieve an appropriate speed-up ratio and improved performance that can be universally applied . Additionally, research can focus on analyzing the index distribution after the first subsampling in the SUBLLM model and comparing it with the attention distribution within the pre-subsampling block to understand how important tokens are preserved through the subsampling process . This in-depth analysis can provide insights into the effectiveness of the subsampling mechanism in retaining crucial semantic information during model training and decoding.

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