Towards Sampling Data Structures for Tensor Products

Authors: Zhao Song, Samson Zhou
Primary area: probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)
Keywords: sampling, data structures, tensor products

Abstract:
This paper studies the computational challenges of attention-based models in artificial intelligence by introducing innovative sampling methods to accelerate attention computation in large language models (LLM). Inspired by the recent progress of LLM in real-life applications, we introduces a streaming sampler question for attention setting. Our approach significantly reduces the computational burden of traditional attention mechanisms while maintaining or enhancing model performance. We demonstrate these methods' effectiveness from theoretical perspective, including space, update time. Additionally, our framework exhibits scalability and broad applicability across various model architectures and domains.

Decision: reject
Reviewer scores: 3, 3, 3, 5

Reviews and discussion:
<thread>
creation date:
1730158545700

thread_type:
official_review

<thread_object>
creator: reviewer_3nfn

<content>
summary:
This paper is investigating theoretically how sampling can be used in order to accelerate the computation of attention when focusing on a subset of the elements instead of the entire set. The paper provides distribution-specific sampling schemes and associated theoretical analysis of the proposed samplers.

strengths:
+ Accelerating the computation of attention has the potential to accelerate the vast amount of modern AI systems
+ The paper presents strong theoretical results for different distribution-specific samplers and for different sampling scenarios.

weaknesses:
- The original motivation is to improve the efficiency and scalability of the attention mechanism and as a result the overall efficiency and scalability of the larger AI system which relies on attention. However, it is not clear how effective are the proposed schemes in practice, and it would be important to showcase a couple of example scenarios where significant speedup (with reasonable drop in precision) can be achieved, that corroborates the theoretical bounds.

questions:
1) How does the proposed set of samplers behave in practice in terms of (a) speedup and scalability of the actual attention operation itself, (b) speedup and scalability of training and testing using a transformer model with the proposed samplers implemented?
</content>
</thread_object>

</thread>
<thread>
creation date:
1730399529836

thread_type:
official_review

<thread_object>
creator: reviewer_f6bp

<content>
summary:
The paper presents a  approach to addressing the computational challenges of attention-based models in AI, particularly in the context of large language models (LLM). By introducing novel sampling methods, the authors claims to significantly reduce the computational burden.

strengths:
1. The topic intresting, as LLMs' acceleration is really a important topic.
2. This paper is well-wrting.

weaknesses:
The paper has several significant limitations that make it fall short of ICLR standards:

1.While the authors claim their methods reduce computational costs, they provide no experimental evidence to support this assertion. Such claims require rigorous empirical validation.
2. The practical applicability to current popular LLM architectures like LLaMA and Mistral remains unexplored. The authors should have conducted comparative experiments demonstrating the performance and computational costs with and without their proposed methods on these widely-used models.
3. The paper lacks analysis of the methods' robustness under different failure scenarios and adversarial conditions. For real-world deployment, it is crucial to understand how these sampling methods perform under stress conditions and when processing corrupted data.

The absence of computational experiments to validate the paper's core claims is particularly concerning. This fundamental oversight in empirical validation significantly undermines the paper's contribution and makes it fall well below the quality standards expected for ICLR publications.

questions:
1. Can your methods be integrated to current LLMs? What is the performance and costs if your methods are implemented.
2. Can you gives a instruction on how to implement your methods?
3. In what cases your method can work and in what cases your method fails? Could you provide more discussions on the limitation of your methods?
</content>
</thread_object>

</thread>
<thread>
creation date:
1730644302543

thread_type:
official_review

<thread_object>
creator: reviewer_q7id

<content>
summary:
In recent years, artificial intelligence has experienced a paradigm shift with the advent of attention-based models, particularly in natural language processing and computer vision. At the core of these models is the attention mechanism, which enhances deep learning networks by focusing on relevant parts of the input data for more nuanced processing. However, as these models grow in size and complexity, the computational demands of the attention mechanism increase exponentially, posing challenges in efficiency and scalability. Traditional attention mechanisms, such as those used in Transformer models, require quadratic computational complexity with respect to sequence length, which hinders their deployment in resource-constrained environments and limits real-time processing capabilities. Additionally, the high computational cost increases the environmental impact due to higher energy consumption. This paper introduces innovative sampling methods to accelerate attention computation in deep learning models by strategically sampling key elements from the input data, thereby reducing computational overhead while maintaining or enhancing performance.

strengths:
1. The theoretical contributions are significant. This paper offers an extensive theoretical analysis of attention calculation in the transformer. The analysis showcases the authors' deep understanding and expertise in the field.

2. Clear and precise use of notations. Each notation is well-defined and consistently applied throughout the paper, contributing to overall clarity.
   

3. Logical writing. The author's logical expression of ideas ensures that the theoretical framework is robust and well-supported.

weaknesses:
1.The paper's theoretical effectiveness is not empirically verified. Speeding up attention calculation is a hot topic, and while the authors cite numerous relevant papers, the lack of experimental validation is a significant drawback. Although the authors acknowledge this in the limitation section, it remains problematic that they claim their framework "maintains or even enhances the model’s performance" without providing experimental proof. This leaves the effectiveness of their application framework unproven. 
2.The novelty and impact of this theory are not clearly articulated. The authors present their theory and proofs but do not compare their approach to existing frameworks for speeding up attention calculations. Furthermore, the impact on the community is unclear, as the authors do not highlight which problems their theory addresses that have been overlooked or difficult to solve until now.

questions:
Q1. Can the authors provide any preliminary experimental results or simulations that support their theoretical claims?
Q2. What specific challenges or gaps in the current research does this theory address that have been previously overlooked or inadequately solved by the community?
</content>
</thread_object>

</thread>
<thread>
creation date:
1730731141473

thread_type:
official_review

<thread_object>
creator: reviewer_vhkb

<content>
summary:
The paper addresses the computational challenges in attention-based models by introducing innovative sampling techniques to accelerate attention computation. It provides theoretical upper and lower bounds for different types of sampling, validated through rigorous theoretical analysis. However, the lack of experimental evaluation limits the work's validation, leaving room for further empirical testing.

strengths:
1. This work introduces interesting sampling methods for optimizing attention mechanisms in LLMs, which is an important advancement considering the increasing computational demands of these models.
2. The theoretical analysis for various sampling problems is comprehensive.

weaknesses:
1. The paper does not provide any empirical experiments to demonstrate the practical performance improvements of the proposed sampling methods. Including experimental results on real datasets would significantly enhance the credibility and applicability of the proposed approach. This does not align with the statement about "detailing the underlying principles, implementation strategies, and the resultant gains in computational efficiency."

questions:
Please provide an "overall score" for this submission.
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</thread_object>

</thread>


Metareview:
metareview:
The paper introducing sampling techniques to accelerate attention computation via sampling. It provides a theoretical analysis for the presented techniques resulting in rigorous guarantees. The problem of accelerating the attention computation is highly motivated given the wide usage of it, and a paper providing novel techniques for it is a welcome addition to ICLR. This being said, the paper does not provide empirical evidence for the quality of the method and this is agreed by all reviews to be a crucial problem making the paper incomplete.  

I would add to the reviews that what I feel is missing, that would be obtained from empirical experiments, is an understanding of the tradeoff between the compute cost and down-stream task performance. This cost does not have to be measured in wall clock time since this might require too big of an investment but can be something much easier to obtain such as FLOPs. 

In any case, I agree with the reviewers that without the experiments the paper is not ready for publication.