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CATS: Contextually-Aware Thresholding for Sparsity in Large Language Models (2404.08763v4)

Published 12 Apr 2024 in cs.LG and cs.CL

Abstract: LLMs have dramatically advanced AI applications, yet their deployment remains challenging due to their immense inference costs. Recent studies ameliorate the computational costs of LLMs by increasing their activation sparsity but suffer from significant performance degradation on downstream tasks. In this work, we introduce a new framework for sparsifying the activations of base LLMs and reducing inference costs, dubbed Contextually Aware Thresholding for Sparsity (CATS). CATS is relatively simple, easy to implement, and highly effective. At the heart of our framework is a new non-linear activation function. We demonstrate that CATS can be applied to various base models, including Mistral-7B and Llama2-7B, and outperforms existing sparsification techniques in downstream task performance. More precisely, CATS-based models often achieve downstream task performance within 1-2% of their base models without any fine-tuning and even at activation sparsity levels of 50%. Furthermore, CATS-based models converge faster and display better task performance than competing techniques when fine-tuning is applied. Finally, we develop a custom GPU kernel for efficient implementation of CATS that translates the activation of sparsity of CATS to real wall-clock time speedups. Our custom kernel implementation of CATS results in a ~15% improvement in wall-clock inference latency of token generation on both Llama-7B and Mistral-7B.

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Authors (5)
  1. Je-Yong Lee (3 papers)
  2. Donghyun Lee (37 papers)
  3. Genghan Zhang (9 papers)
  4. Mo Tiwari (15 papers)
  5. Azalia Mirhoseini (40 papers)
Citations (8)
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Summary

Enhancing LLM Inference Efficiency with Contextually Aware Thresholding for Sparsity (CATS)

Introduction to Inference Cost Issues

LLMs like GPT-3 and variants within the Llama and Mistral families, despite showing impressive capabilities, continue to pose significant challenges regarding inference costs, largely surpassing even their training costs in energy and computational demands. To address these, techniques such as quantization and pruning have been employed, with Mixture of Experts (MoE) emerging as a particularly promising method. MoEs work by selectively activating parameters necessary for specific inference tasks, effectively reducing operational redundancy. Following a similar philosophy but distinct path, our examination revealed that LLMs often exhibit sparsity in MLP activations, suggesting a potential for computational savings if these sparse activations could be systematically identified and leveraged.

Novel Framework Introduction: CATS

To exploit this naturally occurring sparsity, the paper introduces the CATS framework. CATS stands for Contextually Aware Thresholding for Sparsity, which utilizes a novel non-linear activation function tailored to enhance sparsity in neural activations. Unlike existing approaches which might compromise model performance, CATS uniquely maintains task performance within 1-2% of the base model across various downstream tasks, even achieving 50% activation sparsity. Moreover, when fine-tuning is applied, CATS outperforms other state-of-the-art techniques in terms of task performance at similar sparsity levels.

Methodology

Data-driven Sparsity Enhancement

CATS activates by setting a contextually determined sparsity threshold on activations post a non-linear function in the network’s MLP blocks. By analyzing activation distributions from layers of pre-trained models like Llama-7B and Mistral-7B, the approach defines thresholds that help maintain essential activations while dispensing ones close to zero. This selective activation mimics the role of 'experts' in MoE approaches but with the significant benefit of not requiring additional parameters or complex routing mechanisms.

Custom GPU Kernel Implementation

To translate activation sparsity into real-time computational savings, CATS includes a custom GPU kernel optimized to exploit sparse activation patterns effectively. This implementation achieves up to approximately 15% improvement in wall-clock inference latency during token generation tasks, underscoring CATS's practical utility beyond theoretical sparsity.

Experimentation and Results

The paper evaluates CATS using baseline models of Llama-7B and Mistral-7B across different performance metrics and datasets designed to test varied capabilities from reasoning to comprehension. CATS-based models consistently match or exceed performance metrics of dense models without sparsity while requiring significantly fewer computational resources. In detailed latency tests, CATS not only reduces computational overhead but does so with minimal impact on response times, a critical factor in user-facing applications.

Implications and Future Directions

The ability of CATS to maintain high task performance while significantly reducing inference costs addresses a crucial barrier in deploying LLMs at scale, particularly in resource-constrained environments. Looking ahead, further optimization of the CATS architecture and its kernel could yield even more significant gains. Additionally, exploring the interplay between CATS-induced sparsity and other model compression techniques might provide a path toward ultra-efficient LLMs ready for broad deployment across various platforms, from edge devices to large-scale cloud environments.

In conclusion, the CATS framework marks a substantial advancement in our approach to managing the computational costs associated with LLMs, aligning performance with efficiency through innovative use of sparsity in neural networks. The development of a tailored GPU kernel further exemplifies the practical application of theoretical insights, establishing a template for future explorations in the field.