toplogo
Đăng nhập

Keyformer: KV Cache Reduction for Efficient Generative Inference


Khái niệm cốt lõi
Keyformer introduces a novel approach to reduce KV cache size, improving inference latency and token generation throughput without compromising accuracy.
Tóm tắt

Transformers are crucial for Large Language Models (LLMs), but face challenges with long-context processing. Keyformer reduces KV cache size by identifying key tokens, improving performance across various models and tasks. The Gumbel distribution proves effective in regularization for key token identification.

edit_icon

Tùy Chỉnh Tóm Tắt

edit_icon

Viết Lại Với AI

edit_icon

Tạo Trích Dẫn

translate_icon

Dịch Nguồn

visual_icon

Tạo sơ đồ tư duy

visit_icon

Xem Nguồn

Thống kê
Keyformer reduces inference latency by 2.1× and improves token generation throughput by 2.4×. Approximately 90% of attention weight focuses on key tokens. Keyformer maintains accuracy while reducing KV cache size by up to 50%.
Trích dẫn
"Keyformer leverages the observation that approximately 90% of the attention weight in generative inference focuses on a specific subset of tokens, referred to as 'key' tokens." "Even with a 50% reduction in KV cache, Keyformer maintains the desired accuracy threshold."

Thông tin chi tiết chính được chắt lọc từ

by Muhammad Adn... lúc arxiv.org 03-15-2024

https://arxiv.org/pdf/2403.09054.pdf
Keyformer

Yêu cầu sâu hơn

How does Keyformer's approach compare to other methods like H2O in terms of accuracy preservation?

Keyformer's approach outperforms other methods like H2O in terms of accuracy preservation. By leveraging a novel score function that incorporates Gumbel noise distribution for key token identification, Keyformer maintains high model accuracy even with a reduced KV cache size. In comparison, H2O falls short of achieving the desired accuracy levels when reducing the KV cache by the same percentage. The Gumbel-based logit adjustment introduced by Keyformer proves to be more effective in identifying key tokens and preserving accuracy compared to other regularization strategies.

How might the findings from this study impact the development of future Large Language Models?

The findings from this study have several implications for the development of future Large Language Models (LLMs). Firstly, Keyformer's innovative approach to dynamically reduce KV cache size while maintaining model accuracy sets a new standard for efficient generative inference. This can inspire further research into optimizing memory bandwidth utilization and improving computational efficiency in LLMs. Additionally, the use of Gumbel distribution for key token identification introduces a new perspective on how positional information can be leveraged effectively during inference. Future LLMs could potentially benefit from incorporating similar probabilistic approaches to enhance context understanding and text generation quality. Moreover, the performance improvements demonstrated by Keyformer in terms of reduced inference latency and increased token generation throughput highlight the importance of optimizing memory usage and computation resources in large-scale language models. These insights could guide future developments towards more efficient and scalable LLM architectures that balance performance with resource constraints effectively.
0
star