TurboRAG: Accelerating Retrieval-Augmented Generation with Precomputed KV Caches for Chunked Text (2410.07590v1)

Abstract: Current Retrieval-Augmented Generation (RAG) systems concatenate and process numerous retrieved document chunks for prefill which requires a large volume of computation, therefore leading to significant latency in time-to-first-token (TTFT). To reduce the computation overhead as well as TTFT, we introduce TurboRAG, a novel RAG system that redesigns the inference paradigm of the current RAG system by first pre-computing and storing the key-value (KV) caches of documents offline, and then directly retrieving the saved KV cache for prefill. Hence, online computation of KV caches is eliminated during inference. In addition, we provide a number of insights into the mask matrix and positional embedding mechanisms, plus fine-tune a pretrained LLM to maintain model accuracy of TurboRAG. Our approach is applicable to most existing LLMs and their applications without any requirement in modification of models and inference systems. Experimental results across a suite of RAG benchmarks demonstrate that TurboRAG reduces TTFT by up to 9.4x compared to the conventional RAG systems (on an average of 8.6x), but reserving comparable performance to the standard RAG systems.

• The paper introduces TurboRAG, which precomputes key-value caches for document chunks to achieve up to 9.4x faster time-to-first-token.
• It employs a hybrid offline-online framework that significantly reduces computational overhead while preserving model accuracy.
• Experimental results confirm enhanced throughput and resource efficiency, facilitating practical deployments in latency-sensitive environments.

An Analysis of TurboRAG: Enhancing Efficiency in Retrieval-Augmented Generation

The paper "TurboRAG: Accelerating Retrieval-Augmented Generation with Precomputed KV Caches for Chunked Text" presents a refined approach to improving the efficiency of Retrieval-Augmented Generation (RAG) systems. These systems are instrumental in enhancing LLMs by integrating external knowledge, thereby reducing hallucinations and improving contextual relevance. TurboRAG is innovatively designed to tackle the prevalent issue of high time-to-first-token (TTFT) by precomputing and storing key-value (KV) caches offline, which significantly reduces computational overhead during inference.

Key Contributions and Methods

The primary innovation of TurboRAG lies in its restructuring of the RAG inference framework by precomputing the KV caches of document chunks offline. This precomputation eliminates the need for real-time computation of these caches during the generation process. The methodology employed by TurboRAG introduces a hybrid paradigm of offline and online processes, which contrasts starkly with the conventional online computation in standard RAG systems.

A notable challenge with this approach arises from the potential inconsistencies in attention mask matrices and positional embeddings when KV caches are accessed separately. TurboRAG addresses this through the identification of two critical observations: the inherent sparsity in cross-attention among document chunks and the relative nature of positional embeddings such as RoPE. By leveraging independent attention mask matrices and reorganizing position IDs, TurboRAG maintains model accuracy while facilitating vastly reduced online computational loads.

Experimental Evaluation

The experimental results highlight TurboRAG's efficacy, particularly regarding TTFT reduction. The system achieves up to a 9.4x reduction in latency on RAG benchmarks, averaging an 8.6x improvement. Importantly, these latency gains do not compromise the model's accuracy, which remains competitive with baseline RAG systems. The detailed analyses across various benchmarks underscore TurboRAG’s ability to support larger batch sizes and increased throughput due to decreased resource utilization, marking a substantial advancement in the practical deployment of RAG systems.

Implications and Future Directions

TurboRAG’s contributions signify a pivotal shift in handling retrieval-augmented generation tasks, particularly where latency constraints are paramount. By transforming the computation paradigm, TurboRAG not only enhances performance metrics but also potentially broadens the application scope of RAG systems to environments where rapid response times are critical.

Furthermore, this research invites further exploration into optimizing KV cache utilization and extending similar methodologies across diverse NLP applications. The approach could potentially be adapted to other areas of LLMing that struggle with computational efficiency due to input size and complexity.

Conclusion

TurboRAG presents a significant advancement in the efficiency of RAG systems, offering a practical solution to the computational challenges posed by traditional methods. By achieving substantial reductions in TTFT without sacrificing accuracy, TurboRAG paves the way for more responsive and resource-efficient LLM applications. As the field of AI continues to evolve, such methodologies will play an essential role in optimizing the balance between computational demand and performance efficacy.