FastQuery: Communication-efficient Embedding Table Query for Private LLM Inference
Summary
Paper digest
What problem does the paper attempt to solve? Is this a new problem?
The paper "FastQuery: Communication-efficient Embedding Table Query for Private LLM Inference" aims to address the issue of communication overhead in secure two-party computation (2PC) based on homomorphic encryption (HE) for private large language model (LLM) inference . This problem arises due to the transfer of a large number of high bit-width homomorphically encrypted ciphertexts between the server and the client, leading to high communication costs . The paper introduces FastQuery as a framework to optimize private embedding table queries by reducing both computation and communication costs, specifically focusing on the one-hot nature of user queries and the robustness of the embedding table to low bit-width quantization noise . This problem of communication efficiency in private LLM inference is not entirely new, as previous works have focused on optimizing HE-based Transformer computation but overlooked the private embedding table query, which is found to be more time-consuming and communication-intensive .
What scientific hypothesis does this paper seek to validate?
This paper aims to validate the scientific hypothesis related to optimizing communication-efficient embedding table queries for private Large Language Models (LLMs) using FastQuery. The hypothesis focuses on reducing both computation and communication costs by implementing a communication-aware embedding table quantization algorithm and a one-hot-aware dense packing algorithm . The study seeks to demonstrate that FastQuery outperforms prior-art Homomorphic Encryption (HE)-based frameworks like Cheetah, Iron, and Bumblebee by achieving significant reductions in latency and communication overhead on LLAMA-7B and LLAMA-30B models . The research hypothesis is centered on enhancing the efficiency of privacy-preserving Deep Neural Network (DNN) inference involving two parties, the server, and the client, by minimizing communication overhead and latency .
What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?
The paper "FastQuery: Communication-efficient Embedding Table Query for Private LLM Inference" proposes several innovative ideas, methods, and models to enhance communication efficiency and privacy in deep neural network (DNN) inference involving two parties, a server, and a client . Here are the key contributions of the paper:
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FastQuery Framework: The paper introduces the FastQuery framework, which is a private embedding table query framework designed to improve communication efficiency in DNN inference . FastQuery leverages the one-hot nature of queries and robustness to low bit-width quantization noise to achieve significant reductions in both latency and communication overhead compared to prior-art homomorphic encryption (HE)-based frameworks like Cheetah, Iron, and Bumblebee .
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Communication-aware Embedding Table Quantization: The paper focuses on reducing the bit-width of the embedding table while maintaining the performance of Large Language Models (LLMs) . By proposing a communication-aware post-training embedding table quantization method, the paper aims to decrease the latency associated with embedding table queries .
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Packing Algorithms: FastQuery features a communication-aware embedding table quantization algorithm and a one-hot-aware dense packing algorithm . These algorithms optimize the packing density of output polynomials and reduce the number of output ciphertexts, thereby improving communication efficiency in DNN inference .
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Quantization Strategies: The paper explores different quantization granularities, including per-tensor, per-token, and per-channel quantization, to enhance model performance and reduce latency in DNN inference . By evaluating various bit-width combinations and channel judgment strategies, the proposed strategy achieves optimal performance on different datasets .
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Secure Two-Party Computation (2PC): The paper addresses privacy concerns in DNN inference by utilizing secure 2PC based on homomorphic encryption . This approach ensures the privacy of user data and model parameters while enabling accurate LLM inference with a formal privacy guarantee .
In summary, the paper introduces the FastQuery framework, communication-aware embedding table quantization, packing algorithms, quantization strategies, and secure 2PC methods to enhance communication efficiency and privacy in DNN inference involving two parties . FastQuery, as proposed in the paper "FastQuery: Communication-efficient Embedding Table Query for Private LLM Inference," introduces several key characteristics and advantages compared to previous methods, such as Cheetah, Iron, and Bumblebee, in the context of communication efficiency and privacy in deep neural network (DNN) inference involving two parties, a server, and a client .
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Protocol Optimization:
- FastQuery leverages the one-hot nature of user queries and low bit-width embedding tables to reduce the accumulation bit-width, plaintext, and ciphertext bit-widths, thereby enhancing communication efficiency .
- The protocol optimization in FastQuery focuses on computing 𝑊𝑋 directly in the online stage, which is a promising direction for reducing total communication costs compared to moving HE computation to the pre-processing stage .
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Network Optimization:
- FastQuery features a channel-wise mix-precision quantization method to reduce the bit-width of the embedding table, improving communication efficiency for sub-13-bit quantization .
- The proposed novel element packing algorithm in FastQuery squeezes the embedding table dimension, further reducing communication overhead for efficient DNN inference .
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Communication Reduction:
- Compared to prior-art HE-based frameworks like Cheetah, Iron, and Bumblebee, FastQuery achieves significant reductions in both latency and communication overhead. For instance, FastQuery outperforms Cheetah, Iron, and Bumblebee by more than 4.3×, 2.7×, and 1.3× in latency reduction, respectively, and more than 75.7×, 60.2×, and 20.2× in communication reduction, respectively, on models like LLAMA-7B and LLAMA-30B .
- The proposed methods in FastQuery, including communication-aware embedding table quantization and one-hot-aware dense packing algorithm, effectively reduce both latency and communication overhead in DNN inference scenarios involving private LLMs .
In summary, FastQuery stands out due to its innovative protocol and network optimizations, which leverage the one-hot nature of queries, low bit-width embedding tables, and novel element packing algorithms to significantly enhance communication efficiency and privacy in DNN inference compared to previous methods like Cheetah, Iron, and Bumblebee .
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 works exist in the field of private inference for large language models (LLMs) based on homomorphic encryption (HE) and secure two-party computation (2PC) . Noteworthy researchers in this field include:
- Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher
- Pratyush Mishra, Ryan Lehmkuhl, Akshayaram Srinivasan, Wenting Zheng, and RalucaAda Popa
- Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu
- Deevashwer Rathee, Mayank Rathee, Rahul Kranti Kiran Goli, Divya Gupta, Rahul Sharma, Nishanth Chandran, and Aseem Rastogi
- Zahra Ghodsi, Nandan Kumar Jha, Siddharth Garg, and Brandon Reagen
- Meng Hao, Hongwei Li, Hanxiao Chen, Pengzhi Xing, Guowen Xu, and Tianwei Zhang
- Xiaoyang Hou, Jian Liu, Jingyu Li, Yuhan Li, Wen-jie Lu, Cheng Hong, and Kui Ren
The key to the solution mentioned in the paper "FastQuery: Communication-efficient Embedding Table Query for Private LLM Inference" is the development of a private embedding table query optimization framework called FastQuery. This framework focuses on reducing both computation and communication costs by leveraging a communication-aware embedding table quantization algorithm and a one-hot-aware dense packing algorithm. FastQuery aims to enhance communication efficiency in private inference for LLMs by addressing the challenges related to the one-hot nature of user queries and the robustness of the embedding table to low bit-width quantization noise .
How were the experiments in the paper designed?
The experiments in the paper were designed by conducting evaluations and comparisons to demonstrate the effectiveness of the proposed methods in FastQuery . The experiments involved benchmarking for embedding table quantization, comparing different bit-width combinations and salient channel judgment strategies, and evaluating communication efficiency and latency across different embedding table dimensions . Additionally, the experiments included an ablation study to incrementally integrate optimization techniques into the baseline Cheetah and analyze the impact on communication efficiency and latency . The experimental setup utilized the SEAL library, OpenCheetah, EZPC library, and SPU library for simulation, conducted on a server with specific hardware specifications .
What is the dataset used for quantitative evaluation? Is the code open source?
The dataset used for quantitative evaluation in the study is WikiText-103 and C4 . The code used in the study is not explicitly mentioned to be open source in the provided context.
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 strong support for the scientific hypotheses that needed to be verified. The paper focuses on optimizing private embedding table queries for large language models (LLMs) through a framework called FastQuery . The experiments conducted in the paper demonstrate the effectiveness of FastQuery in reducing both computation and communication costs compared to prior-art HE-based frameworks like Cheetah, Iron, and Bumblebee .
The experiments include a detailed analysis of the proposed optimization techniques, such as communication-aware embedding table quantization and one-hot-aware dense packing algorithms, which aim to reduce latency and communication overhead . These experiments provide empirical evidence of the effectiveness of FastQuery in achieving significant reductions in latency and communication compared to existing frameworks .
Furthermore, the paper includes benchmark comparisons with other quantization methods like Round to Nearest per-channel quantization (RTN) . The results show that FastQuery outperforms RTN in terms of query latency reduction and model performance, providing additional support for the effectiveness of the proposed optimization techniques .
Overall, the experiments and results presented in the paper offer strong empirical support for the scientific hypotheses underlying the development of FastQuery as an efficient and effective framework for optimizing private embedding table queries for LLMs. The comparisons with existing frameworks and quantization methods demonstrate the superiority of FastQuery in reducing latency and communication overhead while maintaining model performance .
What are the contributions of this paper?
The paper "FastQuery: Communication-efficient Embedding Table Query for Private LLM Inference" makes the following contributions:
- Proposing FastQuery Framework: The paper introduces the FastQuery framework, which focuses on optimizing private embedding table queries by reducing computation and communication costs simultaneously .
- Communication Reduction: FastQuery achieves significant communication reduction compared to prior-art HE-based frameworks like Cheetah, Iron, and Bumblebee. It achieves more than 75.7×, 60.2×, and 20.2× communication reduction on LLAMA-7B and LLAMA-30B models, respectively .
- Latency Reduction: The FastQuery framework outperforms previous HE-based frameworks by achieving more than 4.3×, 2.7×, and 1.3× latency reduction compared to Cheetah, Iron, and Bumblebee, respectively .
What work can be continued in depth?
Further research in the field of private inference for large language models (LLMs) can be expanded in several directions based on the existing work on communication-efficient embedding table queries for private LLM inference . Some potential areas for continued research include:
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Optimizing Communication Efficiency: Future work can focus on enhancing communication efficiency in private inference systems by exploring novel algorithms and protocols that reduce communication overhead while maintaining data privacy and security .
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Quantization Techniques: Research can delve deeper into quantization techniques for embedding tables in LLMs to achieve better performance in terms of latency and model perplexity. Investigating different bit-width combinations and strategies for selecting salient channels can lead to further improvements in communication efficiency and model accuracy .
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Comparative Studies: Conducting more comparative studies between existing HE-based 2PC frameworks like Cheetah, Iron, and Bumblebee, and newer protocols like FastQuery can provide insights into the strengths and weaknesses of each approach. This can help in identifying the most suitable framework for specific use cases and applications .
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Security and Privacy Enhancements: Exploring advanced security and privacy enhancements in private inference systems, such as incorporating additional cryptographic techniques or refining existing protocols to mitigate potential vulnerabilities and ensure robust protection of user data and model parameters .
By delving deeper into these areas, researchers can contribute to the advancement of private inference technologies for large language models, paving the way for more efficient, secure, and privacy-preserving applications in the field of artificial intelligence and machine learning.