Iron: Private Inference on Transformers
Authors: Meng Hao, Hongwei Li, Hanxiao Chen, Pengzhi Xing, Guowen Xu, Tianwei Zhang
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments conducted on several real-world datasets and models demonstrate that Iron achieves 3 14 less communication and 3 11 less runtime compared to the prior art. |
| Researcher Affiliation | Academia | Meng Hao1 Hongwei Li1 Hanxiao Chen1 Pengzhi Xing1 Guowen Xu2 Tianwei Zhang2 1University of Electronic Science and Technology of China 2Nanyang Technological University |
| Pseudocode | Yes | Algorithm 1 Secure Matrix Multiplication Protocol; Algorithm 2 Secure Softmax Protocol; Algorithm 3 Secure GELU Protocol; Algorithm 4 Secure Layer Norm Protocol. |
| Open Source Code | No | The paper states 'Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [No]' in its ethics checklist. While it mentions building on existing libraries, it does not provide the code for Iron itself. |
| Open Datasets | Yes | We train the models for four NLP tasks over the datasets of the Stanford Sentiment Treebank (SST-2), the Microsoft Research Paraphrase Corpus (MRPC), the Multi-Genre Natural Language Inference Corpus (MNLI) and the Stanford Question Answering Dataset (QNLI) from GLUE benchmarks [18]. |
| Dataset Splits | Yes | We train the models for four NLP tasks over the datasets of the Stanford Sentiment Treebank (SST-2), the Microsoft Research Paraphrase Corpus (MRPC), the Multi-Genre Natural Language Inference Corpus (MNLI) and the Stanford Question Answering Dataset (QNLI) from GLUE benchmarks [18]. The GLUE benchmarks are standard datasets with predefined train, validation, and test splits. |
| Hardware Specification | Yes | All the following experiments are performed on AWS c5.9xlarge instances with Intel Xeon 8000 series CPUs at 3.6GHz. |
| Software Dependencies | No | Iron is built on top of the SEAL library [32] and the EMP toolkit [33] in C++. We also use the Ez PC framework [34]. No specific version numbers are provided for SEAL, EMP toolkit, or Ez PC. |
| Experiment Setup | Yes | These models are parameterized by three hyper-parameters: the number of blocks, the dimension of representations and the number of input tokens (refer to Appendix A.4.1 for the hyper-parameters of these models). |