SwiftInfer Project Introduction
Overview
SwiftInfer is a project which stands at the intersection of advanced technology and everyday utility. It is built upon the innovative Streaming-LLM technique, designed to handle infinite input lengths in large language model (LLM) inference, effectively supporting endless streams of data without the typical pitfalls of attention window shifts. Initially developed in PyTorch, SwiftInfer brings this functionality into the TensorRT realm for robust, production-ready solutions.
Quick Start
Installation
SwiftInfer leverages the TensorRT-LLM library for model construction and inference. Given the evolving nature of TensorRT-LLM’s API, SwiftInfer aligns itself with a specific version (v0.6.0), but it is adaptable for future updates as the API stabilizes. For installation, one can either integrate SwiftInfer over an existing TensorRT-LLM setup or start from scratch.
Installation Scenarios:
- With Docker: Easy setup by following TensorRT-LLM's installation guide using Docker. This involves cloning the SwiftInfer repository and performing a simple installation command with Python's pip tool.
- Without Docker: A more manual approach includes ensuring dependencies like CUDA, cuDNN, NCCL, and others are appropriately installed before proceeding with SwiftInfer's setup.
Run Llama Example
To explore SwiftInfer in action, users can execute a Llama example. This involves preparing model data through repositories like meta-llama or using derivatives like Vicuna models. By configuring the TensorRT engine with relevant parameters—such as data types, input/output lengths, and plugins—users can then download required benchmark data to evaluate performance and run conversational examples to witness real-time capabilities.
Benchmark
SwiftInfer is meticulously tested against its PyTorch predecessor to ensure efficiency and performance. Implementations are benchmarked on high-end hardware setups, revealing comprehensive insights through multiple conversational rounds, focusing on both robustness and adaptability to newer TensorRT versions. The empirical results promise improvements and adaptations aimed at expansive text generation scenarios.
Roadmap
The project has methodically ticked off several key objectives, including attention mechanisms specific to TensorRT-LLM, advanced caching methods, and incorporating seamless conversation flow capabilities. Future plans hint at continued enhancements and integrating advanced features to keep pace with technological advancements.
Acknowledgement
SwiftInfer’s development journey draws inspiration and technical insight from the pioneering Streaming-LLM efforts and comprehensive resources provided by the TensorRT-LLM community. The project extends its gratitude to these foundational contributions which have been instrumental in sculpting SwiftInfer into a viable, open-source production tool.
Citation
For those who find utility in SwiftInfer, the project acknowledges the impactful contributions of initial research by Guangxuan Xiao and colleagues at MIT Han Lab. Researchers and practitioners are encouraged to cite SwiftInfer alongside these foundational works to appreciate its integrated technological lineage.