StreamingLLM Breakthrough: Handling Over 4 Million Tokens with 22.2x Inference Speedup

blockchain.news:

In the dynamic field of AI and large language models (LLMs), recent advancements have brought significant improvements in handling multi-round conversations. The challenge with LLMs like ChatGPT is maintaining generation quality during extended interactions, constrained by the input length and GPU memory limits. LLMs struggle with inputs longer than their training sequence and can collapse if the input exceeds the attention window, limited by GPU memory

The introduction of StreamingLLM by Xiao et al. published with title «EFFICIENT STREAMING LANGUAGE MODELS WITH ATTENTION SINKS»  from MIT has been a breakthrough. This method allows streaming text inputs of over 4 million tokens in multi-round conversations without compromising on inference speed and generation quality, achieving a remarkable 22.2 times speedup compared to traditional methods. However, StreamingLLM, implemented in native PyTorch, needed further optimization for practical applications requiring low cost, low latency, and high throughput.

Addressing this need, the Colossal-AI team developed SwiftInfer, a TensorRT-based implementation of StreamingLLM. This implementation enhances the inference performance of large language models by an additional 46%, making it an efficient solution for multi-round conversations.

SwiftInfer's combination with TensorRT inference optimization in the SwiftInfer project maintains all advantages of the original StreamingLLM while boosting inference efficiency. Using TensorRT-LLM's API, models can be constructed similarly to PyTorch models. It's crucial to note that StreamingLLM doesn't increase the context length the model can access but ensures model generation with longer dialog text inputs.

Colossal-AI, a PyTorch-based AI