Efficient Deployment of Long Context Large Language Models
, Machine Learning Developer, US Navy
, Machine Learning Developer, US Navy
Inference with long contexts has become increasingly important for enabling real-world applications of Large Language Models (LLMs), particularly in scenarios requiring the integration of external information such as Retrieval Augmented Generation (RAG) and function calling. This talk explains the practical challenges associated with deploying long context LLMs.
We begin by addressing the two core architectural obstacles to handling long contexts within transformer models: the positional encoding problem and the attention computation problem. First, the positional encoding problem is resolved using position interpolation methods, such as Yet another RoPE extensioN method (YaRN). Second, the O(n^2) attention computation problem is mitigated through the use of hardware-aware attention calculation methods like Flash Attention v2.
Moving beyond architectural challenges, a critical consideration in the transition of long context models from research to production is the significant GPU memory demands associated with serving full precision (FP16/BF16) models with long contexts. To tackle this issue, we examine two viable solutions: sparse attention mechanisms and quantization. By adopting these techniques, we demonstrate the potential for substantial reduction in GPU memory requirements, enabling the deployment of long context LLMs in production environments.