Optimizing Transformer Models for Variable-Length Input Sequences

This post discusses optimizing the performance and reducing costs of training and deploying large-scale AI models, particularly focusing on the Transformer architecture and its attention mechanism. It introduces PyTorch NestedTensors, FlashAttention2, and xFormers as solutions to address the challenge of variable-length input sequences. The post also demonstrates how to integrate these optimizations into existing HuggingFace models with minimal code changes. A toy LLM model is defined, and a dataset containing sequences of variable lengths is created. The post concludes with a discussion on PyTorch SDPA with padding and provides a baseline experiment configuration for further analysis.