Use FlashAttention-2 for Faster Fine-tuning and Inference

How to use FlashAttention-2 for QLoRA fine-tuning

FlashAttention is a popular method to optimize the attention computation in the Transformer. It can significantly accelerate inference and fine-tuning for large language models (LLM).

FlashAttention is now implemented in many frameworks, including Hugging Face Transformers and Unsloth, and supports most of the recent LLMs.

unsloth: Faster and Memory-Efficient QLoRA Fine-tuning

In this article, I briefly describe how FlashAttention works and especially detail the optimizations brought by FlashAttention-2. We will see how to use it with Hugging Face Transformers and what kind of speedup you can expect when using it for QLoRA fine-tuning.

You can find the code for QLoRA fine-tuning using FlashAttention-2 in this notebook:

Last update: March 13th, 2024