Attention

This repository implements several types of attention modules in PyTorch, including:

• Attention: The basic attention module
• Multi-head Attention: A multi-head attention module that performs attention on multiple different "heads"(each head is a set of Q, K, V) of the input sequence.
• Multi-Query Attention: A multi-query attention module that allows multiple queries and only one key, value to attend to the same input sequence.
• Grouped-Query Attention: A grouped query attention module that allows queries to be grouped together (each group include multiple queries and only one key) and attended to jointly.

<p align="center"> <img src="images/grouped-query-attention.png" /> </p>

• Linformer: which reduces the overall self-attention complexity from O(n²) to O(n) in both time and space.

<p align="center"> <img src="images/linformer.png" style="width: 45%; height: 45%"/> </p>

multi-query attention and grouped-query attention modules is an alternative to multi-head attention with much lower memory bandwidth requirements. They has been used in many models, the most famous of which are:

• multi-query attention: PaLM, AlphaDev, AlphaZero, Google Gemini
• grouped-query attention: llama2

I implemented it in a simple way, with the purpose of understanding how attention works. It is an unoptimized version. If you are looking for attention with better performance, I suggest:

• flash attention
• torch.nn.functional.scaled_dot_product_attention
• huggingface
• xformers

For more information, please see the following papers:

• Attention is All You Need (Vaswani et al., 2017)
• Fast Transformer Decoding: One Write-Head is All You Need (Noam Shazeer, 2019)
• GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints (Ainslie et al., 2023)
• Linformer: Self-Attention with Linear Complexity (Wang et al., 2020)