attention-mechanisms-catalog

What problem does it solve?

This Skill helps you overcome the quadratic memory and time complexity of standard self-attention, enabling you to process much longer sequences and accelerate training/inference. It guides you through modern variants like Flash Attention, sparse attention, and linear attention to prevent GPU OOM errors and slow performance.

Core Features & Use Cases

• Complexity Management: Choose between exact (Flash, sparse) and approximate (linear) attention based on sequence length and memory constraints.
• Performance Optimization: Implement Flash Attention for 4x less memory and 2-3x faster processing without accuracy loss.
• Use Case: You're training a Transformer on documents with 8k tokens and hitting GPU memory limits. This skill directs you to use Flash Attention (if exactness is critical) or Longformer (sparse attention) to handle the long sequences efficiently.

Quick Start

I need to process sequences of 5000 tokens. What attention mechanism should I use to avoid GPU memory errors?