Latent Space: The AI Engineer Podcast — Practitioners talking LLMs, CodeGen, Agents, Multimodality, AI UX, GPU Infra and all things Software 3.0 cover image

Latent Space: The AI Engineer Podcast — Practitioners talking LLMs, CodeGen, Agents, Multimodality, AI UX, GPU Infra and all things Software 3.0

FlashAttention 2: making Transformers 800% faster w/o approximation - with Tri Dao of Together AI

Jul 26, 2023
54:31

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Quick takeaways

  • Flash Attention offers linear memory usage instead of quadratic, optimizing computation without approximating attention mechanism.
  • Flash Attention's optimizations focus on memory reading and writing, leading to faster computation and efficient resource utilization.

Deep dives

Flash Attention: A Memory-Efficient Alternative to Traditional Attention

Flash Attention is a new approach to attention that offers linear memory usage instead of the traditional quadratic usage. It achieves this by optimizing the computation to be more hardware-friendly without approximating the attention mechanism. The result is a significant speedup in training and inference, allowing for longer sequence lengths without sacrificing accuracy. Flash Attention has gained popularity and is being widely used in various libraries for tasks like language modeling and fine-tuning.

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