Flash Attention derived and coded from first principles with Triton (Python)

In this video, I'll be deriving and coding Flash Attention from scratch. I'll be deriving every operation we do in Flash Attention using only pen and "paper". Moreover, I'll explain CUDA and Triton from zero, so no prior knowledge of CUDA is required. To code the backwards pass, I'll first explain how the autograd system works in PyTorch and then derive the Jacobian of the matrix multiplication and the Softmax operation and use it to code the backwards pass. All the code will be written in Python with Triton, but no prior knowledge of Triton is required. I'll also explain the CUDA programming model from zero. Chapters 00:00:00 - Introduction 00:03:10 - Multi-Head Attention 00:09:06 - Why Flash Attention 00:12:50 - Safe Softmax 00:27:03 - Online Softmax 00:39:44 - Online Softmax (Proof) 00:47:26 - Block Matrix Multiplication 01:28:38 - Flash Attention forward (by hand) 01:44:01 - Flash Attention forward (paper) 01:50:53 - Intro to CUDA with examples 02:26:28 - Tensor Layouts 02:40:48 - Intro to Triton with examples 02:54:26 - Flash Attention forward (coding) 04:22:11 - LogSumExp trick in Flash Attention 2 04:32:53 - Derivatives, gradients, Jacobians 04:45:54 - Autograd 05:00:00 - Jacobian of the MatMul operation 05:16:14 - Jacobian through the Softmax 05:47:33 - Flash Attention backwards (paper) 06:13:11 - Flash Attention backwards (coding) 07:21:10 - Triton Autotuning 07:23:29 - Triton tricks: software pipelining 07:33:38 - Running the code This video won't only teach you one of the most influential algorithms in deep learning history; it'll also give you the knowledge you need to solve any new problem that involves writing CUDA or Triton kernels. Moreover, it'll give you the mathematical foundations to derive backwards passes! As usual, the code is available on GitHub: https://github.com/hkproj/triton-flas... 🚀Join Writer 🚀 If you're a ML researcher who wants to do research at the hottest AI startup in Silicon Valley, consider applying to Writer and help us make GPUs go brrrrrrrrr. Join Writer: https://writer.com/company/careers/