Triton Layernorm Kernel | A MyTorch Sidequest

Code: https://github.com/priyammaz/MyTorch/blob/main/mytorch/nn/functional/fused_ops/layernorm.py LayerNorm is a crucial component of the transformer architecture because it stabilizes the scale of activations across layers, preventing signals from exploding or vanishing as models get deeper. This stability is what allows modern transformers to train efficiently and reliably. Today, we’ll implement a fused LayerNorm in Triton, covering both the forward and backward pass. Along the way, we also explore some important considerations. Sometimes it’s better to store intermediate values during the forward pass to reuse in the backward pass, while other times it’s more efficient to recompute them on the fly. Balancing this tradeoff between memory and compute is key to writing high-performance GPU kernels! Citations: I referenced triton-transformer from Lucidrains as well as LigerKernels for this! Prereqs: - Layernorm Derivation: https://www.youtube.com/watch?v=1ceCvJvyjzM&t=5645s Timestamps: 00:00:00 - What is Layernorm? 00:01:10 - Forward Pass Formulation 00:03:10 - Backward Pass Formulation 00:06:40 - Atomic Add for Gradient Accumulation 00:08:40 - Naive Layernorm 00:11:50 - Forward Pass Wrapper 00:15:00 - Store in forward and reuse in backward 00:17:30 - Finish Forward Pass Wrapper 00:19:00 - Triton Layernorm Forward Kernel 00:31:40 - Backward Pass Wrapper 00:34:18 - Triton Layernorm Backward Kernel 00:44:30 - Test / Debug Socials! X https://twitter.com/data_adventurer Instagram https://www.instagram.com/nixielights/ Linkedin https://www.linkedin.com/in/priyammaz/ Discord https://discord.gg/RaguqCTURA 🚀 Github: https://github.com/priyammaz 🌐 Website: https://www.priyammazumdar.com/