diffusion-kernel

What problem does it solve?

This Skill streamlines the development and optimization of GPU kernels for diffusion models, addressing performance bottlenecks in critical components like normalization, attention, and elementwise operations.

Core Features & Use Cases

• JIT Kernel Development: Guides for writing and integrating custom Triton and CUDA kernels for diffusion models.
• Performance Profiling: Provides workflows for benchmarking and deep-diving into kernel performance using torch.profiler, nsys, and ncu.
• Use Case: A developer needs to optimize the RMSNorm layer in a diffusion model. They can use this Skill to write a highly efficient JIT CUDA kernel, test its correctness against PyTorch, benchmark its speedup, and profile it with ncu to ensure it saturates GPU memory bandwidth.

Quick Start

Use the diffusion-kernel skill to add a new Triton kernel for fused elementwise operations.