PyTorch Conference Europe 2026

Custom kernels are essential for high-performance PyTorch workflows, but their integration often comes with a hidden cost. While torch.compile promises speedups, calling custom operations typically triggers graph-breaks: fallbacks to Eager mode that introduce overhead and negate your performance gains.

In this session, we provide a practical roadmap for making your extensions "compiler-aware". Using the Transformer Engine project as a case study, we will show how to utilize the custom_op extension point to bridge the gap between high-performance kernels and the torch.compile stack.

What you will learn:
• Identifying the Friction: How to profile and detect graph-breaks caused by custom extensions.
• The Registration Path: A walkthrough of the custom_op registration process for torch.compile.
• Solving the "Hard Parts": Strategies for handling complex Python-side logic that disrupts graph capture.
• Real-World Impact: How these integrations function within the Transformer Engine to maintain peak throughput.

Who should join: This talk is designed for developers building custom PyTorch extensions who want to understand how advanced operations fit into the compiled stack.