PyTorch Conference Europe 2026

Modern ML compilers follow a familiar pattern: a frontend lowers models into an intermediate representation, while a backend applies graph and kernel optimizations before generating code for target accelerators. PyTorch provides strong foundations through nn.Module, FX, and graph capture, but implementing optimized backends remains challenging due to hardware diversity and kernel-level complexity.

Optimizing GPU kernels is hard. Few engineers do it well. Hardware architectures evolve yearly, and with hyperscalers, chip makers, and AI labs building custom silicon, demand for efficient kernel generation keeps growing. This creates a gap between model developers and hardware capabilities.

This talk explores coding agents as engineering tools for compiler construction, not general-purpose assistants. We discuss how agents can generate and refine backend components by analyzing model mathematics and hardware specifications to produce optimized kernels tailored to specific targets.

We present a compiler architecture built as a PyTorch add-on that accepts PyTorch models or FX graphs and produces executable artifacts, demonstrating practical integration with existing PyTorch workflows.