Conclusion
PyTorch 2.0 represents a major step in continuing to broaden support for ML developers by increasing performance while maintaining a simple, Pythonic interface. This performance uplift is made possible in large part by the new TorchInductor infrastructure, which in turn harnesses the Triton ML programming language and just-in-time compiler. AMD’s support for these technologies allows users to realize the full promise of the new PyTorch architecture.  Our GPU support in PyTorch 2.0 is just one manifestation of a larger vision around AI and machine learning. AI/ML plays an important role in multiple AMD product lines, including Instinct and Radeon GPUs, Alveo™ data center accelerators, and both Ryzen™ and EPYC processors. These hardware and software initiatives are all part of AMD’s Pervasive AI vision, and we look forward to addressing the many new challenges and opportunities of this dynamic space.