PyTorch Model with Metal Acceleration: Performance and Correctness

This article presents a PyTorch-based model that attempts to leverage Metal for accelerated computation while providing pure PyTorch fallbacks to guarantee correctness. The model's core involves complex calculations including matrix multiplications, cumulative sums, and exponentiation. To enhance performance, the authors attempt to use Metal for custom kernels, but fall back to a pure PyTorch implementation if the Metal extension isn't available. This design ensures compatibility and reliability across different hardware platforms, offering developers a solution that balances performance and correctness.