Writing Tensor Programs in Lean · TorchLean Verified Examples (robertboy18.github.io)

A significant advancement has been made in the integration of Lean, a functional programming language and theorem prover, with TorchLean, a tensor library for performing GPU computations. The new capability allows users to write tensor programs in Lean that can be executed on either CPU or GPU while maintaining the ability to prove properties about those functions. This means that users can now define mathematical operations like updates or simulations in Lean, and run them efficiently on a GPU, benefiting from parallel processing without having to rewrite code in lower-level languages like CUDA. This development is particularly important for the AI/ML community as it bridges the gap between functional languages and hardware acceleration, making it easier to conduct computations that require both independence and dependency within the same calculations. Key features include the newly implemented autograd system for automatic differentiation, support for various numerical formats, and the ability to easily switch computational devices. The synthesis of functional programming principles with GPU-optimized execution allows researchers and developers to explore more complex models and calculations while ensuring correctness through Lean’s proof system, enhancing the robustness of AI/ML experiments.