JAXbind: Bind any function to JAX
This solves the integration bottleneck for researchers and practitioners in machine learning and scientific computing who rely on external code, though it is incremental as it builds on JAX's existing framework.
The authors tackled the problem of integrating existing high-performance code into JAX, which often requires impractical reimplementation or deep expertise, by developing JAXbind, a tool that reduces effort to bind custom functions with full Jacobian support, enabling all JAX transformations.
JAX is widely used in machine learning and scientific computing, the latter of which often relies on existing high-performance code that we would ideally like to incorporate into JAX. Reimplementing the existing code in JAX is often impractical and the existing interface in JAX for binding custom code either limits the user to a single Jacobian product or requires deep knowledge of JAX and its C++ backend for general Jacobian products. With JAXbind we drastically reduce the effort required to bind custom functions implemented in other programming languages with full support for Jacobian-vector products and vector-Jacobian products to JAX. Specifically, JAXbind provides an easy-to-use Python interface for defining custom, so-called JAX primitives. Via JAXbind, any function callable from Python can be exposed as a JAX primitive. JAXbind allows a user to interface the JAX function transformation engine with custom derivatives and batching rules, enabling all JAX transformations for the custom primitive.