Custom bindings for Enzyme within JaX. Currently this is set up to allow you to automatically import, and automatically differentiate (both jvp and vjp) external C++ code into JaX. As Enzyme is language-agnostic, this can be extended for arbitrary programming languages (Julia, Swift, Fortran, Rust, and even Python)!

You can use

from enzyme_jax import cpp_call

# Forward-mode C++ AD example

@jax.jit
def something(inp):
    y = cpp_call(inp, out_shapes=[jax.core.ShapedArray([2, 3], jnp.float32)], source="""
        template<std::size_t N, std::size_t M>
        void myfn(enzyme::tensor<float, N, M>& out0, const enzyme::tensor<float, N, M>& in0) {
        out0 = 56.0f + in0(0, 0);
        }
        """, fn="myfn")
    return y

ones = jnp.ones((2, 3), jnp.float32)
primals, tangents = jax.jvp(something, (ones,), (ones,) )

# Reverse-mode C++ AD example

primals, f_vjp = jax.vjp(something, ones)
(grads,) = f_vjp((x,))

The easiest way to install is using pip.

# The project is available on PyPi and installable like
# a usual python package (https://pypi.org/project/enzyme-jax/)
pip install enzyme-jax

Requirements: bazel-5.3.0, clang++, python, python-virtualenv, python3-dev.

Build our extension with:

# Will create a whl in bazel-bin/enzyme_jax-VERSION-SYSTEM.whl
bazel build :enzyme_jax

Finally, install the built library with:

pip install bazel-bin/enzyme_jax-VERSION-SYSTEM.whl

Note that you cannot run code from the root of the git directory. For instance, in the code below, you have to first run cd test before running test.py.

cd test && python test.py