Turn SymPy expressions into trainable JAX expressions. The output will be an Equinox module with all SymPy floats (integers, rationals, ...) as leaves. SymPy symbols will be inputs.

Optimise your symbolic expressions via gradient descent!

pip install sympy2jax

Requires:
Python 3.7+
JAX 0.3.4+
Equinox 0.5.3+
SymPy 1.7.1+.

import jax
import sympy
import sympy2jax

x_sym = sympy.symbols("x_sym")
cosx = 1.0 * sympy.cos(x_sym)
sinx = 2.0 * sympy.sin(x_sym)
mod = sympy2jax.SymbolicModule([cosx, sinx])  # PyTree of input expressions

x = jax.numpy.zeros(3)
out = mod(x_sym=x)  # PyTree of results.
params = jax.tree_leaves(mod)  # 1.0 and 2.0 are parameters.
                               # (Which may be trained in the usual way for Equinox.)

sympytorch.SymbolicModule(expressions, extra_funcs=None, make_array=True)

Where:

• expressions is a PyTree of SymPy expressions.
• extra_funcs is an optional dictionary from SymPy functions to JAX operations, to extend the built-in translation rules.
• make_array is whether integers/floats/rationals should be stored as Python integers/etc., or as JAX arrays.

Instances can be called with key-value pairs of symbol-value, as in the above example.

Instances have a .sympy() method that translates the module back into a PyTree of SymPy expressions.

(That's literally the entire documentation, it's super easy.)

jaxtyping: type annotations for shape/dtype of arrays.

Equinox: neural networks.

Optax: first-order gradient (SGD, Adam, ...) optimisers.

Diffrax: numerical differential equation solvers.

Optimistix: root finding, minimisation, fixed points, and least squares.

Lineax: linear solvers.

BlackJAX: probabilistic+Bayesian sampling.

Orbax: checkpointing (async/multi-host/multi-device).

Eqxvision: computer vision models.

Levanter: scalable+reliable training of foundation models (e.g. LLMs).

PySR: symbolic regression. (Non-JAX honourable mention!)

This is not an official Google product.