https://github.com/patrick-kidger/sympy2jax

Turn SymPy expressions into trainable JAX expressions.
https://github.com/patrick-kidger/sympy2jax

jax sympy

Last synced: 15 days ago
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Turn SymPy expressions into trainable JAX expressions.

• Host: GitHub
• URL: https://github.com/patrick-kidger/sympy2jax
• Owner: patrick-kidger
• License: apache-2.0
• Created: 2022-06-23T13:33:02.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2024-04-20T09:31:34.000Z (7 months ago)
• Last Synced: 2024-10-17T10:06:44.186Z (27 days ago)
• Topics: jax, sympy
• Language: Python
• Homepage:
• Size: 26.4 KB
• Stars: 318
• Watchers: 9
• Forks: 12
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

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README

        
sympy2jax


Turn SymPy expressions into trainable JAX expressions. The output will be an [Equinox](https://github.com/patrick-kidger/equinox) module with all SymPy floats (integers, rationals, ...) as leaves. SymPy symbols will be inputs.

Optimise your symbolic expressions via gradient descent!

## Installation

```bash

pip install sympy2jax

```

Requires:  

Python 3.7+  

JAX 0.3.4+  

Equinox 0.5.3+  

SymPy 1.7.1+.

## Example

```python

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.)

```

## Documentation

```python

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.)

## See also: other libraries in the JAX ecosystem

**Always useful**  

[Equinox](https://github.com/patrick-kidger/equinox): neural networks and everything not already in core JAX!  

[jaxtyping](https://github.com/patrick-kidger/jaxtyping): type annotations for shape/dtype of arrays.  

**Deep learning**  

[Optax](https://github.com/deepmind/optax): first-order gradient (SGD, Adam, ...) optimisers.  

[Orbax](https://github.com/google/orbax): checkpointing (async/multi-host/multi-device).  

[Levanter](https://github.com/stanford-crfm/levanter): scalable+reliable training of foundation models (e.g. LLMs).  

**Scientific computing**  

[Diffrax](https://github.com/patrick-kidger/diffrax): numerical differential equation solvers.  

[Optimistix](https://github.com/patrick-kidger/optimistix): root finding, minimisation, fixed points, and least squares.  

[Lineax](https://github.com/patrick-kidger/lineax): linear solvers.  

[BlackJAX](https://github.com/blackjax-devs/blackjax): probabilistic+Bayesian sampling.  

[PySR](https://github.com/milesCranmer/PySR): symbolic regression. (Non-JAX honourable mention!)  

**Awesome JAX**  

[Awesome JAX](https://github.com/n2cholas/awesome-jax): a longer list of other JAX projects.