diffsol-jax 0.0.2

JAX bindings for the diffsol Rust ODE solver, callable inside jax.jit and differentiable with jax.grad.

JAX wrapper around diffsol, a Rust ODE solver library. Exposes diffsol's ODE solvers via jax.ffi so they can be called from inside jax.jit and differentiated with jax.grad.

The user writes an RHS function in Python, which gets lowered to a diffsl source string and compiled on first call.

Example usage

import jax
import jax.numpy as jnp
from diffsol_jax import ODEProblem

def lotka_volterra(t, y, p):
    x, yy = y[0], y[1]
    alpha, beta, delta, gamma = p[0], p[1], p[2], p[3]
    return (alpha * x - beta * x * yy, delta * x * yy - gamma * yy)

params = jnp.array([1.5, 1.0, 0.75, 3.0])
y0     = jnp.array([1.0, 0.5])

problem = ODEProblem(lotka_volterra, y0=y0, params=params)

t_span = jnp.array([0.0, 10.0])
ts, ys = jax.jit(lambda p: problem.solve(p, t_span))(params)
# ts: float64[200],  ys: float64[200, 2]

Limitations

• CPU only
• No vmap batching rule; vmap_method="sequential" gives correct results via a Python loop.
• The DiffSL lowerer handles elementwise ops and the common ODE patterns (Lotka-Volterra, Lorenz). Operations like dot_general, reduce_sum, and concatenate are not supported, so neural ODEs are not supported yet.
• Differentiation uses JAX-level forward sensitivities (no separate adjoint pass). DiffSL modules are JIT-compiled with Cranelift and cached per source string; the first call compiles, subsequent calls reuse the compiled module and update parameters via set_params.
• Does not work with jax.pmap
• Higher-order derivatives