README.md

sparsejac: Efficient sparse Jacobians using Jax

v0.2.0

Sparse Jacobians are frequently encountered in the simulation of physical systems. Jax tranformations jacfwd and jacrev make it easy to compute dense Jacobians, but these are wasteful when the Jacobian is sparse. sparsejac provides a function to more efficiently compute the Jacobian if its sparsity is known. It makes use of the recently-introduced jax.experimental.sparse module.

Install

pip install sparsejac

Example

A trivial example with a diagonal Jacobian follows:

fn = lambda x: x**2
x = jax.random.uniform(jax.random.PRNGKey(0), shape=(10000,))

@jax.jit
def sparse_jacrev_fn(x):
  with jax.ensure_compile_time_eval():
    sparsity = jax.experimental.sparse.BCOO.fromdense(jnp.eye(10000))
    jacrev_fn = sparsejac.jacrev(fn, sparsity=sparsity)
  return jacrev_fn(x)

dense_jacrev_fn = jax.jit(jax.jacrev(fn))

assert jnp.all(sparse_jacrev_fn(x).todense() == dense_jacrev_fn(x))

%timeit sparse_jacrev_fn(x).block_until_ready()
%timeit dense_jacrev_fn(x).block_until_ready()

And, the performance improvement can easily be seen:

93.1 µs ± 17.2 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
182 ms ± 26.9 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

Performance

• In general, it is preferable to directly provide the sparsity, rather than obtaining it from a dense matrix.
• GPU may show minimal or no performance advantage over CPU.
• Users are encouraged to test jacrev and jacfwd on their specific problem to select the most performant option.