Could not perform index operation scatter

[zhangylch]

Hi

Thank you for implementing the forward Lap. It significantly accelerates the Laplacian calculation. However, I am encountering an error when attempting to use sparsity. It seems that the sparsity is not being applied successfully. Here are the details of the issue:

Next is the minimal code to reproduce the error.

import folx
import jax
import time


def fwd(x):
    x = x.reshape(-1, 3)
    distances = jnp.sqrt(jnp.sum(jnp.square(x), axis=1))
    sph = jnp.zeros((2, distances.shape[0]))
    sph = sph.at[0].set(distances)
    sph = sph.at[1].set(distances * 5.0)
    return jnp.sum(x)



key = jax.random.PRNGKey(12)
x = jax.random.normal(key, (100,300))


lapl = jax.jit(jax.vmap(folx.ForwardLaplacianOperator(6)(fwd)))
jax.block_until_ready(lapl(x))
start_time = time.time()
jax.block_until_ready(lapl(x))
end_time = time.time()
print(end_time - start_time)

[n-gao]

Scatter ops are not yet implemented for sparse jacobians. folx should still work correctly but default to a dense implementation. I'm open to accept PRs that implement scatter ops.

[zhangylch]

Scatter ops are not yet implemented for sparse jacobians. folx should still work correctly but default to a dense implementation. I'm open to accept PRs that implement scatter ops.

Thanks!

[Riccardo231]

Scatter ops are not yet implemented for sparse jacobians. folx should still work correctly but default to a dense implementation. I'm open to accept PRs that implement scatter ops.

Hello, may I work on this? Thank you

[n-gao]

Hello, may I work on this? Thank you

For sure, I look forward to your implementation! If you need help don't hesitate to ask.