Comments (2)
This is expected -- the output is a PyTree (of type Solution
), one of whose leaves is a jaxpr, which is not something JAX knows how to vmap. (i.e. to treat an axis of an array as a batch dimension).
The fix should be to use equinox.filter_vmap
, which is basically a wrapper to accomplish jax.vmap(..., out_axes=(<0 for all arrays, None for all non-arrays>)
.
from optimistix.
Thanks!
from optimistix.
Related Issues (20)
- Classical newton methods HOT 6
- Non-finite values in the root function are not handled well HOT 2
- Will constrained optimization be supported? HOT 4
- Behavior of BFGS HOT 2
- pytree output structure mismatch error in backprop during vmap HOT 9
- Incompatibility of least_squares and custom_vjp HOT 2
- Extracting intermediate function values/ losses from the solve HOT 4
- Zero implicit gradients when using `ImplicitAdjoint` with CG solver HOT 4
- Would an exhaustive grid search have a place in `optimistix`? HOT 2
- Using `optimistix` with an `equinox` model HOT 2
- Incompatibility with jax 0.4.27 HOT 1
- Possibly of interest HOT 1
- Unexpected behaviour with JAX version HOT 3
- Slow compile of least_squares with large dict parameters HOT 2
- Can't vmap across input using Gauss Newton fwd HOT 11
- Question: errorhandling, BFGS minimization, vmap, and best practices HOT 2
- Parallel multi start HOT 10
- Optimization across multidimensional array HOT 4
- Accessing the (success/not) result of the solver HOT 1
- `UnexpectedTracerError` when enabling `EQX_ON_ERROR=breakpoint` HOT 1
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