Troubleshooting¶

Every snippet on this page is executed by the test suite, so what is written here is what actually happens.

An inverse came back as NaN¶

Program inversion reports failure by returning NaN rather than raising. This is almost always one of the unsupported patterns:

import jax.numpy as jnp
from probjax.core import inverse

assert jnp.isnan(inverse(lambda x: x + jnp.tanh(x))(jnp.asarray(1.0)))

The full list is in Program inversion. The short version: a variable used more than once nonlinearly, fori_loop, a scan with a non-invertible carry, or inverse(inverse(f)). Affine reuse such as 3 * x - x is solved automatically. Check jnp.isfinite on results you have not seen before.

If the NaN is only in some elements, that is a guard firing — the inverse does not exist at those values, which is different from being unsupported. Turn it into an error with inverse_checks plus checkify, as shown in the guide.

"no usable INVERSE_LOGDET rule for ..."¶

The primitive inverts but has no log-determinant rule, and one is not guessed: differentiating an inverse elementwise is only valid for elementwise maps. Either avoid the primitive on the inverted path, or register a rule — register_rearrangement_inverse_logdet if it only moves elements around.

inverse alone still works in this case; only inverse_and_logabsdet refuses.

"Cannot abstractly evaluate a checkify.check"¶

checkify.check cannot be staged out by a plain jit. If you enabled inverse_checks(), the call must be wrapped in checkify.checkify:

import jax.numpy as jnp
from jax.experimental import checkify
from probjax.core import inverse
from probjax.core.registry import inverse_checks

f = lambda x: x * jnp.float32(0.0)
with inverse_checks():
    error, _ = checkify.checkify(inverse(f))(jnp.float32(5.0))

"custom_inverse ...: dynamic argument N is a Foo"¶

An argument that is not an array or a pytree of arrays reached the primitive. Either register the type as a JAX pytree so its arrays become visible, or list its position in static_argnums.

The related message "is a traced value, so it cannot be held as a static parameter" is the reverse: something traced was passed where a compile-time constant was expected. Pass it as a dynamic positional argument instead.

"definv was called twice"¶

A second registration of the same kind discards the first. Usually a module imported twice, or a decorator applied in a loop. Registering definv and then definv_and_logdet is fine and does not warn — that is the intended way to supply both.

Training loss is NaN¶

Eager fit warns about non-finite losses and returns its current parameters. The JIT path does not issue Python value-check warnings, so inspect valid losses explicitly. Restart from a finite snapshot after correcting the cause:

import jax
import jax.numpy as jnp
from probjax.nn.training import fit

def loss_fn(params, rng, batch):
    del rng
    return jnp.mean((batch["data"] - params["w"]) ** 2)

data = jax.random.normal(jax.random.key(0), (64, 2))
result = fit(
    loss_fn, {"w": jnp.zeros(2)}, jax.random.key(1), {"data": data},
    num_steps=20, learning_rate=1e-2,
)
assert jnp.all(jnp.isfinite(result.losses))

Lower the learning rate, tighten clip_norm (10.0 by default), or look for an unbounded transform in the model. Flows bound their scales precisely to avoid this, so a NaN from a stock flow is worth reporting.

Losses only arrive at the end of `fit`¶

The returned history is available after fit finishes. Pass callback=fn (with every=N) for state/info snapshots during the run, and return False from it to stop early. Callbacks run on the host, so they cannot be used inside JIT; see the training reference.

A list of arrays was treated as a stream¶

fit decides by container type, never by looking at the leaves: a list, generator or data loader is a stream of batches, while a dict, array or tuple is one batch. Pass one batch as a dict or tuple, e.g. {"data": x}.

JAX picks the wrong backend¶

import jax
print(jax.devices())

Force one with JAX_PLATFORMS=cpu (or metal,cpu) before importing JAX. Metal covers fewer primitives than CPU and CUDA; if something fails only there, confirm against JAX_PLATFORMS=cpu before reporting it.

`TracerArrayConversionError`¶

A traced value was used where Python needed a concrete one — int(), float(), if, or .item() inside a jit. Move the conversion outside the jitted function, or use jax.lax.cond and jnp.where for data-dependent branching.

Shape errors inside a model¶

jax.eval_shape gives the shapes without running anything, which is usually faster to read than a traceback:

import jax
import jax.numpy as jnp
from flax import nnx
from probjax.nn import MLP

model = MLP([4, 16, 2], rngs=nnx.Rngs(0))
print(jax.eval_shape(model, jnp.ones((3, 4))))