# FAQ¤

## Optax is throwing an error.¤

Probably you're writing code that looks like

optim = optax.adam(learning_rate)
optim.init(model)

and getting an error that looks like
TypeError: zeros_like requires ndarray or scalar arguments, got <class 'jax._src.custom_derivatives.custom_jvp'> at position 0.


This can be fixed by doing

optim.init(eqx.filter(model, eqx.is_array))

which after a little thought should make sense: Optax can only optimise JAX arrays. It's not meaningful to ask Optax to optimise whichever other arbitrary Python objects may be a part of your model. (e.g. the activation function of an eqx.nn.MLP).

## A module saved in two places has become two independent copies.¤

Probably you're doing something like

class Module(eqx.Module):
linear1: eqx.nn.Linear
linear2: eqx.nn.Linear

def __init__(...):
shared_linear = eqx.nn.Linear(...)
self.linear1 = shared_linear
self.linear2 = shared_linear

in which the same object is saved multiple times in the model.

Don't do this!

After making some gradient updates you'll find that self.linear1 and self.linear2 are now different.

Recall that in Equinox, models are PyTrees. Meanwhile, JAX treats all PyTrees as trees: that is, the same object does not appear more in the tree than once. (If it did, then it would be a directed acyclic graph instead.) If JAX ever encounters the same object multiple times then it will unwittingly make independent copies of the object whenever it transforms the overall PyTree.

The resolution is simple: just don't store the same object in multiple places in the PyTree.

## How do I input higher-order tensors (e.g. with batch dimensions) into my model?¤

Use jax.vmap. This maps arbitrary JAX operations -- including any Equinox module -- over additional dimensions (such as batch dimensions).

For example if x is an array/tensor of shape (batch_size, input_size), then the following PyTorch code:

import torch
linear = torch.nn.Linear(input_size, output_size)

y = linear(x)


is equivalent to the following Equinox code:

import jax
import equinox as eqx
key = jax.random.PRNGKey(seed=0)
linear = eqx.nn.Linear(input_size, output_size, key=key)

y = jax.vmap(linear)(x)


## TypeError: not a valid JAX type.¤

You might be getting an error like

TypeError: Argument '<function ...>' of type <class 'function'> is not a valid JAX type.

Example:
import jax
import equinox as eqx

def loss_fn(model, x, y):
return ((model(x) - y) ** 2).mean()

model = eqx.nn.Lambda(lambda x: x)
model = eqx.nn.MLP(2, 2, 2, 2, key=jax.random.PRNGKey(0))

x = jax.numpy.arange(2)
y = x * x

try:
jax.jit(loss_fn)(model, x, y) # error
except TypeError as e:
print(e)

eqx.filter_jit(loss_fn)(model, x, y) # ok


This error happens because a model, when treated as a PyTree, may have leaves that are not JAX types (such as functions). It only makes sense to trace arrays. Filtering is used to handle this.

Instead of jax.jit, use equinox.filter_jit. Likewise for other transformations.