nnx.jit() cannot specify backend or device due to out_shardings

[wookayin]

System information

• OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Linux, but OS-agnostic
• Flax, jax, jaxlib versions (obtain with pip show flax jax jaxlib:
  • flax 0.10.6
  • jax 0.6.0
  • jaxlib 0.6.0 (cuda12)
• Python version: 3.12
• GPU/TPU model and memory: not relevant
• CUDA version (if applicable): 12.6

Problem you have encountered:

nnx.jit() doesn't seem to fully support backend=... or device=... parameters unlike the vanilla jax.jit() does, because out_shardings must be UNSPECIFIED when device is specified.

ValueError: If backend or device is specified on jit, then out_shardings should not be specified.

What you expected to happen:

nnx.jit(fn, backend=...) or nnx.jit(fn, device=...) should work seamlessly as the vanilla jit(). The minimal reproduction code below should run and print a [1, 4] array.

Logs, error messages, etc:

This is probably because nnx's jit wrapper always passes a 3-tuple (jax_in_shardings, kwargs_shardings, jax_out_shardings) to jax.jit according to the output of JitFn.

See https://github.com/google/flax/blob/main/flax/nnx/transforms/compilation.py#L379

Steps to reproduce:

A minimal reproduction:

import flax.nnx as nnx
import jax.numpy as jnp

model = nnx.Linear(3, 4, rngs=nnx.Rngs(0))


@nnx.jit(backend='cpu')
def foo(model: nnx.Linear, x):
    return model(x)


batch_size: int = 2
y = foo(model, jnp.ones([batch_size, 3]))
assert y.shape == (batch_size, 4)
print(y)

Expected: no error
Actual: ValueError: If backend or device is specified on jit, then out_shardings should not be specified.

[cgarciae]

Hey @wookayin, thanks for reporting this. I think it makes sense to support this, we'd have to tweak nnx.jit a little bit but it should be possible to avoid defining out_shardings under the hood if both user defined in_shardings and out_shardings are not provided.