GNNLux NNConv returns its state without the nn wrapper, so the state can't be fed back in

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#727 1 comentario 0 reacciones 0 asignados Ver en GitHub

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@parth-patel010 ya está trabajando en esto.

Desde el 4/11/2026.

  • #728 de @parth-patel010 — fusionado

Evaluación

Dificultad
2/5
Tiempo estimado
1-3 horas
Aptitud para principiantes
35/100
Tipo de issue
Error
Claridad
Bien especificado
Estado de actividad
Activo
Stack tecnológico
julia
Área
machine-learning

Línea de trabajo

Start with GNNLux/src/layers/conv.jl at the reported NNConv state return around line 1673, then compare the state handling in EdgeConv and GINConv. Reproduce the failure by calling NNConv twice with the returned state or by running Lux.Training.single_train_step! twice; done means the returned state keeps the nn wrapper and the second call succeeds. The author says they plan to open a PR shortly, so check for work already underway.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

In GNNLux, NNConv returns the inner network's state directly instead of wrapping it as (; nn = ...). Its input state is (; nn = ...) (from initialstates, and the forward pass reads st.nn), so the returned state has a different structure than the one it expects. Feeding it back into the layer then fails, which is what a training loop does on its second step.

Minimal example:

using GNNLux, Lux, Random

rng = Random.Xoshiro(0)
g = GNNGraph([1, 1, 2, 3], [2, 3, 1, 1])
x = randn(rng, Float32, 3, g.num_nodes)
e = randn(rng, Float32, 10, g.num_edges)

l = NNConv(3 => 5, Dense(10 => 15), tanh)
ps, st = Lux.setup(rng, l)   # st == (nn = NamedTuple(),)

y, st2 = l(g, x, e, ps, st)  # st2 == NamedTuple()   <- `nn` wrapper is gone
l(g, x, e, ps, st2)
# ERROR: FieldError: type NamedTuple has no field `nn`; NamedTuple has no fields at all.

With a stateful inner network, e.g. Chain(Dense(10 => 15), BatchNorm(15)), the returned state is (layer_1 = ..., layer_2 = ...) and the second call fails the same way (type NamedTuple has no field nn, available fields: layer_1, layer_2).

The same thing happens with Lux.Training.single_train_step!: the first step works, the second one throws the FieldError, so an NNConv model can't be trained with the standard Lux training loop.

Expected: the returned state has the same structure as the input, (; nn = <updated nn state>), like EdgeConv and GINConv already do.

Actual: NNConv returns _getstate(nn) without the nn key (GNNLux/src/layers/conv.jl#L1673).

This is the same kind of problem that #715 fixed for GNNChain. The current tests don't catch it because test_lux_layer never passes the returned state back in.

Versions: Julia 1.13.3, GNNLux 0.3.0-DEV (master at aa2926c), Lux 1.31.5.

I'd like to fix this, will open a PR shortly.

Lenguaje dominante
Julia
Estrellas
310
Forks
75
Merge medio
2 d 15 h
PR fusionados (30 d)
10

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