MethodError: no method matching applychain

[prbzrg]

Error:

ERROR: LoadError: MethodError: no method matching applychain(::@NamedTuple{layer_1::Lux.Dense{…}}, ::Matrix{Float32}, ::@NamedTuple{nn::@NamedTuple{…}}, ::@NamedTuple{nn::@NamedTuple{…}})

Closest candidates are:
  applychain(::NamedTuple{fields}, ::Any, ::Any, ::NamedTuple{fields}) where fields
   @ Lux C:\Users\prbzr\.julia\packages\Lux\bNV16\src\layers\containers.jl:482

in expression starting at D:\Codes\Mine\Bug Reports\br-2\br-2.jl:26
Some type information was truncated. Use `show(err)` to see complete types.

Code:

import ADTypes, DifferentiationInterface, Enzyme, Lux, ContinuousNormalizingFlows

Enzyme.API.runtimeActivity!(true)

nvars = 2^3
naugs = nvars
n_in = nvars + naugs
n = 2^6
nn = Lux.Chain(Lux.Dense(n_in => n_in, tanh))

icnf = ContinuousNormalizingFlows.construct(
    ContinuousNormalizingFlows.RNODE,
    nn,
    nvars,
    naugs;
    compute_mode = ContinuousNormalizingFlows.DIJacVecMatrixMode(
        ADTypes.AutoEnzyme(; function_annotation = Enzyme.Const),
    ),
    tspan = (0.0f0, 13.0f0),
    steer_rate = 1.0f-1,
    λ₃ = 1.0f-2,
)
ps, st = Lux.setup(icnf.rng, icnf)
r = rand(icnf.rng, Float32, nvars, n)

ContinuousNormalizingFlows.loss(icnf, ContinuousNormalizingFlows.TrainMode(), r, ps, st)

Environment:

Status `D:\Codes\Mine\Bug Reports\br-2\Project.toml`
  [47edcb42] ADTypes v1.7.1
  [00b1973d] ContinuousNormalizingFlows v0.24.0 `https://github.com/impICNF/ContinuousNormalizingFlows.jl.git#main`
  [a0c0ee7d] DifferentiationInterface v0.5.17
  [7da242da] Enzyme v0.12.36
  [b2108857] Lux v1.0.1

Status `D:\Codes\Mine\Bug Reports\br-2\Manifest.toml`
  [47edcb42] ADTypes v1.7.1
  [621f4979] AbstractFFTs v1.5.0
  [1520ce14] AbstractTrees v0.4.5
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Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m`

Julia Version 1.10.5
Commit 6f3fdf7b36 (2024-08-27 14:19 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Windows (x86_64-w64-mingw32)
  CPU: 12 × Intel(R) Core(TM) i7-10750H CPU @ 2.60GHz
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, skylake)
Threads: 12 default, 2 interactive, 7 GC (on 12 virtual cores)

[avik-pal]

Can you link the code causing this? Looking at the types it seems to be caused by the change in custom layer types -- https://lux.csail.mit.edu/dev/introduction/updating_to_v1#Breaking-Changes-2 (point 2)

[prbzrg]

the code is https://github.com/impICNF/ContinuousNormalizingFlows.jl/blob/main/test/regression_tests.jl and the CI is https://github.com/impICNF/ContinuousNormalizingFlows.jl/actions/runs/10762993612/job/29844016134

[avik-pal]

https://github.com/impICNF/ContinuousNormalizingFlows.jl/commit/2e55ff43ec2937f7b6004b3a195950946d23bb1c#diff-525588e68b2421901be164965272940effa093de733a3fd36c4b9a4344b8c20cL49 is very likely the cause

[prbzrg]

I think, the error comes from the fact that the fields are different for layers and st when AD applies. (in my case, Zygote over Enzyme.Forward)

@generated function applychain(
        layers::NamedTuple{fields}, x, ps, st::NamedTuple{fields}) where {fields}

[avik-pal]

It works if AD is not involved?

[avik-pal]

The fields of layers and st being different suggest an incorrect model construction or that the parameter / state structure was incorrectly manipulated.

[prbzrg]

I found the problem, I was calling with nn but ps and st of container. New AbstractLuxContainerLayer doesn't comply with it.