MethodError: The applicable method may be too new: running in world age 33714, while current world is 33900 #845

ghost · 2023-07-21T14:48:15Z

Hello,

I have been been facing an issue.

Depending on the uODE I am solving I am getting the error:

┌ Warning: EnzymeVJP tried and failed in the automated AD choice algorithm with the following error. (To turn off this printing, add `verbose = false` to the `solve` call)
└  SciMLSensitivity ~/.julia/packages/SciMLSensitivity/NhfkF/src/concrete_solve.jl:21
MethodError: no method matching asprogress(::Base.CoreLogging.LogLevel, ::String, ::Module, ::Symbol, ::Symbol, ::String, ::Int64)
The applicable method may be too new: running in world age 33714, while current world is 33900.

Closest candidates are:
  asprogress(::Any, ::Any, ::Any, ::Any, ::Any, ::Any, ::Any; progress, kwargs...) (method too new to be called from this world context.)
    ProgressLogging ~/.julia/packages/ProgressLogging/6KXlp/src/ProgressLogging.jl:156
  asprogress(::Any, ::ProgressLogging.Progress, ::Any...; _...) (method too new to be called from this world context.)
    ProgressLogging ~/.julia/packages/ProgressLogging/6KXlp/src/ProgressLogging.jl:155
  asprogress(::Any, ::ProgressLogging.ProgressString, ::Any...; _...) (method too new to be called from this world context.)
    ProgressLogging ~/.julia/packages/ProgressLogging/6KXlp/src/ProgressLogging.jl:200

and I do not understand why.

What is this world_age error and should I be worried?
I can turn it off with verbose=false, but I think it would be wiser to ask here.

Best Regards

The text was updated successfully, but these errors were encountered:

ghost · 2023-07-25T08:28:46Z

Anyone that can help me with this?

ChrisRackauckas · 2023-07-30T07:53:49Z

MWE?

ghost · 2023-07-30T09:33:09Z

What is the meaning of MWE?

prbzrg · 2023-07-30T10:16:11Z

https://en.wikipedia.org/wiki/Minimal_reproducible_example

ghost · 2023-07-30T11:03:40Z

Here is a minimal reproducible example of this:

using OrdinaryDiffEq, ModelingToolkit, DataDrivenDiffEq, SciMLSensitivity, DataDrivenSparse
using Optimization, OptimizationOptimisers, OptimizationOptimJL

using LinearAlgebra, Statistics

using ComponentArrays, Lux, Zygote, Plots, StableRNGs


rng = StableRNG(1111)

function example!(du, u, p, t)
    alpha = p[1];

    du[1] = (-alpha*(0.9*u[1] - 0.45) + 0.45)*u[3]; 

    du[2] = -alpha*(0.9*u[2]*u[3] - 0.225*u[5]) + 0.225*u[5]; 
    
    du[3] = -alpha*(0.9*u[3] ^ 2 + 0.225*u[4] - 0.225) - 0.45* u[1] - 0.225* u[4] + 0.225; 
    
    du[4] = -0.9*alpha*u[3]*u[4]; 
    
    du[5] = alpha*(0.225*u[2] - 0.9*u[3]*u[5]) - 0.225*u[2]; 
end


t_true= LinRange(0.0,5.0,300)
tspan = (0.0, 5.0)
u0 = [0.7, 0.1, -0.05, 0.2, 0.025];
p_ = [1.0];
prob = ODEProblem(example!, u0, tspan, p_)
solution = solve(prob, Vern7(), abstol = 1e-12, reltol = 1e-12, saveat = t_true)

Xₙ = Array(solution)
t = solution.t


U = Lux.Chain(  Lux.Dense(5, 50, Lux.tanh), 
                Lux.Dense(50, 5))

p, st = Lux.setup(rng, U)

# Define the hybrid model
function ude_dynamics!(du, u, p, t, p_true)
    û = U(u, p, st)[1]

    alpha = p_true[1];

    du[1] = alpha*(0.45*u[3] - 2*û[1]) + 0.45*u[3]; 

    du[2] = alpha*(0.225*u[5] - 2*û[2]) + 0.225*u[5]; 
    
    du[3] = -alpha*(0.225*u[4] + 2*û[3] - 0.225) - 0.45*u[1] - 0.225*u[4] + 0.225; 
    
    du[4] = -2*alpha*û[4]; 
    
    du[5] = alpha*(0.225*u[2] - 2*û[5]) - 0.225*u[2]; 
end

# Closure with the known parameter
nn_dynamics!(du, u, p, t) = ude_dynamics!(du, u, p, t, p_)
# Define the problem
prob_nn = ODEProblem(nn_dynamics!, Xₙ[:, 1], tspan, p)

function predict(θ, X = Xₙ[:, 1], T = t)
    _prob = remake(prob_nn, u0 = X, tspan = (T[1], T[end]), p = θ)

    Array(solve(_prob, Tsit5(), saveat = T,
                abstol = 1e-5, reltol = 1e-5))
end

function loss(θ)
    X̂ = predict(θ)
    mean(abs2, Xₙ .- X̂)
end

losses = Float64[]

callback = function (p, l)
    println("loss is called")
    push!(losses, l)
    if length(losses) % 1 == 0
        println("Current loss after $(length(losses)) iterations: $(losses[end])")
    end
    return false
end

adtype = Optimization.AutoZygote()
optf = Optimization.OptimizationFunction((x, p) -> loss(x), adtype)
optprob = Optimization.OptimizationProblem(optf, ComponentVector{Float64}(p))

res = Optimization.solve(optprob, ADAM(), callback = callback, maxiters = 10)

ChrisRackauckas · 2023-07-30T17:16:00Z

I cannot reproduce this.

Are you using a standard REPL? Are your packages all up to date?

ghost · 2023-07-31T08:22:19Z

Thanks for the input!

I am using REPL in vscode.

How can I install all of the the latest stable releases of the packages for SciML?

Edit: I when into package mode ] and typed up. Some of the packages went through an update. However, I still have a similar error.

Edit2: I open Julia inside the terminal and did the same thing as before (update packages). I got several updates and now I have an output like the one in your image. I guess I have duplicated packages?

Additionally, I am constantly getting:

┌ Warning: EnzymeVJP tried and failed in the automated AD choice algorithm with the following error. (To turn off this printing, add `verbose = false` to the `solve` call)
└ @ SciMLSensitivity ~/.julia/packages/SciMLSensitivity/ETXYy/src/concrete_solve.jl:21
Enzyme execution failed.
Mismatched activity for:   store i64 %9, i64 addrspace(10)* %8, align 8, !dbg !9, !tbaa !23 const val:   %9 = load i64, i64 addrspace(11)* %7, align 8, !dbg !9, !tbaa !23
Type tree: {[-1]:Pointer, [-1,0]:Pointer, [-1,0,-1]:Float@double, [-1,8]:Integer, [-1,9]:Integer, [-1,10]:Integer, [-1,11]:Integer, [-1,12]:Integer, [-1,13]:Integer, [-1,14]:Integer, [-1,15]:Integer, [-1,16]:Integer, [-1,17]:Integer, [-1,18]:Integer, [-1,19]:Integer, [-1,20]:Integer, [-1,21]:Integer, [-1,22]:Integer, [-1,23]:Integer, [-1,24]:Integer, [-1,25]:Integer, [-1,26]:Integer, [-1,27]:Integer, [-1,28]:Integer, [-1,29]:Integer, [-1,30]:Integer, [-1,31]:Integer, [-1,32]:Integer, [-1,33]:Integer, [-1,34]:Integer, [-1,35]:Integer, [-1,36]:Integer, [-1,37]:Integer, [-1,38]:Integer, [-1,39]:Integer}
You may be using a constant variable as temporary storage for active memory (https://enzyme.mit.edu/julia/stable/#Activity-of-temporary-storage). If not, please open an issue, and either rewrite this variable to not be conditionally active or use Enzyme.API.runtimeActivity!(true) as a workaround for now

Is this relevant for the calculations or can I just set verbose = false to suppress the output?

ChrisRackauckas · 2023-07-31T12:19:47Z

What does ]st show you?

ghost · 2023-07-31T13:46:08Z

That line outputs: Status ~/Desktop/myCase/Project.toml (empty project)

ChrisRackauckas · 2023-07-31T13:51:33Z

Did you add the packages😅 ?

ghost · 2023-07-31T14:01:39Z

I can execute the script, so they must somewhere (Or should I do something?) [sorry if this is a newbish question. I am still new to this whole thing]

Edit:
So I added everything with:
Pkg.add(["Pacakge1"..."PackageN"])

Updated everything (]up)

Now, I am back at having the

┌ Warning: EnzymeVJP tried and failed in the automated AD choice algorithm with the following error. (To turn off this printing, add `verbose = false` to the `solve` call)
└ @ SciMLSensitivity ~/.julia/packages/SciMLSensitivity/NhfkF/src/concrete_solve.jl:21
MethodError: no method matching asprogress(::Base.CoreLogging.LogLevel, ::String, ::Module, ::Symbol, ::Symbol, ::String, ::Int64)
The applicable method may be too new: running in world age 33718, while current world is 34053.

When I do ]st I get:

  [b0b7db55] ComponentArrays v0.14.0
  [2445eb08] DataDrivenDiffEq v1.2.0
  [5b588203] DataDrivenSparse v0.1.2
  [b2108857] Lux v0.5.0
  [961ee093] ModelingToolkit v8.63.0
  [7f7a1694] Optimization v3.15.2
  [36348300] OptimizationOptimJL v0.1.9
  [42dfb2eb] OptimizationOptimisers v0.1.5
  [1dea7af3] OrdinaryDiffEq v6.53.4
  [91a5bcdd] Plots v1.38.17
  [1ed8b502] SciMLSensitivity v7.35.1
  [860ef19b] StableRNGs v1.0.0
  [e88e6eb3] Zygote v0.6.62
  [37e2e46d] LinearAlgebra
  [10745b16] Statistics v1.9.0

ChrisRackauckas · 2023-08-01T04:08:29Z

Try this in the REPL? Directly in the v1.9.2 REPL? How did you install julia, is it the standard binary from juliaup?

ghost · 2023-08-01T09:16:08Z

Hi,
So I am using Windows subsystem linux (WSL).
I went to https://julialang.org/downloads/ and downloaded the 64-bit (glibc) [Generic Linux on x86].
I created a folder called Julia in my home directory and unpacked the files there.
Afterwards, I added the folder to my PATH: export PATH="/home/myComputer/Julia/julia-1.9.2/bin:$PATH"

With this, I had Julia available in the terminal.

Next, opened vscode went to the Extensions page [crtl + shift + X] amd typed Julia.
I installed Julia v1.47.2- Julia Language Support

Went to my .jl script and Opened the REPL [Alt + J followed by Alt + O]

Added the required packages [previous post].

Executed the script (highlight everything and press shift + Enter)

As I am writing this post I get the message:

┌ Warning: EnzymeVJP tried and failed in the automated AD choice algorithm with the following error. (To turn off this printing, add `verbose = false` to the `solve` call)
└ @ SciMLSensitivity ~/.julia/packages/SciMLSensitivity/NhfkF/src/concrete_solve.jl:21
MethodError: no method matching asprogress(::Base.CoreLogging.LogLevel, ::String, ::Module, ::Symbol, ::Symbol, ::String, ::Int64)
The applicable method may be too new: running in world age 33725, while current world is 33870.

Closest candidates are:
  asprogress(::Any, ::Any, ::Any, ::Any, ::Any, ::Any, ::Any; progress, kwargs...) (method too new to be called from this world context.)
   ProgressLogging ~/.julia/packages/ProgressLogging/6KXlp/src/ProgressLogging.jl:156
  asprogress(::Any, ::ProgressLogging.Progress, ::Any...; _...) (method too new to be called from this world context.)
   ProgressLogging ~/.julia/packages/ProgressLogging/6KXlp/src/ProgressLogging.jl:155
  asprogress(::Any, ::ProgressLogging.ProgressString, ::Any...; _...) (method too new to be called from this world context.)
   ProgressLogging ~/.julia/packages/ProgressLogging/6KXlp/src/ProgressLogging.jl:200

ghost · 2023-08-01T14:55:58Z

@ChrisRackauckas What version of the packages are you using?

The error is linked to the ┌ Warning: EnzymeVJP tried and failed in the automated AD choice algorithm with the following error. (To turn off this printing, add `verbose = false` to the `solve` call). which appears in line 21 of concrete_solve.jl

If I just set the verbose=false, the warn message will not trigger. If I do this, can I safely carry on with the calculations?

Best Regards

ChrisRackauckas · 2023-08-01T22:08:49Z

Wait, are you talking about the Enzyme warning and not an error?

ghost · 2023-08-02T08:10:11Z

When I set verbose=false I get no enzyme warning followed by the

The applicable method may be too new: running in world age 33725, while current world is 33870

What I am asking is: if I set verbose=false will this have any implication to the calculations?

ChrisRackauckas · 2023-08-02T13:42:00Z

No. It has been silent for a very long time, it's just telling you that you're missing a potential performance optimization to fix compatibility with Enzyme.

ChrisRackauckas closed this as completed May 20, 2024

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MethodError: The applicable method may be too new: running in world age 33714, while current world is 33900 #845

MethodError: The applicable method may be too new: running in world age 33714, while current world is 33900 #845

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MethodError: The applicable method may be too new: running in world age 33714, while current world is 33900 #845

MethodError: The applicable method may be too new: running in world age 33714, while current world is 33900 #845

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