Closed
Description
Describe the example
#3210 changed how the output of generate_control_function expects its parameters. It now requires MTKParameters, which implicitly removed support for tracing the generated control function symbolically.
Minimal Reproducible Example 👇
Based on the example in the docs (which haven't been updated with the change to MTKParameters, and so are currently inaccurate).
using ModelingToolkit
import ModelingToolkit: t_nounits as t, D_nounits as D
@variables x(t)=0 u(t)=0 y(t)
@parameters k = 1 m = 1 # I added m = 1, which isn't in the docs, since the error only shows up when you have multiple parameters
eqs = [D(x) ~ -k * m * (x + u)
y ~ x]
@named sys = ODESystem(eqs, t)
f, x_sym, p_sym = ModelingToolkit.generate_control_function(sys, [u], simplify = true);
ps = [1, 1]
x_num = [rand()]
u_num = [rand()]
# Evaluate numerically
f[1](x_num, u_num, ps, 1) ≈ -prod(ps) * (x_num + u_num) # Works in v9.50.0
f[1](x_num, u_num, MTKParameters(io_sys, Dict(p_sym .=> ps), Dict([u => u_num[]])), t) ≈ -prod(ps) * (x_num + u_num) # Works in v9.51.0+
# Trace symbolically
f[1](x_sym, [u], p_sym, t) # Works in v9.50.0
f[1](x_sym, [u], MTKParameters(io_sys, Dict(p_sym .=> p_sym), Dict([u => u])), t) # Equivalent for v9.51.0+, but it doesn't workError & Stacktrace
For f[1](x_sym, [u], MTKParameters(io_sys, Dict(p_sym .=> p_sym), Dict([u => u])), t) in v9.51.0+:
ERROR: Could not evaluate value of parameter k. Missing values for variables in expression k.
Stacktrace:
[1] error(s::String)
@ Base ./error.jl:35
[2] MTKParameters(sys::ODESystem, p::Dict{…}, u0::Dict{…}; tofloat::Bool, use_union::Bool, t0::Nothing)
@ ModelingToolkit ~/.julia/packages/ModelingToolkit/NQQXr/src/systems/parameter_buffer.jl:154
[3] MTKParameters(sys::ODESystem, p::Dict{…}, u0::Dict{…})
@ ModelingToolkit ~/.julia/packages/ModelingToolkit/NQQXr/src/systems/parameter_buffer.jl:28
[4] top-level scope
@ ~/julia_projects/NeuralPDEtests/modelingtoolkittest.jl:22
Some type information was truncated. Use `show(err)` to see complete types.This error is from a specific check that the inputs to MTKParameters are not symbolic.
Trying to work around by not using MTKParameters also results in errors when you have multiple parameters, even for numerical evaluation.
For f[1](x_num, u_num, (ps,), 1) in v9.51.0+ (ps is in a tuple instead of in an MTKParameters object):
ERROR: BoundsError: attempt to access 2-element Vector{Int64} at index [3]
Stacktrace:
[1] throw_boundserror(A::Vector{Int64}, I::Tuple{Int64})
@ Base ./essentials.jl:14
[2] getindex
@ ./essentials.jl:916 [inlined]
[3] macro expansion
@ ~/.julia/packages/SymbolicUtils/jf8aQ/src/code.jl:480 [inlined]
[4] macro expansion
@ ~/.julia/packages/RuntimeGeneratedFunctions/M9ZX8/src/RuntimeGeneratedFunctions.jl:163 [inlined]
[5] macro expansion
@ ./none:0 [inlined]
[6] generated_callfunc
@ ./none:0 [inlined]
[7] (::RuntimeGeneratedFunctions.RuntimeGeneratedFunction{…})(::Vector{…}, ::Vector{…}, ::Tuple{…}, ::Int64)
@ RuntimeGeneratedFunctions ~/.julia/packages/RuntimeGeneratedFunctions/M9ZX8/src/RuntimeGeneratedFunctions.jl:150
[8] top-level scope
@ ~/julia_projects/NeuralPDEtests/modelingtoolkittest.jl:32
Some type information was truncated. Use `show(err)` to see complete types.For some reason, k and m would be elements 1 and 3 in the MTKParameters object, with u being element 2, but there's no way to predict that from the output or documentation of generate_control_function.
Not Working Environment (please complete the following information):
- Output of
using Pkg; Pkg.status()
Status `~/julia_projects/NeuralPDEtests/Manifest.toml`
[961ee093] ModelingToolkit v9.58.0- Output of
using Pkg; Pkg.status(; mode = PKGMODE_MANIFEST)
Status `~/julia_projects/NeuralPDEtests/Manifest.toml`
[47edcb42] ADTypes v1.11.0
[1520ce14] AbstractTrees v0.4.5
[7d9f7c33] Accessors v0.1.39
[79e6a3ab] Adapt v4.1.1
[66dad0bd] AliasTables v1.1.3
[ec485272] ArnoldiMethod v0.4.0
[4fba245c] ArrayInterface v7.17.1
[4c555306] ArrayLayouts v1.11.0
[e2ed5e7c] Bijections v0.1.9
[62783981] BitTwiddlingConvenienceFunctions v0.1.6
[8e7c35d0] BlockArrays v1.3.0
[70df07ce] BracketingNonlinearSolve v1.1.0
[2a0fbf3d] CPUSummary v0.2.6
[00ebfdb7] CSTParser v3.4.3
[7057c7e9] Cassette v0.3.14
[d360d2e6] ChainRulesCore v1.25.0
[fb6a15b2] CloseOpenIntervals v0.1.13
[861a8166] Combinatorics v1.0.2
[a80b9123] CommonMark v0.8.15
[38540f10] CommonSolve v0.2.4
[bbf7d656] CommonSubexpressions v0.3.1
[f70d9fcc] CommonWorldInvalidations v1.0.0
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[429524aa] Optim v1.10.0
[bac558e1] OrderedCollections v1.7.0
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Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated -m`- Output of
versioninfo()
Julia Version 1.11.2
Commit 5e9a32e7af2 (2024-12-01 20:02 UTC)
Build Info:
Official https://julialang.org/ release
Platform Info:
OS: Linux (x86_64-linux-gnu)
CPU: 20 × 12th Gen Intel(R) Core(TM) i9-12900H
WORD_SIZE: 64
LLVM: libLLVM-16.0.6 (ORCJIT, alderlake)
Threads: 20 default, 0 interactive, 10 GC (on 20 virtual cores)
Environment:
JULIA_NUM_THREADS = auto
JULIA_EDITOR = codeWorking Environment (please complete the following information):
- Output of
using Pkg; Pkg.status()
Status `~/julia_projects/NeuralPDEtests/Manifest.toml`
⌃ [961ee093] ModelingToolkit v9.50.0
Info Packages marked with ⌃ have new versions available and may be upgradable.- Output of
using Pkg; Pkg.status(; mode = PKGMODE_MANIFEST)
Status `~/julia_projects/NeuralPDEtests/Manifest.toml`
[47edcb42] ADTypes v1.11.0
[1520ce14] AbstractTrees v0.4.5
[7d9f7c33] Accessors v0.1.39
[79e6a3ab] Adapt v4.1.1
[66dad0bd] AliasTables v1.1.3
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⌃ [961ee093] ModelingToolkit v9.50.0
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[f517fe37] Polyester v0.7.16
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[27ebfcd6] Primes v0.5.6
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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`- Output of
versioninfo()
Julia Version 1.11.2
Commit 5e9a32e7af2 (2024-12-01 20:02 UTC)
Build Info:
Official https://julialang.org/ release
Platform Info:
OS: Linux (x86_64-linux-gnu)
CPU: 20 × 12th Gen Intel(R) Core(TM) i9-12900H
WORD_SIZE: 64
LLVM: libLLVM-16.0.6 (ORCJIT, alderlake)
Threads: 20 default, 0 interactive, 10 GC (on 20 virtual cores)
Environment:
JULIA_NUM_THREADS = auto
JULIA_EDITOR = codeAdditional context
As far as I can tell, the MTKParameters object includes both the parameters and the control inputs, but the generated control function doesn't use the control inputs in that object. This means that vector of parameters output by generate_control_function no longer tells us how we should order the parameters (and control inputs) as an input to the generated control function if we wanted to get around using MTKParameters. Users are locked in to using MTKParameters, which isn't always an option since it won't allow symbolic parameters.
The requirement of using MTKParameters breaks some code I'm working on in NeuralLyapunov.jl that calls the generated control function with symbolic variables, since MTKParameters errors when the supplied parameters are symbolic, so I'd appreciate some kind of support for that. (In my specific case, I need to take the f from generate_control_function and make a PDE like dot(grad(V,x), f(x,u,p,t) ~ 0 to be solved by NeuralPDE, which is why I need to symbolically trace f.)
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