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fix: infer oop form for SDEProblem/SDEFunction with StaticArrays #2834

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Jul 3, 2024
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fix: infer oop form for SDEProblem/SDEFunction with StaticArrays #2834

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97e51fe
fix: infer oop form for SDEProblem/SDEFunction with StaticArrays
AayushSabharwal Jul 1, 2024
54df3cc
feat: generate vector noise function for diagonal noise matrix
AayushSabharwal Jul 2, 2024
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42 changes: 36 additions & 6 deletions src/systems/diffeqs/sdesystem.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -231,6 +231,9 @@ function generate_diffusion_function(sys::SDESystem, dvs = unknowns(sys),
if isdde
eqs = delay_to_function(sys, eqs)
end
if eqs isa AbstractMatrix && isdiag(eqs)
eqs = diag(eqs)
end
Comment on lines +234 to +236
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@ChrisRackauckas @AayushSabharwal I don't think this is correct code for symbolics. The problem is that isdiag calls iszero which for a non-Num will create a symbolic boolean expression, i.e.

julia> @variables A
e1-element Vector{Num}:
 A

julia> eq = 5*A^2 + 2*A + 65
65 + 2A + 5(A^2)

julia> iszero(eq)
false

julia> iszero(ModelingToolkit.unwrap(eq))
(65 + 2A + 5(A^2)) == 0

So this now explicitly breaks creating an SDESystem with unwrapped expressions.

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Also, as @TorkelE mentioned Catalyst master tests should now generally pass, so if Catalyst tests fail when running downstream tests on an MTK PR that is meaningful again (unlike before we updated Catalyst to MTK 9).

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The Catalyst v14 release hasn't been made yet, so it's still not testing the updated code?

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The problem is that isdiag calls iszero which for a non-Num will create a symbolic boolean expression, i.e.

Thanks, we need to have a different isdiag that uses isequal. @AayushSabharwal can you prioritize this?

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@isaacsas is there an issue open on this? If so, let's assign @AayushSabharwal to it.

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I opened #2868

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The Catalyst v14 release hasn't been made yet, so it's still not testing the updated code?

Also, doesn't this:

https://github.com/SciML/ModelingToolkit.jl/actions/runs/9772401615/job/26976712250#step:6:579

indicate it was tested against Catalyst master?

u = map(x -> time_varying_as_func(value(x), sys), dvs)
p = if has_index_cache(sys) && get_index_cache(sys) !== nothing
reorder_parameters(get_index_cache(sys), ps)
Expand Down Expand Up @@ -403,14 +406,14 @@ function Girsanov_transform(sys::SDESystem, u; θ0 = 1.0)
checks = false)
end

function DiffEqBase.SDEFunction{iip}(sys::SDESystem, dvs = unknowns(sys),
function DiffEqBase.SDEFunction{iip, specialize}(sys::SDESystem, dvs = unknowns(sys),
ps = parameters(sys),
u0 = nothing;
version = nothing, tgrad = false, sparse = false,
jac = false, Wfact = false, eval_expression = false,
eval_module = @__MODULE__,
checkbounds = false,
kwargs...) where {iip}
kwargs...) where {iip, specialize}
if !iscomplete(sys)
error("A completed `SDESystem` is required. Call `complete` or `structural_simplify` on the system before creating an `SDEFunction`")
end
Expand Down Expand Up @@ -480,7 +483,7 @@ function DiffEqBase.SDEFunction{iip}(sys::SDESystem, dvs = unknowns(sys),

observedfun = ObservedFunctionCache(sys; eval_expression, eval_module)

SDEFunction{iip}(f, g,
SDEFunction{iip, specialize}(f, g,
sys = sys,
jac = _jac === nothing ? nothing : _jac,
tgrad = _tgrad === nothing ? nothing : _tgrad,
Expand All @@ -505,6 +508,16 @@ function DiffEqBase.SDEFunction(sys::SDESystem, args...; kwargs...)
SDEFunction{true}(sys, args...; kwargs...)
end

function DiffEqBase.SDEFunction{true}(sys::SDESystem, args...;
kwargs...)
SDEFunction{true, SciMLBase.AutoSpecialize}(sys, args...; kwargs...)
end

function DiffEqBase.SDEFunction{false}(sys::SDESystem, args...;
kwargs...)
SDEFunction{false, SciMLBase.FullSpecialize}(sys, args...; kwargs...)
end

"""
```julia
DiffEqBase.SDEFunctionExpr{iip}(sys::AbstractODESystem, dvs = unknowns(sys),
Expand Down Expand Up @@ -583,14 +596,16 @@ function SDEFunctionExpr(sys::SDESystem, args...; kwargs...)
SDEFunctionExpr{true}(sys, args...; kwargs...)
end

function DiffEqBase.SDEProblem{iip}(sys::SDESystem, u0map = [], tspan = get_tspan(sys),
function DiffEqBase.SDEProblem{iip, specialize}(
sys::SDESystem, u0map = [], tspan = get_tspan(sys),
parammap = DiffEqBase.NullParameters();
sparsenoise = nothing, check_length = true,
callback = nothing, kwargs...) where {iip}
callback = nothing, kwargs...) where {iip, specialize}
if !iscomplete(sys)
error("A completed `SDESystem` is required. Call `complete` or `structural_simplify` on the system before creating an `SDEProblem`")
end
f, u0, p = process_DEProblem(SDEFunction{iip}, sys, u0map, parammap; check_length,
f, u0, p = process_DEProblem(
SDEFunction{iip, specialize}, sys, u0map, parammap; check_length,
kwargs...)
cbs = process_events(sys; callback, kwargs...)
sparsenoise === nothing && (sparsenoise = get(kwargs, :sparse, false))
Expand Down Expand Up @@ -628,6 +643,21 @@ function DiffEqBase.SDEProblem(sys::SDESystem, args...; kwargs...)
SDEProblem{true}(sys, args...; kwargs...)
end

function DiffEqBase.SDEProblem(sys::SDESystem,
u0map::StaticArray,
args...;
kwargs...)
SDEProblem{false, SciMLBase.FullSpecialize}(sys, u0map, args...; kwargs...)
end

function DiffEqBase.SDEProblem{true}(sys::SDESystem, args...; kwargs...)
SDEProblem{true, SciMLBase.AutoSpecialize}(sys, args...; kwargs...)
end

function DiffEqBase.SDEProblem{false}(sys::SDESystem, args...; kwargs...)
SDEProblem{false, SciMLBase.FullSpecialize}(sys, args...; kwargs...)
end

"""
```julia
DiffEqBase.SDEProblemExpr{iip}(sys::AbstractODESystem, u0map, tspan,
Expand Down
28 changes: 28 additions & 0 deletions test/sdesystem.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -614,3 +614,31 @@ sys2 = complete(sys2)
prob = SDEProblem(sys1, sts .=> [1.0, 0.0, 0.0],
(0.0, 100.0), ps .=> (10.0, 26.0))
solve(prob, LambaEulerHeun(), seed = 1)

# SDEProblem construction with StaticArrays
# Issue#2814
@parameters p d
@variables x(tt)
@brownian a
eqs = [D(x) ~ p - d * x + a * sqrt(p)]
@mtkbuild sys = System(eqs, tt)
u0 = @SVector[x => 10.0]
tspan = (0.0, 10.0)
ps = @SVector[p => 5.0, d => 0.5]
sprob = SDEProblem(sys, u0, tspan, ps)
@test !isinplace(sprob)
@test !isinplace(sprob.f)
@test_nowarn solve(sprob, ImplicitEM())

# Ensure diagonal noise generates vector noise function
@variables y(tt)
@brownian b
eqs = [D(x) ~ p - d * x + a * sqrt(p)
D(y) ~ p - d * y + b * sqrt(d)]
@mtkbuild sys = System(eqs, tt)
u0 = @SVector[x => 10.0, y => 20.0]
tspan = (0.0, 10.0)
ps = @SVector[p => 5.0, d => 0.5]
sprob = SDEProblem(sys, u0, tspan, ps)
@test sprob.f.g(sprob.u0, sprob.p, sprob.tspan[1]) isa SVector{2, Float64}
@test_nowarn solve(sprob, ImplicitEM())
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