Decapodes conversion to SciML fails for Halfar #141
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minimal" reproducing example (not minimal because I don't have a sufficient sense of decapode goings-on to easily reduce further). import Pkg
Pkg.add(name="Decapodes", rev="main")
Pkg.add(name="ACSets", rev="main")
Pkg.add([
"Catlab",
"CombinatorialSpaces",
"MLStyle",
"MultiScaleArrays",
"LinearAlgebra",
"OrdinaryDiffEq",
"JLD2",
"SparseArrays",
"Statistics",
"GLMakie",
"GeometryBasics",
"ComponentArrays"])
# AlgebraicJulia Dependencies
using Catlab
using Catlab.Graphics
using CombinatorialSpaces
using Decapodes
using ComponentArrays
# External Dependencies
using MLStyle
using MultiScaleArrays
using LinearAlgebra
using OrdinaryDiffEq
using JLD2
using SparseArrays
using Statistics
using GLMakie # Just for visualization
using GeometryBasics: Point2, Point3
Point2D = Point2{Float64};
Point3D = Point3{Float64};
@info("Packages Loaded")
halfar_eq2 = @decapode begin
h::Form0
Γ::Form1
n::Constant
ḣ == ∂ₜ(h)
ḣ == ∘(⋆, d, ⋆)(Γ * d(h) * avg₀₁(mag(♯(d(h)))^(n-1)) * avg₀₁(h^(n+2)))
end
glens_law = @decapode begin
Γ::Form1
(A,ρ,g,n)::Constant
Γ == (2/(n+2))*A*(ρ*g)^n
end
@info("Decapodes Defined")
ice_dynamics_composition_diagram = @relation () begin
dynamics(Γ,n)
stress(Γ,n)
end
ice_dynamics_cospan = oapply(ice_dynamics_composition_diagram,
[Open(halfar_eq2, [:Γ,:n]),
Open(glens_law, [:Γ,:n])])
ice_dynamics = apex(ice_dynamics_cospan)
ice_dynamics1D = expand_operators(ice_dynamics)
infer_types!(ice_dynamics1D, op1_inf_rules_1D, op2_inf_rules_1D)
resolve_overloads!(ice_dynamics1D, op1_res_rules_1D, op2_res_rules_1D)
s_prime = EmbeddedDeltaSet1D{Bool, Point2D}()
add_vertices!(s_prime, 100, point=Point2D.(range(-2, 2, length=100), 0))
add_edges!(s_prime, 1:nv(s_prime)-1, 2:nv(s_prime))
orient!(s_prime)
s = EmbeddedDeltaDualComplex1D{Bool, Float64, Point2D}(s_prime)
subdivide_duals!(s, Circumcenter())
@info("Spaces Defined")
function generate(sd, my_symbol; hodge=GeometricHodge())
e_vecs = map(edges(sd)) do e
point(sd, sd[e, :∂v0]) - point(sd, sd[e, :∂v1])
end
neighbors = map(vertices(sd)) do v
union(incident(sd, v, :∂v0), incident(sd, v, :∂v1))
end
n_vecs = map(neighbors) do es
[e_vecs[e] for e in es]
end
I = Vector{Int64}()
J = Vector{Int64}()
V = Vector{Float64}()
for e in 1:ne(s)
append!(J, [s[e,:∂v0],s[e,:∂v1]])
append!(I, [e,e])
append!(V, [0.5, 0.5])
end
avg_mat = sparse(I,J,V)
op = @match my_symbol begin
:♯ => x -> begin
map(neighbors, n_vecs) do es, nvs
sum([nv*norm(nv)*x[e] for (e,nv) in zip(es,nvs)]) / sum(norm.(nvs))
end
end
:mag => x -> begin
norm.(x)
end
:avg₀₁ => x -> begin
avg_mat * x
end
:^ => (x,y) -> x .^ y
:* => (x,y) -> x .* y
:show => x -> begin
@show x
x
end
x => error("Unmatched operator $my_symbol")
end
return (args...) -> op(args...)
end
sim = eval(gensim(ice_dynamics1D, dimension=1))
fₘ = sim(s, generate)
function f(flow_rate, ice_density, u_init_arr)
n = 3
ρ = ice_density
g = 9.8101
A = fill(flow_rate, ne(s))
tₑ = 5e13
u₀ = ComponentArray(dynamics_h = u_init_arr)
constants_and_parameters = (
n = n,
stress_ρ = ρ,
stress_g = g,
stress_A = A)
prob = ODEProblem{true, SciMLBase.FullSpecialize}(fₘ, u₀, (0, tₑ), constants_and_parameters)
@info("Solving")
soln = solve(prob, Tsit5())
@info("Done")
return soln(tₑ)
end
h₀ = map(x -> try sqrt(1. - x[1]^2) catch DomainError return 0.0 end, point(s_prime))
y = f(1e-16, 910., h₀)
Pkg.add("ForwardDiff")
using ForwardDiff: gradient;
gradient(x -> f(1e-16, 910., x), h₀)results in this stacktrace: ERROR: ArgumentError: cannot reinterpret an `ForwardDiff.Dual{nothing, Float64, 11}` array to `ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}` whose first dimension has size `99`.
The resulting array would have non-integral first dimension.
Stacktrace:
[1] (::Base.var"#thrownonint#326")(S::Type, T::Type, dim::Int64)
@ Base ./reinterpretarray.jl:53
[2] reinterpret
@ ./reinterpretarray.jl:71 [inlined]
[3] get_tmp(dc::PreallocationTools.FixedSizeDiffCache{Vector{Float64}, Vector{ForwardDiff.Dual{nothing, Float64, 11}}}, u::ComponentVector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}}, Tuple{ComponentArrays.Axis{(dynamics_h = 1:100,)}}})
@ PreallocationTools ~/.julia/packages/PreallocationTools/nhCNl/src/PreallocationTools.jl:51
[4] (::var"#f#34"{PreallocationTools.FixedSizeDiffCache{Vector{Float64}, Vector{ForwardDiff.Dual{nothing, Float64, 12}}}, PreallocationTools.FixedSizeDiffCache{Vector{Float64}, Vector{ForwardDiff.Dual{nothing, Float64, 12}}}, PreallocationTools.FixedSizeDiffCache{Vector{Float64}, Vector{ForwardDiff.Dual{nothing, Float64, 11}}}, PreallocationTools.FixedSizeDiffCache{Vector{Float64}, Vector{ForwardDiff.Dual{nothing, Float64, 11}}}, PreallocationTools.FixedSizeDiffCache{Vector{Float64}, Vector{ForwardDiff.Dual{nothing, Float64, 11}}}, var"#20#33"{var"#17#30"}, var"#20#33"{var"#16#29"{SparseMatrixCSC{Float64, Int64}}}, var"#20#33"{var"#15#28"}, var"#20#33"{var"#12#25"{Vector{Vector{Point2{Float64}}}, Vector{Vector{Int64}}}}, Diagonal{Float64, Vector{Float64}}, SparseMatrixCSC{Int64, Int64}, Diagonal{Float64, Vector{Float64}}, SparseMatrixCSC{Int64, Int64}})(du::ComponentVector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}}, Tuple{ComponentArrays.Axis{(dynamics_h = 1:100,)}}}, u::ComponentVector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}}, Tuple{ComponentArrays.Axis{(dynamics_h = 1:100,)}}}, p::NamedTuple{(:n, :stress_ρ, :stress_g, :stress_A), Tuple{Int64, Float64, Float64, Vector{Float64}}}, t::Float64)
@ Main ~/.julia/packages/Decapodes/AQT8L/src/simulation.jl:432
[5] ODEFunction
@ ~/.julia/packages/SciMLBase/eK30d/src/scimlfunctions.jl:2407 [inlined]
[6] initialize!(integrator::OrdinaryDiffEq.ODEIntegrator{Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, true, ComponentVector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}}, Tuple{ComponentArrays.Axis{(dynamics_h = 1:100,)}}}, Nothing, Float64, NamedTuple{(:n, :stress_ρ, :stress_g, :stress_A), Tuple{Int64, Float64, Float64, Vector{Float64}}}, Float64, Float64, Float64, Float64, Vector{ComponentVector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}}, Tuple{ComponentArrays.Axis{(dynamics_h = 1:100,)}}}}, ODESolution{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}, 2, Vector{ComponentVector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}}, Tuple{ComponentArrays.Axis{(dynamics_h = 1:100,)}}}}, Nothing, Nothing, Vector{Float64}, Vector{Vector{ComponentVector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}}, Tuple{ComponentArrays.Axis{(dynamics_h = 1:100,)}}}}}, ODEProblem{ComponentVector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}}, Tuple{ComponentArrays.Axis{(dynamics_h = 1:100,)}}}, Tuple{Float64, Float64}, true, NamedTuple{(:n, :stress_ρ, :stress_g, :stress_A), Tuple{Int64, Float64, Float64, Vector{Float64}}}, ODEFunction{true, SciMLBase.FullSpecialize, var"#f#34"{PreallocationTools.FixedSizeDiffCache{Vector{Float64}, Vector{ForwardDiff.Dual{nothing, Float64, 12}}}, PreallocationTools.FixedSizeDiffCache{Vector{Float64}, Vector{ForwardDiff.Dual{nothing, Float64, 12}}}, PreallocationTools.FixedSizeDiffCache{Vector{Float64}, Vector{ForwardDiff.Dual{nothing, Float64, 11}}}, PreallocationTools.FixedSizeDiffCache{Vector{Float64}, Vector{ForwardDiff.Dual{nothing, Float64, 11}}}, PreallocationTools.FixedSizeDiffCache{Vector{Float64}, Vector{ForwardDiff.Dual{nothing, Float64, 11}}}, var"#20#33"{var"#17#30"}, var"#20#33"{var"#16#29"{SparseMatrixCSC{Float64, Int64}}}, var"#20#33"{var"#15#28"}, var"#20#33"{var"#12#25"{Vector{Vector{Point2{Float64}}}, Vector{Vector{Int64}}}}, Diagonal{Float64, Vector{Float64}}, SparseMatrixCSC{Int64, Int64}, Diagonal{Float64, Vector{Float64}}, SparseMatrixCSC{Int64, Int64}}, UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, OrdinaryDiffEq.InterpolationData{ODEFunction{true, SciMLBase.FullSpecialize, var"#f#34"{PreallocationTools.FixedSizeDiffCache{Vector{Float64}, Vector{ForwardDiff.Dual{nothing, Float64, 12}}}, PreallocationTools.FixedSizeDiffCache{Vector{Float64}, Vector{ForwardDiff.Dual{nothing, Float64, 12}}}, PreallocationTools.FixedSizeDiffCache{Vector{Float64}, Vector{ForwardDiff.Dual{nothing, Float64, 11}}}, PreallocationTools.FixedSizeDiffCache{Vector{Float64}, Vector{ForwardDiff.Dual{nothing, Float64, 11}}}, PreallocationTools.FixedSizeDiffCache{Vector{Float64}, Vector{ForwardDiff.Dual{nothing, Float64, 11}}}, var"#20#33"{var"#17#30"}, var"#20#33"{var"#16#29"{SparseMatrixCSC{Float64, Int64}}}, var"#20#33"{var"#15#28"}, var"#20#33"{var"#12#25"{Vector{Vector{Point2{Float64}}}, Vector{Vector{Int64}}}}, Diagonal{Float64, Vector{Float64}}, SparseMatrixCSC{Int64, Int64}, Diagonal{Float64, Vector{Float64}}, SparseMatrixCSC{Int64, Int64}}, UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, Vector{ComponentVector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}}, Tuple{ComponentArrays.Axis{(dynamics_h = 1:100,)}}}}, Vector{Float64}, Vector{Vector{ComponentVector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}}, Tuple{ComponentArrays.Axis{(dynamics_h = 1:100,)}}}}}, OrdinaryDiffEq.Tsit5Cache{ComponentVector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}}, Tuple{ComponentArrays.Axis{(dynamics_h = 1:100,)}}}, ComponentVector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}}, Tuple{ComponentArrays.Axis{(dynamics_h = 1:100,)}}}, ComponentVector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}}, Tuple{ComponentArrays.Axis{(dynamics_h = 1:100,)}}}, typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}}, SciMLBase.DEStats, Nothing}, ODEFunction{true, SciMLBase.FullSpecialize, var"#f#34"{PreallocationTools.FixedSizeDiffCache{Vector{Float64}, Vector{ForwardDiff.Dual{nothing, Float64, 12}}}, PreallocationTools.FixedSizeDiffCache{Vector{Float64}, Vector{ForwardDiff.Dual{nothing, Float64, 12}}}, PreallocationTools.FixedSizeDiffCache{Vector{Float64}, Vector{ForwardDiff.Dual{nothing, Float64, 11}}}, PreallocationTools.FixedSizeDiffCache{Vector{Float64}, Vector{ForwardDiff.Dual{nothing, Float64, 11}}}, PreallocationTools.FixedSizeDiffCache{Vector{Float64}, Vector{ForwardDiff.Dual{nothing, Float64, 11}}}, var"#20#33"{var"#17#30"}, var"#20#33"{var"#16#29"{SparseMatrixCSC{Float64, Int64}}}, var"#20#33"{var"#15#28"}, var"#20#33"{var"#12#25"{Vector{Vector{Point2{Float64}}}, Vector{Vector{Int64}}}}, Diagonal{Float64, Vector{Float64}}, SparseMatrixCSC{Int64, Int64}, Diagonal{Float64, Vector{Float64}}, SparseMatrixCSC{Int64, Int64}}, UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, OrdinaryDiffEq.Tsit5Cache{ComponentVector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}}, Tuple{ComponentArrays.Axis{(dynamics_h = 1:100,)}}}, ComponentVector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}}, Tuple{ComponentArrays.Axis{(dynamics_h = 1:100,)}}}, ComponentVector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}}, Tuple{ComponentArrays.Axis{(dynamics_h = 1:100,)}}}, typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, OrdinaryDiffEq.DEOptions{Float64, ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}, Float64, Float64, PIController{Rational{Int64}}, typeof(DiffEqBase.ODE_DEFAULT_NORM), typeof(opnorm), Nothing, CallbackSet{Tuple{}, Tuple{}}, typeof(DiffEqBase.ODE_DEFAULT_ISOUTOFDOMAIN), typeof(DiffEqBase.ODE_DEFAULT_PROG_MESSAGE), typeof(DiffEqBase.ODE_DEFAULT_UNSTABLE_CHECK), DataStructures.BinaryHeap{Float64, DataStructures.FasterForward}, DataStructures.BinaryHeap{Float64, DataStructures.FasterForward}, Nothing, Nothing, Int64, Tuple{}, Tuple{}, Tuple{}}, ComponentVector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}}, Tuple{ComponentArrays.Axis{(dynamics_h = 1:100,)}}}, ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}, Nothing, OrdinaryDiffEq.DefaultInit}, cache::OrdinaryDiffEq.Tsit5Cache{ComponentVector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}}, Tuple{ComponentArrays.Axis{(dynamics_h = 1:100,)}}}, ComponentVector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}}, Tuple{ComponentArrays.Axis{(dynamics_h = 1:100,)}}}, ComponentVector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}}, Tuple{ComponentArrays.Axis{(dynamics_h = 1:100,)}}}, typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False})
@ OrdinaryDiffEq ~/.julia/packages/OrdinaryDiffEq/JJd6g/src/perform_step/low_order_rk_perform_step.jl:792
[7] __init(prob::ODEProblem{ComponentVector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}}, Tuple{ComponentArrays.Axis{(dynamics_h = 1:100,)}}}, Tuple{Float64, Float64}, true, NamedTuple{(:n, :stress_ρ, :stress_g, :stress_A), Tuple{Int64, Float64, Float64, Vector{Float64}}}, ODEFunction{true, SciMLBase.FullSpecialize, var"#f#34"{PreallocationTools.FixedSizeDiffCache{Vector{Float64}, Vector{ForwardDiff.Dual{nothing, Float64, 12}}}, PreallocationTools.FixedSizeDiffCache{Vector{Float64}, Vector{ForwardDiff.Dual{nothing, Float64, 12}}}, PreallocationTools.FixedSizeDiffCache{Vector{Float64}, Vector{ForwardDiff.Dual{nothing, Float64, 11}}}, PreallocationTools.FixedSizeDiffCache{Vector{Float64}, Vector{ForwardDiff.Dual{nothing, Float64, 11}}}, PreallocationTools.FixedSizeDiffCache{Vector{Float64}, Vector{ForwardDiff.Dual{nothing, Float64, 11}}}, var"#20#33"{var"#17#30"}, var"#20#33"{var"#16#29"{SparseMatrixCSC{Float64, Int64}}}, var"#20#33"{var"#15#28"}, var"#20#33"{var"#12#25"{Vector{Vector{Point2{Float64}}}, Vector{Vector{Int64}}}}, Diagonal{Float64, Vector{Float64}}, SparseMatrixCSC{Int64, Int64}, Diagonal{Float64, Vector{Float64}}, SparseMatrixCSC{Int64, Int64}}, UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, alg::Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, timeseries_init::Tuple{}, ts_init::Tuple{}, ks_init::Tuple{}, recompile::Type{Val{true}}; saveat::Tuple{}, tstops::Tuple{}, d_discontinuities::Tuple{}, save_idxs::Nothing, save_everystep::Bool, save_on::Bool, save_start::Bool, save_end::Nothing, callback::Nothing, dense::Bool, calck::Bool, dt::Float64, dtmin::Nothing, dtmax::Float64, force_dtmin::Bool, adaptive::Bool, gamma::Rational{Int64}, abstol::Nothing, reltol::Nothing, qmin::Rational{Int64}, qmax::Int64, qsteady_min::Int64, qsteady_max::Int64, beta1::Nothing, beta2::Nothing, qoldinit::Rational{Int64}, controller::Nothing, fullnormalize::Bool, failfactor::Int64, maxiters::Int64, internalnorm::typeof(DiffEqBase.ODE_DEFAULT_NORM), internalopnorm::typeof(opnorm), isoutofdomain::typeof(DiffEqBase.ODE_DEFAULT_ISOUTOFDOMAIN), unstable_check::typeof(DiffEqBase.ODE_DEFAULT_UNSTABLE_CHECK), verbose::Bool, timeseries_errors::Bool, dense_errors::Bool, advance_to_tstop::Bool, stop_at_next_tstop::Bool, initialize_save::Bool, progress::Bool, progress_steps::Int64, progress_name::String, progress_message::typeof(DiffEqBase.ODE_DEFAULT_PROG_MESSAGE), progress_id::Symbol, userdata::Nothing, allow_extrapolation::Bool, initialize_integrator::Bool, alias_u0::Bool, alias_du0::Bool, initializealg::OrdinaryDiffEq.DefaultInit, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
@ OrdinaryDiffEq ~/.julia/packages/OrdinaryDiffEq/JJd6g/src/solve.jl:502
[8] __init (repeats 5 times)
@ ~/.julia/packages/OrdinaryDiffEq/JJd6g/src/solve.jl:10 [inlined]
[9] __solve(::ODEProblem{ComponentVector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}}, Tuple{ComponentArrays.Axis{(dynamics_h = 1:100,)}}}, Tuple{Float64, Float64}, true, NamedTuple{(:n, :stress_ρ, :stress_g, :stress_A), Tuple{Int64, Float64, Float64, Vector{Float64}}}, ODEFunction{true, SciMLBase.FullSpecialize, var"#f#34"{PreallocationTools.FixedSizeDiffCache{Vector{Float64}, Vector{ForwardDiff.Dual{nothing, Float64, 12}}}, PreallocationTools.FixedSizeDiffCache{Vector{Float64}, Vector{ForwardDiff.Dual{nothing, Float64, 12}}}, PreallocationTools.FixedSizeDiffCache{Vector{Float64}, Vector{ForwardDiff.Dual{nothing, Float64, 11}}}, PreallocationTools.FixedSizeDiffCache{Vector{Float64}, Vector{ForwardDiff.Dual{nothing, Float64, 11}}}, PreallocationTools.FixedSizeDiffCache{Vector{Float64}, Vector{ForwardDiff.Dual{nothing, Float64, 11}}}, var"#20#33"{var"#17#30"}, var"#20#33"{var"#16#29"{SparseMatrixCSC{Float64, Int64}}}, var"#20#33"{var"#15#28"}, var"#20#33"{var"#12#25"{Vector{Vector{Point2{Float64}}}, Vector{Vector{Int64}}}}, Diagonal{Float64, Vector{Float64}}, SparseMatrixCSC{Int64, Int64}, Diagonal{Float64, Vector{Float64}}, SparseMatrixCSC{Int64, Int64}}, UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, ::Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}; kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
@ OrdinaryDiffEq ~/.julia/packages/OrdinaryDiffEq/JJd6g/src/solve.jl:5
[10] __solve
@ ~/.julia/packages/OrdinaryDiffEq/JJd6g/src/solve.jl:1 [inlined]
[11] #solve_call#34
@ ~/.julia/packages/DiffEqBase/NpZ7U/src/solve.jl:557 [inlined]
[12] solve_call
@ ~/.julia/packages/DiffEqBase/NpZ7U/src/solve.jl:523 [inlined]
[13] #solve_up#42
@ ~/.julia/packages/DiffEqBase/NpZ7U/src/solve.jl:1006 [inlined]
[14] solve_up
@ ~/.julia/packages/DiffEqBase/NpZ7U/src/solve.jl:992 [inlined]
[15] #solve#40
@ ~/.julia/packages/DiffEqBase/NpZ7U/src/solve.jl:929 [inlined]
[16] solve(prob::ODEProblem{ComponentVector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}}, Tuple{ComponentArrays.Axis{(dynamics_h = 1:100,)}}}, Tuple{Float64, Float64}, true, NamedTuple{(:n, :stress_ρ, :stress_g, :stress_A), Tuple{Int64, Float64, Float64, Vector{Float64}}}, ODEFunction{true, SciMLBase.FullSpecialize, var"#f#34"{PreallocationTools.FixedSizeDiffCache{Vector{Float64}, Vector{ForwardDiff.Dual{nothing, Float64, 12}}}, PreallocationTools.FixedSizeDiffCache{Vector{Float64}, Vector{ForwardDiff.Dual{nothing, Float64, 12}}}, PreallocationTools.FixedSizeDiffCache{Vector{Float64}, Vector{ForwardDiff.Dual{nothing, Float64, 11}}}, PreallocationTools.FixedSizeDiffCache{Vector{Float64}, Vector{ForwardDiff.Dual{nothing, Float64, 11}}}, PreallocationTools.FixedSizeDiffCache{Vector{Float64}, Vector{ForwardDiff.Dual{nothing, Float64, 11}}}, var"#20#33"{var"#17#30"}, var"#20#33"{var"#16#29"{SparseMatrixCSC{Float64, Int64}}}, var"#20#33"{var"#15#28"}, var"#20#33"{var"#12#25"{Vector{Vector{Point2{Float64}}}, Vector{Vector{Int64}}}}, Diagonal{Float64, Vector{Float64}}, SparseMatrixCSC{Int64, Int64}, Diagonal{Float64, Vector{Float64}}, SparseMatrixCSC{Int64, Int64}}, UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, args::Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False})
@ DiffEqBase ~/.julia/packages/DiffEqBase/NpZ7U/src/solve.jl:919
[17] f(flow_rate::Float64, ice_density::Float64, u_init_arr::Vector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}})
@ Main ~/GitRepo/causal_pyro/docs/source/pde_scratch/src/halfar_ice/ice_hackathon.jl:141
[18] #39
@ ~/GitRepo/causal_pyro/docs/source/pde_scratch/src/halfar_ice/ice_hackathon.jl:156 [inlined]
[19] chunk_mode_gradient(f::var"#39#40", x::Vector{Float64}, cfg::ForwardDiff.GradientConfig{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12, Vector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}}})
@ ForwardDiff ~/.julia/packages/ForwardDiff/PcZ48/src/gradient.jl:123
[20] gradient(f::Function, x::Vector{Float64}, cfg::ForwardDiff.GradientConfig{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12, Vector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}}}, ::Val{true})
@ ForwardDiff ~/.julia/packages/ForwardDiff/PcZ48/src/gradient.jl:0
[21] gradient(f::Function, x::Vector{Float64}, cfg::ForwardDiff.GradientConfig{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12, Vector{ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}}})
@ ForwardDiff ~/.julia/packages/ForwardDiff/PcZ48/src/gradient.jl:17
[22] gradient(f::Function, x::Vector{Float64})
@ ForwardDiff ~/.julia/packages/ForwardDiff/PcZ48/src/gradient.jl:17
[23] top-level scope
@ ~/GitRepo/causal_pyro/docs/source/pde_scratch/src/halfar_ice/ice_hackathon.jl:156
Collapse
This snippet was truncated for display; [see it in full](https://askemgroup.slack.com/files/U05A31F749Z/F066TS847NE/stacktrace.txt?origin_team=T03JAGZ2ZHU&origin_channel=C03MVAWRMPU) |
|
I would try out Barycenter() instead of Circumcenter() as a quick check |
|
Barycenter gives the same error. :( |
|
@ChrisRackauckas any progress to report on this issue? |
|
Any updates on this issue? |
|
I'm finding someone to investigate this. I don't suppose there's a more minimal reproducible example? |
|
Here's a slightly modified reproducer that throws an segfault_reproducer.jl# AlgebraicJulia Dependencies
using Catlab
using CombinatorialSpaces
using Decapodes
using ComponentArrays
# External Dependencies
using MLStyle
using MultiScaleArrays
using LinearAlgebra
using OrdinaryDiffEq
using JLD2
using SparseArrays
using Statistics
using GeometryBasics: Point2, Point3
const Point2D = Point2{Float64};
@info("Packages Loaded")
halfar_eq2 = @decapode begin
h::Form0
Γ::Form1
n::Constant
ḣ == ∂ₜ(h)
ḣ == ∘(⋆, d, ⋆)(Γ * d(h) * avg₀₁(mag(♯(d(h)))^(n-1)) * avg₀₁(h^(n+2)))
end
glens_law = @decapode begin
Γ::Form1
(A,ρ,g,n)::Constant
Γ == (2/(n+2))*A*(ρ*g)^n
end
@info("Decapodes Defined")
ice_dynamics_composition_diagram = @relation () begin
dynamics(Γ,n)
stress(Γ,n)
end
ice_dynamics_cospan = oapply(ice_dynamics_composition_diagram,
[Open(halfar_eq2, [:Γ,:n]),
Open(glens_law, [:Γ,:n])])
ice_dynamics = apex(ice_dynamics_cospan)
ice_dynamics1D = expand_operators(ice_dynamics)
infer_types!(ice_dynamics1D, op1_inf_rules_1D, op2_inf_rules_1D)
resolve_overloads!(ice_dynamics1D, op1_res_rules_1D, op2_res_rules_1D)
s_prime = EmbeddedDeltaSet1D{Bool, Point2D}()
add_vertices!(s_prime, 100, point=Point2D.(range(-2, 2, length=100), 0))
add_edges!(s_prime, 1:nv(s_prime)-1, 2:nv(s_prime))
orient!(s_prime)
s = EmbeddedDeltaDualComplex1D{Bool, Float64, Point2D}(s_prime)
subdivide_duals!(s, Circumcenter())
@info("Spaces Defined")
function generate(sd, my_symbol; hodge=GeometricHodge())
e_vecs = map(edges(sd)) do e
point(sd, sd[e, :∂v0]) - point(sd, sd[e, :∂v1])
end
neighbors = map(vertices(sd)) do v
union(incident(sd, v, :∂v0), incident(sd, v, :∂v1))
end
n_vecs = map(neighbors) do es
[e_vecs[e] for e in es]
end
I = Vector{Int64}()
J = Vector{Int64}()
V = Vector{Float64}()
for e in 1:ne(s)
append!(J, [s[e,:∂v0],s[e,:∂v1]])
append!(I, [e,e])
append!(V, [0.5, 0.5])
end
avg_mat = sparse(I,J,V)
op = @match my_symbol begin
:♯ => x -> begin
map(neighbors, n_vecs) do es, nvs
sum([nv*norm(nv)*x[e] for (e,nv) in zip(es,nvs)]) / sum(norm.(nvs))
end
end
:mag => x -> begin
norm.(x)
end
:avg₀₁ => x -> begin
avg_mat * x
end
:^ => (x,y) -> x .^ y
:* => (x,y) -> x .* y
:show => x -> begin
@show x
x
end
x => error("Unmatched operator $my_symbol")
end
return (args...) -> op(args...)
end
sim = eval(gensim(ice_dynamics1D, dimension=1))
fₘ = sim(s, generate)
function f(u_init_arr)
n = 3
ρ = 1e-16
g = 9.8101
A = fill(910, ne(s))
tₑ = 5e10
u₀ = ComponentArray(dynamics_h = u_init_arr)
constants_and_parameters = (
n = n,
stress_ρ = ρ,
stress_g = g,
stress_A = A)
prob = ODEProblem{true, SciMLBase.FullSpecialize}(fₘ, u₀, (0, tₑ), constants_and_parameters)
@info("Solving")
soln = solve(prob, Tsit5())
@info("Done")
return soln(tₑ)
end
h₀ = map(x -> sqrt(max(0.0, 1.0 - x[1]^2)), point(s_prime))
y = f(h₀)
using ForwardDiff: gradient;Invocation: julia --project --startup-file=no -e 'using Revise; includet("segfault_reproducer.jl")'Project.toml[deps]
ACSets = "227ef7b5-1206-438b-ac65-934d6da304b8"
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ArrayInterfaceStaticArraysCoreExt = "StaticArraysCore"
ArrayInterfaceTrackerExt = "Tracker"
[deps.ArrayInterface.weakdeps]
BandedMatrices = "aae01518-5342-5314-be14-df237901396f"
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CatlabSCSExt = "SCS"
CatlabTikzPicturesExt = "TikzPictures"
[deps.Catlab.weakdeps]
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|
This has another piece beyond the v6.65. begin
#= /Users/chrisrackauckas/.julia/packages/Decapodes/AQT8L/src/simulation.jl:205 =#
var"__dynamics_•1" = Decapodes.FixedSizeDiffCache(Vector{Float64}(undef, nparts(mesh, :E)))
var"__dynamics_•6" = Decapodes.FixedSizeDiffCache(Vector{Float64}(undef, nparts(mesh, :E)))
var"__•_8_1" = Decapodes.FixedSizeDiffCache(Vector{Float64}(undef, nparts(mesh, :E)))
var"__•_8_2" = Decapodes.FixedSizeDiffCache(Vector{Float64}(undef, nparts(mesh, :V)))
__dynamics_ḣ = Decapodes.FixedSizeDiffCache(Vector{Float64}(undef, nparts(mesh, :V)))
endthose are inappropriately eltyped. |
|
@ChrisRackauckas, I'm looking into this issue but having trouble making sense of what is going on or how to get started. Do you have any advice on how to get up to speed and contribute effectively to the resolution of this issue? |
|
Take a look at: sim = eval(gensim(ice_dynamics1D, dimension=1))
fₘ = sim(s, generate)
function f(flow_rate, ice_density, u_init_arr)
n = 3
ρ = ice_density
g = 9.8101
A = fill(flow_rate, ne(s))
tₑ = 5e13
u₀ = ComponentArray(dynamics_h = u_init_arr)
constants_and_parameters = (
n = n,
stress_ρ = ρ,
stress_g = g,
stress_A = A)
prob = ODEProblem{true, SciMLBase.FullSpecialize}(fₘ, u₀, (0, tₑ), constants_and_parameters)
@info("Solving")
soln = solve(prob, Tsit5())
@info("Done")
return soln(tₑ)
endSome choices have to be made since there's no way that can work. Effectively, Now there's a few ways to do this. One way to do this which I investigated was to move the gensim like: fₘ = sim(s, generate)
function f(flow_rate, ice_density, u_init_arr)
n = 3
ρ = ice_density
g = 9.8101
A = fill(flow_rate, ne(s))
tₑ = 5e13
u₀ = ComponentArray(dynamics_h = u_init_arr)
sim = eval(gensim(ice_dynamics1D, dimension=1, stateeltype = eltype(u₀)))
constants_and_parameters = (
n = n,
stress_ρ = ρ,
stress_g = g,
stress_A = A)
prob = ODEProblem{true, SciMLBase.FullSpecialize}(fₘ, u₀, (0, tₑ), constants_and_parameters)
@info("Solving")
soln = solve(prob, Tsit5())
@info("Done")
return soln(tₑ)
endThe downside to this is that the user has to be aware of what's going on here, and it's not all that fast because the function generation and recompilation isn't fast. Also, you cannot eval that and have to use RuntimeGeneratedFunctions, so it's a bit complex on the user. Also, the biggest issue, is that https://github.com/SciML/PreallocationTools.jl/blob/master/src/PreallocationTools.jl#L74 the restructure operation is effectively just a Now the other approach is to develop caches that are not context-dependent. The simplest thing is just to switch from a FixedSizeDiffCache to a DiffCache. This will resize when it's hit with an element type that is sized to be larger bits than expected. It will throw a warning about the resize the first time but other than that it's good to go. This requires the latest version here SciML/PreallocationTools.jl#90 which was the purpose of it, since you'd then need to change https://github.com/AlgebraicJulia/Decapodes.jl/blob/main/src/simulation.jl#L378 to use the element types, i.e. So the simplest thing here might be to just swap it to a If you really want to get crazy you could introduce a bump allocator https://github.com/MasonProtter/Bumper.jl and that would be a general solution here. But I would recommend trying the LBC first and then benchmark it. If the overhead is low enough in comparison to other operations it's a good stable and robust way of handling the AD nesting so we can leave it at that, though the annoyances of DiffCache have nerd sniped me into wanting to make sure DiffCache+ComponentArrays works better. |
|
With SciML/PreallocationTools.jl#93 IIUC the only way to get substantial (more than a few dictionary lookups per solve) savings compared to LazyBufferCache is to re-use the same memory for collections of different size and/or type or across different problems, none of which which seem likely to be worth the effort and complexity. Currently, Decapodes uses IIUC, the disadvantage of using mis-sized DiffCache instead of a correctly sizes TL;DR, switching from FixedSizeDiffCache to DiffCache at https://github.com/AlgebraicJulia/Decapodes.jl/blob/main/src/simulation.jl#L92 "just works" while using LazyBufferCache requires a bit more work. When I switched FixedSizeDiffCache to DiffCache there the forward pass continued to run successfully in about 12s and the gradient appears to run successfully, but either hangs or takes more than 10 minutes to complete which is likely slower than finite differencing would be. What sort of runtimes do the domain experts expect for computing this gradient? I have yet to get the forward pass to succeed with LazyBufferCache, though it should be possible, and maybe more performant given the problems with Logs |
Interesting. You didn't hit any reshape issues on ComponentArrays?
It should be under a minute. How are you calculating the gradient? This is the length 30 example? |
|
No reshape issues, but when I interrupted it threw a stack trace that was allocating which may indicate something isn't working with the pre-allocations.
I pasted the code in the OP into a REPL (excluding the Pkg operations and using a dev'ed version of Decapodes that uses DiffCache instead of FixedSizeDiffCache) |
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That was after moving the function construction into the loss? |
|
Before moving. I ran this: # AlgebraicJulia Dependencies
using Catlab
using Catlab.Graphics
using CombinatorialSpaces
using Decapodes
using ComponentArrays
# External Dependencies
using MLStyle
using MultiScaleArrays
using LinearAlgebra
using OrdinaryDiffEq
using JLD2
using SparseArrays
using Statistics
using GLMakie # Just for visualization
using GeometryBasics: Point2, Point3
Point2D = Point2{Float64};
Point3D = Point3{Float64};
@info("Packages Loaded")
halfar_eq2 = @decapode begin
h::Form0
Γ::Form1
n::Constant
ḣ == ∂ₜ(h)
ḣ == ∘(⋆, d, ⋆)(Γ * d(h) * avg₀₁(mag(♯(d(h)))^(n-1)) * avg₀₁(h^(n+2)))
end
glens_law = @decapode begin
Γ::Form1
(A,ρ,g,n)::Constant
Γ == (2/(n+2))*A*(ρ*g)^n
end
@info("Decapodes Defined")
ice_dynamics_composition_diagram = @relation () begin
dynamics(Γ,n)
stress(Γ,n)
end
ice_dynamics_cospan = oapply(ice_dynamics_composition_diagram,
[Open(halfar_eq2, [:Γ,:n]),
Open(glens_law, [:Γ,:n])])
ice_dynamics = apex(ice_dynamics_cospan)
ice_dynamics1D = expand_operators(ice_dynamics)
infer_types!(ice_dynamics1D, op1_inf_rules_1D, op2_inf_rules_1D)
resolve_overloads!(ice_dynamics1D, op1_res_rules_1D, op2_res_rules_1D)
s_prime = EmbeddedDeltaSet1D{Bool, Point2D}()
add_vertices!(s_prime, 100, point=Point2D.(range(-2, 2, length=100), 0))
add_edges!(s_prime, 1:nv(s_prime)-1, 2:nv(s_prime))
orient!(s_prime)
s = EmbeddedDeltaDualComplex1D{Bool, Float64, Point2D}(s_prime)
subdivide_duals!(s, Circumcenter())
@info("Spaces Defined")
function generate(sd, my_symbol; hodge=GeometricHodge())
e_vecs = map(edges(sd)) do e
point(sd, sd[e, :∂v0]) - point(sd, sd[e, :∂v1])
end
neighbors = map(vertices(sd)) do v
union(incident(sd, v, :∂v0), incident(sd, v, :∂v1))
end
n_vecs = map(neighbors) do es
[e_vecs[e] for e in es]
end
I = Vector{Int64}()
J = Vector{Int64}()
V = Vector{Float64}()
for e in 1:ne(s)
append!(J, [s[e,:∂v0],s[e,:∂v1]])
append!(I, [e,e])
append!(V, [0.5, 0.5])
end
avg_mat = sparse(I,J,V)
op = @match my_symbol begin
:♯ => x -> begin
map(neighbors, n_vecs) do es, nvs
sum([nv*norm(nv)*x[e] for (e,nv) in zip(es,nvs)]) / sum(norm.(nvs))
end
end
:mag => x -> begin
norm.(x)
end
:avg₀₁ => x -> begin
avg_mat * x
end
:^ => (x,y) -> x .^ y
:* => (x,y) -> x .* y
:show => x -> begin
@show x
x
end
x => error("Unmatched operator $my_symbol")
end
return (args...) -> op(args...)
end
sim = eval(gensim(ice_dynamics1D, dimension=1))
fₘ = sim(s, generate)
function f(flow_rate, ice_density, u_init_arr)
n = 3
ρ = ice_density
g = 9.8101
A = fill(flow_rate, ne(s))
tₑ = 5e13
u₀ = ComponentArray(dynamics_h = u_init_arr)
constants_and_parameters = (
n = n,
stress_ρ = ρ,
stress_g = g,
stress_A = A)
prob = ODEProblem{true, SciMLBase.FullSpecialize}(fₘ, u₀, (0, tₑ), constants_and_parameters)
@info("Solving")
soln = solve(prob, Tsit5())
@info("Done")
return soln(tₑ)
end
h₀ = map(x -> try sqrt(1. - x[1]^2) catch DomainError return 0.0 end, point(s_prime))
y = f(1e-16, 910., h₀)
using ForwardDiff: gradient;
gradient(x -> f(1e-16, 910., x), h₀)In this environment Where the diff from Decapodes main is - :($(Symbol(:__,c.name)) = Decapodes.FixedSizeDiffCache(Vector{$(c.T)}(undef, nparts(mesh, $(QuoteNode(resolved_form))))))
+ :($(Symbol(:__,c.name)) = Decapodes.DiffCache(Vector{$(c.T)}(undef, nparts(mesh, $(QuoteNode(resolved_form)))))) |
|
So it looks like we can apply that patch to main Decapodes and this will work? |
|
When I applied that patch locally this example hung for me. |
|
I can no longer reproduce the OP because the latest version of Decapodes is no longer compatible with CombinatorialSpaces v0.6.0. I am investigating that compatibility regression. methodology |
|
I can once more reproduce the OP, though I now need to add the statement using Decapodes: @decapode, Open, expand_operators, infer_types!, resolve_overloads!,
op1_inf_rules_1D, op2_inf_rules_1D, op1_res_rules_1D, op2_res_rules_1Das these names appear to no longer be exported. |
|
@LilithHafner These ought to be exported when using |
|
Yes. The OP with using DiagrammaticEquations
using DiagrammaticEquations.Decaadded also reproduces for me. |
|
Here are some solutions I've tried which did not work:
struct MyBufferCache
cache::Dict{Any, Any}
end
MyBufferCache() = MyBufferCache(Dict())
function get_tmp(cache::MyBufferCache, x::AbstractArray)
get(cache.cache, (axes(x), typeof(x))) do
copy(x)
end
endGives DimensionMismatch on
struct MyFixedSizeBufferCache{N}
cache::Dict{Any, Any}
size::NTuple{N, Int}
end
MyFixedSizeBufferCache(size...) = MyFixedSizeBufferCache(Dict(), size)
function PreallocationTools.get_tmp(cache::MyFixedSizeBufferCache, x)
get(cache.cache, typeof(x)) do
similar(x, cache.size...)
end
endSucceeds in the forward solve and hangs computing the gradient, though keyboard interruption works to stop the hang I plan to investigate why the error using FixedSizeDiffCache is thrown and why the warning using DiffCache is printed. Happy to hear ideas from other folks who might know what's going on or be able to interpret these results. [^1] A simplified excerpt of the generated dynamics is dynamics_h = u.dynamics_h
...
var"dynamics_•1" = Decapodes.get_tmp(var"__dynamics_•1", u)
...
# var"GenSim-M_d₀" is a 99x100 matrix
mul!(var"dynamics_•1", var"GenSim-M_d₀", dynamics_h) # <=== This line throws
...
dynamics_mult_3 .= Γ .* var"dynamics_•1"
dynamics_mult_1 = dynamics_mult_3 .* var"dynamics_•2"
dynamics_mult_2 = dynamics_mult_1 .* var"dynamics_•8"
# var"GenSim-M_GenSim-ConMat_1" is a 100x99 matrix
mul!(dynamics_ḣ, var"GenSim-M_GenSim-ConMat_1", dynamics_mult_2)I think the issue is that all the temporary variables are constructed in terms of The existing implementation does that by storing dims in the cache object and MyBufferCache |
|
When I change the caching system to avoid the errors in the gradient computation, I'm seeing the dynamics run substantially more times computing the gradient than computing the forward pass. Specifically, thy run between 13k and 27k times on the forward pass and more than 16 million times on the reverse pass. From @ChrisRackauckas "It should be under a minute. " I'm guessing this 500x increase is not expected. Do folks have any Idea why the dynamics would run so many times? |
|
@LilithHafner Is it in mutating form? Are you getting a fallback to ReverseDiff non-compiled mode for the vjp calculation? It should be throwing warnings about this. |
Having two dispatches could be helpful for the reverse mode. Reverse mode in some sense "necessarily" allocates in the vjp, though with enough sophistication we can work around that, though my gut says there's a warning being thrown in the hasbranching check |
Removing caching should have not semantic impact whatsoever (unless there is a caching bug like simultaneous uses of a temp from the same cache) I'll check though.
Yes. The dynamic function is defined as #= /home/x/.julia/dev/Decapodes/src/simulation.jl:597 =#
f(du, u, p, t) = begin
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:597 =#
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:598 =#
[...]
mul!(dynamics_ḣ, var"GenSim-M_GenSim-ConMat_1", dynamics_mult_2)
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:600 =#
getproperty(du, :dynamics_h) .= dynamics_ḣ
end
It's not throwing warnings, so probably not? |
|
Replacing all uses of a = get_temp(...)
mul!(a, b, c)with a = b * cand all uses of a = get_temp(...)
a .= ...with a = ...Has no impact. Also switching Removing all this caching increases the forward pass runtime by 1.4x from 92ms to 130ms. I think that (with some upstream fixes, too) switching from a FixedSizeDiffCache to a LazyBufferCache will fix the caching issue, but I think there is also a non-caching-related issue which causes gradient computation to either hang or run for a long time. |
|
Which vjp method is chosen for the reverse mode? |
|
How can I find out which vjp method is chosen for the reverse mode? (this is also ForwardDiff.gradient, so maybe there is no reverse mode?) Here's a stack trace. It includes a frame |
|
Oh if it's ForwardDiff then it's just forward mode and going to be slow. That should be changed to a Zygote call. |
|
The time for forward mode is O((np)), so the scaling will be extremely bad with that ( O((np)^3) with an implicit method) |
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That's fantastic news! I'll verify that this works well with Zygote and we should be good to go (with one or two more forthcoming upstream PRs) EDIT: I'm seeing a new batch of issues with Zygote. I'll investigate more on Monday. |
This was referenced Feb 10, 2024
|
Zygote can't AD this because of mutation. It seems like folks have made a fairly effective fallback system for when AD fails, but that system is still slow and has some inherent limitations. I agree with @ChrisRackauckas's comment that
Specifically, I think we should add a non-mutating option. If anyone is curious, the latest error I'm dealing with is Using the code # AlgebraicJulia Dependencies
using Catlab
using Catlab.Graphics
using CombinatorialSpaces
using Decapodes
using ComponentArrays
# External Dependencies
using MLStyle
using MultiScaleArrays
using LinearAlgebra
using OrdinaryDiffEq
using JLD2
using SparseArrays
using Statistics
using GLMakie # Just for visualization
using GeometryBasics: Point2, Point3
Point2D = Point2{Float64};
Point3D = Point3{Float64};
using DiagrammaticEquations
using DiagrammaticEquations.Deca
@info("Packages Loaded")
halfar_eq2 = @decapode begin
h::Form0
Γ::Form1
n::Constant
ḣ == ∂ₜ(h)
ḣ == ∘(⋆, d, ⋆)(Γ * d(h) * avg₀₁(mag(♯(d(h)))^(n-1)) * avg₀₁(h^(n+2)))
end
glens_law = @decapode begin
Γ::Form1
(A,ρ,g,n)::Constant
Γ == (2/(n+2))*A*(ρ*g)^n
end
@info("Decapodes Defined")
ice_dynamics_composition_diagram = @relation () begin
dynamics(Γ,n)
stress(Γ,n)
end
ice_dynamics_cospan = oapply(ice_dynamics_composition_diagram,
[Open(halfar_eq2, [:Γ,:n]),
Open(glens_law, [:Γ,:n])])
ice_dynamics = apex(ice_dynamics_cospan)
ice_dynamics1D = expand_operators(ice_dynamics)
infer_types!(ice_dynamics1D, op1_inf_rules_1D, op2_inf_rules_1D)
resolve_overloads!(ice_dynamics1D, op1_res_rules_1D, op2_res_rules_1D)
s_prime = EmbeddedDeltaSet1D{Bool, Point2D}()
add_vertices!(s_prime, 100, point=Point2D.(range(-2, 2, length=100), 0))
add_edges!(s_prime, 1:nv(s_prime)-1, 2:nv(s_prime))
orient!(s_prime)
s = EmbeddedDeltaDualComplex1D{Bool, Float64, Point2D}(s_prime)
subdivide_duals!(s, Circumcenter())
@info("Spaces Defined")
function generate(sd, my_symbol; hodge=GeometricHodge())
e_vecs = map(edges(sd)) do e
point(sd, sd[e, :∂v0]) - point(sd, sd[e, :∂v1])
end
neighbors = map(vertices(sd)) do v
union(incident(sd, v, :∂v0), incident(sd, v, :∂v1))
end
n_vecs = map(neighbors) do es
[e_vecs[e] for e in es]
end
I = Vector{Int64}()
J = Vector{Int64}()
V = Vector{Float64}()
for e in 1:ne(s)
append!(J, [s[e,:∂v0],s[e,:∂v1]])
append!(I, [e,e])
append!(V, [0.5, 0.5])
end
avg_mat = sparse(I,J,V)
op = @match my_symbol begin
:♯ => x -> begin
map(neighbors, n_vecs) do es, nvs
sum([nv*norm(nv)*x[e] for (e,nv) in zip(es,nvs)]) / sum(norm.(nvs))
end
end
:mag => x -> begin
norm.(x)
end
:avg₀₁ => x -> begin
avg_mat * x
end
:^ => (x,y) -> max.(x, 0) .^ y # x is sometimes a very small negative number due to numerical error
:* => (x,y) -> x .* y
:show => x -> begin
@show x
x
end
x => error("Unmatched operator $my_symbol")
end
return (args...) -> op(args...)
end
sim = eval(gensim(ice_dynamics1D, dimension=1))
fₘ = sim(s, generate)
function f(flow_rate, ice_density, u_init_arr)
n = 3
ρ = ice_density
g = 9.8101
A = fill(flow_rate, ne(s))
tₑ = 5e13
u₀ = ComponentArray(dynamics_h = u_init_arr)
# Note that this must be a ComponentArray to differentiate
constants_and_parameters = ComponentArray(
n = n,
stress_ρ = ρ,
stress_g = g,
stress_A = A)
prob = ODEProblem{true, SciMLBase.FullSpecialize}(fₘ, u₀, (0, tₑ), constants_and_parameters)
@info("Solving")
soln = solve(prob, Tsit5())
@info("Done")
# return soln(tₑ)
last(soln) # last, not soln(tₑ) because to avoid interpolation fails when AD fails.
end
h₀ = map(x -> try sqrt(1. - x[1]^2) catch DomainError return 0.0 end, point(s_prime))
y = f(1e-16, 910., h₀)
# Must use Zygote—ForwardDiff is asymtotically slow
using SciMLSensitivity
using Zygote
Zygote.jacobian(x -> f(1e-16, 910., x), h₀)With Decapodes configured to use LazyBufferCache and the patch from SciML/PreallocationTools.jl#102. @ChrisRackauckas, would you like me to look into adding an option to Decapodes to generate mutation-free code? |
|
I think that would be really helpful for benchmarking too. I definitely expected that our preallocations were saving more than 1.4X in runtime but we didn't implement a nonpreallocating version because we just assumed that memory traffic would be a significant performance problem that needed a solution. Removing the preallocation would make the code a lot more flexible and if it only costs 2X in runtime it is worth supporting that version too. We probably want to use a compiler configuration abstract type and dispatch rather than a bunch of kwargs and if/else to future proof as we might need more compiler options. |
|
Change soln = solve(prob, Tsit5())
@info("Done")
return soln(tₑ)to soln = solve(prob, Tsit5(), saveat = tₑ)
return Array(soln) |
Shout out to the folks who make the Julia GC
After that change I get |
|
what's the function you're generating now? |
quote
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:594 =#
function simulate(mesh, operators, hodge = GeometricHodge())
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:594 =#
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:595 =#
begin
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:185 =#
(var"GenSim-M_d₀", d₀) = default_dec_matrix_generate(mesh, :d₀, hodge)
(var"GenSim-M_⋆₁", ⋆₁) = default_dec_matrix_generate(mesh, :⋆₁, hodge)
(var"GenSim-M_dual_d₀", dual_d₀) = default_dec_matrix_generate(mesh, :dual_d₀, hodge)
(var"GenSim-M_⋆₀⁻¹", ⋆₀⁻¹) = default_dec_matrix_generate(mesh, :⋆₀⁻¹, hodge)
♯ = operators(mesh, :♯)
mag = operators(mesh, :mag)
avg₀₁ = operators(mesh, :avg₀₁)
(^) = operators(mesh, :^)
end
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:596 =#
begin
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:477 =#
var"GenSim-M_GenSim-ConMat_1" = var"GenSim-M_⋆₀⁻¹" * var"GenSim-M_dual_d₀" * var"GenSim-M_⋆₁"
var"GenSim-ConMat_1" = (x->var"GenSim-M_GenSim-ConMat_1" * x)
end
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:597 =#
begin
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:231 =#
var"__dynamics_•1" = Decapodes.LazyBufferCache(Returns(nparts(mesh, :E)))
var"__dynamics_•6" = Decapodes.LazyBufferCache(Returns(nparts(mesh, :E)))
__dynamics_sum_1 = Decapodes.LazyBufferCache(Returns(nparts(mesh, :V)))
__dynamics_mult_3 = Decapodes.LazyBufferCache(Returns(nparts(mesh, :E)))
__dynamics_ḣ = Decapodes.LazyBufferCache(Returns(nparts(mesh, :V)))
end
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:598 =#
f(du, u, p, t) = begin
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:598 =#
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:599 =#
begin
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:256 =#
dynamics_h = u.dynamics_h
n = p.n
stress_A = p.stress_A
stress_ρ = p.stress_ρ
stress_g = p.stress_g
var"1" = 1.0
var"2" = 2.0
var"2" = 2.0
end
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:600 =#
var"dynamics_•1" = Decapodes.get_tmp(var"__dynamics_•1", u)
var"dynamics_•6" = Decapodes.get_tmp(var"__dynamics_•6", u)
dynamics_sum_1 = Decapodes.get_tmp(__dynamics_sum_1, u)
dynamics_mult_3 = Decapodes.get_tmp(__dynamics_mult_3, u)
dynamics_ḣ = Decapodes.get_tmp(__dynamics_ḣ, u)
mul!(var"dynamics_•1", var"GenSim-M_d₀", dynamics_h)
mul!(var"dynamics_•6", var"GenSim-M_d₀", dynamics_h)
var"dynamics_•5" = ♯(var"dynamics_•6")
var"dynamics_•4" = mag(var"dynamics_•5")
var"dynamics_•7" = n .- var"1"
var"dynamics_•3" = var"dynamics_•4" ^ var"dynamics_•7"
var"stress_•3" = stress_ρ .* stress_g
var"stress_•2" = var"stress_•3" ^ n
dynamics_sum_1 .= (.+)(n, var"2")
stress_sum_1 = (.+)(n, var"2")
var"dynamics_•2" = avg₀₁(var"dynamics_•3")
var"dynamics_•9" = dynamics_h ^ dynamics_sum_1
var"stress_•1" = var"2" / stress_sum_1
stress_mult_1 = var"stress_•1" .* stress_A
Γ = stress_mult_1 .* var"stress_•2"
var"dynamics_•8" = avg₀₁(var"dynamics_•9")
dynamics_mult_3 .= Γ .* var"dynamics_•1"
dynamics_mult_1 = dynamics_mult_3 .* var"dynamics_•2"
dynamics_mult_2 = dynamics_mult_1 .* var"dynamics_•8"
mul!(dynamics_ḣ, var"GenSim-M_GenSim-ConMat_1", dynamics_mult_2)
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:601 =#
getproperty(du, :dynamics_h) .= dynamics_ḣ
end
end
end |
|
And changing that generated function to quote
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:594 =#
function simulate(mesh, operators, hodge = GeometricHodge())
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:594 =#
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:595 =#
begin
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:185 =#
(var"GenSim-M_d₀", d₀) = default_dec_matrix_generate(mesh, :d₀, hodge)
(var"GenSim-M_⋆₁", ⋆₁) = default_dec_matrix_generate(mesh, :⋆₁, hodge)
(var"GenSim-M_dual_d₀", dual_d₀) = default_dec_matrix_generate(mesh, :dual_d₀, hodge)
(var"GenSim-M_⋆₀⁻¹", ⋆₀⁻¹) = default_dec_matrix_generate(mesh, :⋆₀⁻¹, hodge)
♯ = operators(mesh, :♯)
mag = operators(mesh, :mag)
avg₀₁ = operators(mesh, :avg₀₁)
(^) = operators(mesh, :^)
end
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:596 =#
begin
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:477 =#
var"GenSim-M_GenSim-ConMat_1" = var"GenSim-M_⋆₀⁻¹" * var"GenSim-M_dual_d₀" * var"GenSim-M_⋆₁"
var"GenSim-ConMat_1" = (x->var"GenSim-M_GenSim-ConMat_1" * x)
end
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:597 =#
begin
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:231 =#
var"__dynamics_•1" = Decapodes.LazyBufferCache(Returns(nparts(mesh, :E)))
var"__dynamics_•6" = Decapodes.LazyBufferCache(Returns(nparts(mesh, :E)))
__dynamics_sum_1 = Decapodes.LazyBufferCache(Returns(nparts(mesh, :V)))
__dynamics_mult_3 = Decapodes.LazyBufferCache(Returns(nparts(mesh, :E)))
__dynamics_ḣ = Decapodes.LazyBufferCache(Returns(nparts(mesh, :V)))
end
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:598 =#
f(u, p, t) = begin
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:598 =#
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:599 =#
begin
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:256 =#
dynamics_h = u.dynamics_h
n = p.n
stress_A = p.stress_A
stress_ρ = p.stress_ρ
stress_g = p.stress_g
var"1" = 1.0
var"2" = 2.0
var"2" = 2.0
end
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:600 =#
# var"dynamics_•1" = Decapodes.get_tmp(var"__dynamics_•1", u)
# var"dynamics_•6" = Decapodes.get_tmp(var"__dynamics_•6", u)
# dynamics_sum_1 = Decapodes.get_tmp(__dynamics_sum_1, u)
# dynamics_mult_3 = Decapodes.get_tmp(__dynamics_mult_3, u)
dynamics_ḣ = Decapodes.get_tmp(__dynamics_ḣ, u)
var"dynamics_•1" = var"GenSim-M_d₀" * dynamics_h
var"dynamics_•6" = var"GenSim-M_d₀" * dynamics_h
var"dynamics_•5" = ♯(var"dynamics_•6")
var"dynamics_•4" = mag(var"dynamics_•5")
var"dynamics_•7" = n .- var"1"
var"dynamics_•3" = var"dynamics_•4" ^ var"dynamics_•7"
var"stress_•3" = stress_ρ .* stress_g
var"stress_•2" = var"stress_•3" ^ n
dynamics_sum_1 = (.+)(n, var"2")
stress_sum_1 = (.+)(n, var"2")
var"dynamics_•2" = avg₀₁(var"dynamics_•3")
var"dynamics_•9" = dynamics_h ^ dynamics_sum_1
var"stress_•1" = var"2" / stress_sum_1
stress_mult_1 = var"stress_•1" .* stress_A
Γ = stress_mult_1 .* var"stress_•2"
var"dynamics_•8" = avg₀₁(var"dynamics_•9")
dynamics_mult_3 = Γ .* var"dynamics_•1"
dynamics_mult_1 = dynamics_mult_3 .* var"dynamics_•2"
dynamics_mult_2 = dynamics_mult_1 .* var"dynamics_•8"
dynamics_ḣ = var"GenSim-M_GenSim-ConMat_1" * dynamics_mult_2
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:601 =#
ComponentArray(dynamics_h = dynamics_ḣ)
end
end
endResults in the same errors and warnings, so I expect there is also mutation elsewhere. |
|
Mark all of the setup as non-differentiable |
|
I can't find the documentation of Zygote.@Nograd, if that is what you are referring to. When I apply |
|
No, it should be via ChainRules https://juliadiff.org/ChainRulesCore.jl/stable/rule_author/tips_for_packages.html |
|
Thanks! That makes more sense. Unfortunately, it seems to have no effect. Using this as the result of sim = eval(quote
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:593 =#
function simulate(mesh, operators, hodge = GeometricHodge())
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:593 =#
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:594 =#
var"GenSim-M_d₀", var"GenSim-M_GenSim-ConMat_1", ♯, mag, avg₀₁, ^ = ChainRulesCore.ignore_derivatives() do
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:185 =#
(var"GenSim-M_d₀", d₀) = default_dec_matrix_generate(mesh, :d₀, hodge)
(var"GenSim-M_⋆₁", ⋆₁) = default_dec_matrix_generate(mesh, :⋆₁, hodge)
(var"GenSim-M_dual_d₀", dual_d₀) = default_dec_matrix_generate(mesh, :dual_d₀, hodge)
(var"GenSim-M_⋆₀⁻¹", ⋆₀⁻¹) = default_dec_matrix_generate(mesh, :⋆₀⁻¹, hodge)
♯ = operators(mesh, :♯)
mag = operators(mesh, :mag)
avg₀₁ = operators(mesh, :avg₀₁)
(^) = operators(mesh, :^)
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:595 =#
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:477 =#
var"GenSim-M_GenSim-ConMat_1" = var"GenSim-M_⋆₀⁻¹" * var"GenSim-M_dual_d₀" * var"GenSim-M_⋆₁"
var"GenSim-ConMat_1" = (x->var"GenSim-M_GenSim-ConMat_1" * x)
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:596 =#
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:231 =#
var"__dynamics_•1" = Decapodes.LazyBufferCache(Returns(nparts(mesh, :E)))
var"__dynamics_•6" = Decapodes.LazyBufferCache(Returns(nparts(mesh, :E)))
__dynamics_sum_1 = Decapodes.LazyBufferCache(Returns(nparts(mesh, :V)))
__dynamics_mult_3 = Decapodes.LazyBufferCache(Returns(nparts(mesh, :E)))
__dynamics_ḣ = Decapodes.LazyBufferCache(Returns(nparts(mesh, :V)))
var"GenSim-M_d₀", var"GenSim-M_GenSim-ConMat_1", ♯, mag, avg₀₁, ^
end
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:597 =#
f(u, p, t) = begin
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:597 =#
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:598 =#
begin
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:256 =#
dynamics_h = u.dynamics_h
n = p.n
stress_A = p.stress_A
stress_ρ = p.stress_ρ
stress_g = p.stress_g
var"1" = 1.0
var"2" = 2.0
var"2" = 2.0
end
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:599 =#
# var"dynamics_•1" = Decapodes.get_tmp(var"__dynamics_•1", u)
# var"dynamics_•6" = Decapodes.get_tmp(var"__dynamics_•6", u)
# dynamics_sum_1 = Decapodes.get_tmp(__dynamics_sum_1, u)
# dynamics_mult_3 = Decapodes.get_tmp(__dynamics_mult_3, u)
# dynamics_ḣ = Decapodes.get_tmp(__dynamics_ḣ, u)
# mul!(var"dynamics_•1", var"GenSim-M_d₀", dynamics_h)
var"dynamics_•1" = var"GenSim-M_d₀" * dynamics_h
# mul!(var"dynamics_•6", var"GenSim-M_d₀", dynamics_h)
var"dynamics_•6" = var"GenSim-M_d₀" * dynamics_h
var"dynamics_•5" = ♯(var"dynamics_•6")
var"dynamics_•4" = mag(var"dynamics_•5")
var"dynamics_•7" = n .- var"1"
var"dynamics_•3" = var"dynamics_•4" ^ var"dynamics_•7"
var"stress_•3" = stress_ρ .* stress_g
var"stress_•2" = var"stress_•3" ^ n
# dynamics_sum_1 .= (.+)(n, var"2")
dynamics_sum_1 = (.+)(n, var"2")
stress_sum_1 = (.+)(n, var"2")
var"dynamics_•2" = avg₀₁(var"dynamics_•3")
var"dynamics_•9" = dynamics_h ^ dynamics_sum_1
var"stress_•1" = var"2" / stress_sum_1
stress_mult_1 = var"stress_•1" .* stress_A
Γ = stress_mult_1 .* var"stress_•2"
var"dynamics_•8" = avg₀₁(var"dynamics_•9")
# dynamics_mult_3 .= Γ .* var"dynamics_•1"
dynamics_mult_3 = Γ .* var"dynamics_•1"
dynamics_mult_1 = dynamics_mult_3 .* var"dynamics_•2"
dynamics_mult_2 = dynamics_mult_1 .* var"dynamics_•8"
# mul!(dynamics_ḣ, var"GenSim-M_GenSim-ConMat_1", dynamics_mult_2)
dynamics_ḣ = var"GenSim-M_GenSim-ConMat_1" * dynamics_mult_2
#= /home/x/.julia/dev/Decapodes/src/simulation.jl:600 =#
# getproperty(du, :dynamics_h) .= dynamics_ḣ
ComponentArray(dynamics_h=dynamics_ḣ)
end
end
simulate
end)Notably including "Warning: Reverse-Mode AD VJP choices all failed. Falling back to numerical VJPs" |
This was referenced Feb 16, 2024
|
To work around the mutation issues, we're going to get enough Enzyme rules to Enzyme everywhere. The ODE solver mostly has this done, and we're working on doing a bunch of unit tests SciML/SciMLSensitivity.jl#905 and fixing what we find there before doing it on this example. I assume we can get something of that merging either tonight or tomorrow, then try changing this to Enzyme. |
|
We have a gradient! cd(@__DIR__)
using Pkg
Pkg.activate(".")
Pkg.instantiate()
# AlgebraicJulia Dependencies
using Catlab
using Catlab.Graphics
using CombinatorialSpaces
using Decapodes
using ComponentArrays
# External Dependencies
using MLStyle
using MultiScaleArrays
using LinearAlgebra
using OrdinaryDiffEq
using JLD2
using SparseArrays
using Statistics
using GLMakie # Just for visualization
using GeometryBasics: Point2, Point3
Point2D = Point2{Float64};
Point3D = Point3{Float64};
using DiagrammaticEquations
using DiagrammaticEquations.Deca
@info("Packages Loaded")
halfar_eq2 = @decapode begin
h::Form0
Γ::Form1
n::Constant
ḣ == ∂ₜ(h)
ḣ == ∘(⋆, d, ⋆)(Γ * d(h) * avg₀₁(mag(♯(d(h)))^(n-1)) * avg₀₁(h^(n+2)))
end
glens_law = @decapode begin
Γ::Form1
(A,ρ,g,n)::Constant
Γ == (2/(n+2))*A*(ρ*g)^n
end
@info("Decapodes Defined")
ice_dynamics_composition_diagram = @relation () begin
dynamics(Γ,n)
stress(Γ,n)
end
ice_dynamics_cospan = oapply(ice_dynamics_composition_diagram,
[Open(halfar_eq2, [:Γ,:n]),
Open(glens_law, [:Γ,:n])])
ice_dynamics = apex(ice_dynamics_cospan)
ice_dynamics1D = expand_operators(ice_dynamics)
infer_types!(ice_dynamics1D, op1_inf_rules_1D, op2_inf_rules_1D)
resolve_overloads!(ice_dynamics1D, op1_res_rules_1D, op2_res_rules_1D)
s_prime = EmbeddedDeltaSet1D{Bool, Point2D}()
add_vertices!(s_prime, 100, point=Point2D.(range(-2, 2, length=100), 0))
add_edges!(s_prime, 1:nv(s_prime)-1, 2:nv(s_prime))
orient!(s_prime)
s = EmbeddedDeltaDualComplex1D{Bool, Float64, Point2D}(s_prime)
subdivide_duals!(s, Circumcenter())
@info("Spaces Defined")
function generate(sd, my_symbol; hodge=GeometricHodge())
e_vecs = map(edges(sd)) do e
point(sd, sd[e, :∂v0]) - point(sd, sd[e, :∂v1])
end
neighbors = map(vertices(sd)) do v
union(incident(sd, v, :∂v0), incident(sd, v, :∂v1))
end
n_vecs = map(neighbors) do es
[e_vecs[e] for e in es]
end
I = Vector{Int64}()
J = Vector{Int64}()
V = Vector{Float64}()
for e in 1:ne(s)
append!(J, [s[e,:∂v0],s[e,:∂v1]])
append!(I, [e,e])
append!(V, [0.5, 0.5])
end
avg_mat = sparse(I,J,V)
op = @match my_symbol begin
:♯ => x -> begin
map(neighbors, n_vecs) do es, nvs
sum([nv*norm(nv)*x[e] for (e,nv) in zip(es,nvs)]) / sum(norm.(nvs))
end
end
:mag => x -> begin
norm.(x)
end
:avg₀₁ => x -> begin
avg_mat * x
end
:^ => (x,y) -> max.(x, 0) .^ y # x is sometimes a very small negative number due to numerical error
:* => (x,y) -> x .* y
:show => x -> begin
@show x
x
end
x => error("Unmatched operator $my_symbol")
end
return (args...) -> op(args...)
end
sim = eval(gensim(ice_dynamics1D, dimension=1))
fₘ = sim(s, generate)
function f(constants_and_parameters)
prob = ODEProblem{true, SciMLBase.FullSpecialize}(fₘ, u₀, (0, tₑ), constants_and_parameters)
@info("Solving")
soln = solve(prob, Tsit5())
@info("Done")
# return soln(tₑ)
sum(last(soln)) # last, not soln(tₑ) because to avoid interpolation fails when AD fails.
end
h₀ = map(x -> try sqrt(1. - x[1]^2) catch DomainError return 0.0 end, point(s_prime))
flow_rate, ice_density, u_init_arr = 1e-16, 910., h₀
n = 3
ρ = ice_density
g = 9.8101
A = fill(flow_rate, ne(s))
tₑ = 5e13
u₀ = ComponentArray(dynamics_h = u_init_arr)
# Note that this must be a ComponentArray to differentiate
constants_and_parameters = ComponentArray(
n = n,
stress_ρ = ρ,
stress_g = g,
stress_A = A)
y = f(constants_and_parameters)
# Must use Zygote—ForwardDiff is asymtotically slow
using SciMLSensitivity
using Zygote
Zygote.gradient(f, constants_and_parameters)Still more to do, it's using some fallbacks, but this is at least a starting point. |
|
Hand modified decapode: decapode_f = begin
#= /Users/chrisrackauckas/.julia/packages/Decapodes/GugTA/src/simulation.jl:531 =#
function simulate(mesh, operators, hodge = GeometricHodge())
#= /Users/chrisrackauckas/.julia/packages/Decapodes/GugTA/src/simulation.jl:531 =#
#= /Users/chrisrackauckas/.julia/packages/Decapodes/GugTA/src/simulation.jl:532 =#
begin
#= /Users/chrisrackauckas/.julia/packages/Decapodes/GugTA/src/simulation.jl:157 =#
(var"GenSim-M_d₀", d₀) = default_dec_matrix_generate(mesh, :d₀, hodge)
(var"GenSim-M_⋆₁", ⋆₁) = default_dec_matrix_generate(mesh, :⋆₁, hodge)
(var"GenSim-M_dual_d₀", dual_d₀) = default_dec_matrix_generate(mesh, :dual_d₀, hodge)
(var"GenSim-M_⋆₀⁻¹", ⋆₀⁻¹) = default_dec_matrix_generate(mesh, :⋆₀⁻¹, hodge)
♯ = operators(mesh, :♯)
mag = operators(mesh, :mag)
avg₀₁ = operators(mesh, :avg₀₁)
(^) = operators(mesh, :^)
end
#= /Users/chrisrackauckas/.julia/packages/Decapodes/GugTA/src/simulation.jl:533 =#
begin
#= /Users/chrisrackauckas/.julia/packages/Decapodes/GugTA/src/simulation.jl:449 =#
var"GenSim-M_GenSim-ConMat_1" = var"GenSim-M_⋆₀⁻¹" * var"GenSim-M_dual_d₀" * var"GenSim-M_⋆₁"
var"GenSim-ConMat_1" = (x->var"GenSim-M_GenSim-ConMat_1" * x)
end
#= /Users/chrisrackauckas/.julia/packages/Decapodes/GugTA/src/simulation.jl:534 =#
begin
#= /Users/chrisrackauckas/.julia/packages/Decapodes/GugTA/src/simulation.jl:203 =#
var"__dynamics_•1" = Decapodes.LazyBufferCache(x->nparts(mesh, :E))
var"__dynamics_•6" = Decapodes.LazyBufferCache(x->nparts(mesh, :E))
__dynamics_sum_1 = Decapodes.LazyBufferCache(x->nparts(mesh, :V))
__dynamics_mult_3 = Decapodes.LazyBufferCache(x->nparts(mesh, :E))
__dynamics_ḣ = Decapodes.LazyBufferCache(x->nparts(mesh, :V))
end
#= /Users/chrisrackauckas/.julia/packages/Decapodes/GugTA/src/simulation.jl:535 =#
f(du, u, p, t) = begin
#= /Users/chrisrackauckas/.julia/packages/Decapodes/GugTA/src/simulation.jl:535 =#
#= /Users/chrisrackauckas/.julia/packages/Decapodes/GugTA/src/simulation.jl:536 =#
begin
#= /Users/chrisrackauckas/.julia/packages/Decapodes/GugTA/src/simulation.jl:228 =#
dynamics_h = u.dynamics_h
n = p.n
stress_A = p.stress_A
stress_ρ = p.stress_ρ
stress_g = p.stress_g
var"1" = 1.0
var"2" = 2.0
var"2" = 2.0
end
#= /Users/chrisrackauckas/.julia/packages/Decapodes/GugTA/src/simulation.jl:537 =#
var"dynamics_•1" = Decapodes.get_tmp(var"__dynamics_•1", u)
var"dynamics_•6" = Decapodes.get_tmp(var"__dynamics_•6", u)
dynamics_sum_1 = Decapodes.get_tmp(__dynamics_sum_1, u)
dynamics_mult_3 = Decapodes.get_tmp(__dynamics_mult_3, u)
dynamics_ḣ = Decapodes.get_tmp(__dynamics_ḣ, u)
mul!(var"dynamics_•1", var"GenSim-M_d₀", dynamics_h)
mul!(var"dynamics_•6", var"GenSim-M_d₀", dynamics_h)
var"dynamics_•5" = ♯(var"dynamics_•6")
var"dynamics_•4" = mag(var"dynamics_•5")
var"dynamics_•7" = n .- var"1"
var"dynamics_•3" = var"dynamics_•4" ^ var"dynamics_•7"
var"stress_•3" = stress_ρ .* stress_g
var"stress_•2" = var"stress_•3" ^ n
dynamics_sum_1 .= (.+)(n, var"2")
stress_sum_1 = (.+)(n, var"2")
var"dynamics_•2" = avg₀₁(var"dynamics_•3")
@show typeof(dynamics_h)
@show typeof(dynamics_sum_1)
var"dynamics_•9" = dynamics_h ^ dynamics_sum_1
var"stress_•1" = var"2" / stress_sum_1
stress_mult_1 = var"stress_•1" .* stress_A
Γ = stress_mult_1 .* var"stress_•2"
var"dynamics_•8" = avg₀₁(var"dynamics_•9")
dynamics_mult_3 .= Γ .* var"dynamics_•1"
dynamics_mult_1 = dynamics_mult_3 .* var"dynamics_•2"
dynamics_mult_2 = dynamics_mult_1 .* var"dynamics_•8"
mul!(dynamics_ḣ, var"GenSim-M_GenSim-ConMat_1", dynamics_mult_2)
@show size(du)
@show size(dynamics_ḣ)
#= /Users/chrisrackauckas/.julia/packages/Decapodes/GugTA/src/simulation.jl:538 =#
du .= dynamics_ḣ
end
end
endThis is starting to do stuff but I think the AD there is doing something odd, so I'm debugging the lazy buffer cache handling. |
|
Hi @ChrisRackauckas @LilithHafner , What is the current status of this issue? I tried running the julia> include("test.jl")
Activating project at `~/Projects/Proposals/ASKEM/DecapodesDiff`
[ Info: Packages Loaded
[ Info: Decapodes Defined
[ Info: Spaces Defined
ERROR: LoadError: UndefVarError: `sim` not defined
Stacktrace:
[1] top-level scope
@ ~/Projects/Proposals/ASKEM/DecapodesDiff/test.jl:117
[2] include(fname::String)
@ Base.MainInclude ./client.jl:489
[3] top-level scope
@ REPL[13]:1
in expression starting at /Users/zuck016/Projects/Proposals/ASKEM/DecapodesDiff/test.jl:117Presumably, this is due to the fact that the call to fₘ = sim(s, generate) |
|
I've handed this off to @ChrisRackauckas. I'm not sure what the current status is. |
|
Yeah oops I accidentally included some random changes in there. This one should just run. |
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<@U03T6HR1328> <@U03JKH9FNBE> <@U03TU2CFAQY> am now running into:
ERROR: ArgumentError: cannot reinterpret an `ForwardDiff.Dual{nothing, Float64, 11}` array to `ForwardDiff.Dual{ForwardDiff.Tag{var"#39#40", Float64}, Float64, 12}` whose first dimension has size `99`. The resulting array would have non-integral first dimension.More details in thread. Am still investigating.
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