Comments (7)
ngphuoc
commented on May 18, 2024
Here is another error, maybe related to mean/abs2/broadcast
using AutoGrad, Knet
m = [rand(1,3), rand(1)]
x,y = rand(3,4),rand(1,4)
pred(m,x) = m[1]*x .+ m[2]
loss(m,x,y) = mean(abs2, pred(m,x) .- y)
∇ = grad(loss)
∇(m,x,y) # OK
loss(m,x,y) = mean(abs2, pred(m,x) .- y) + mean(mean.(abs2, m))
∇ = grad(loss)
∇(m,x,y) # Error
WARNING: abs2(x::AbstractArray{T}) where T <: Number is deprecated, use abs2.(x) instead.
Stacktrace:
[1] depwarn(::String, ::Symbol) at ./deprecated.jl:70
[2] abs2(::Array{Float64,2}) at ./deprecated.jl:57
[3] (::AutoGrad.##rfun#7#10{Base.#abs2})(::Array{Any,1}, ::Function, ::AutoGrad.Rec{Array{Float64,2}}, ::Vararg{AutoGrad.Rec{Array{Float64,2}},N} where N) at /home/ngphuoc/.julia/v0.6/AutoGrad/src/core.jl:124
[4] abs2(::AutoGrad.Rec{Array{Float64,2}}) at ./<missing>:0
[5] mean(::Base.#abs2, ::AutoGrad.Rec{Array{Array{Float64,N} where N,1}}) at ./statistics.jl:25
[6] loss(::AutoGrad.Rec{Array{Array{Float64,N} where N,1}}, ::Array{Float64,2}, ::Array{Float64,2}) at ./REPL[8]:1
[7] forward_pass(::Function, ::Tuple{Array{Array{Float64,N} where N,1},Array{Float64,2},Array{Float64,2}}, ::Array{Any,1}, ::Int64) at /home/ngphuoc/.julia/v0.6/AutoGrad/src/core.jl:88
[8] (::AutoGrad.##gradfun#1#3{#loss,Int64})(::Array{Any,1}, ::Function, ::Array{Array{Float64,N} where N,1}, ::Vararg{Any,N} where N) at /home/ngphuoc/.julia/v0.6/AutoGrad/src/core.jl:39
[9] (::AutoGrad.#gradfun#2)(::Array{Array{Float64,N} where N,1}, ::Vararg{Any,N} where N) at /home/ngphuoc/.julia/v0.6/AutoGrad/src/core.jl:39
[10] eval(::Module, ::Any) at ./boot.jl:235
[11] eval_user_input(::Any, ::Base.REPL.REPLBackend) at ./REPL.jl:66
[12] macro expansion at ./REPL.jl:97 [inlined]
[13] (::Base.REPL.##1#2{Base.REPL.REPLBackend})() at ./event.jl:73
while loading no file, in expression starting on line 0
ERROR: DimensionMismatch("dimensions must match")
Stacktrace:
[1] promote_shape(::Tuple{Base.OneTo{Int64},Base.OneTo{Int64}}, ::Tuple{Base.OneTo{Int64}}) at ./indices.jl:84
[2] +(::Array{Float64,2}, ::Array{Float64,1}) at ./arraymath.jl:38
[3] (::AutoGrad.##rfun#7#10{Base.#+})(::Array{Any,1}, ::Function, ::AutoGrad.Rec{Array{Float64,2}}, ::Vararg{Any,N} where N) at /home/ngphuoc/.julia/v0.6/AutoGrad/src/core.jl:124
[4] +(::AutoGrad.Rec{Array{Float64,2}}, ::AutoGrad.Rec{Array{Float64,1}}) at ./<missing>:0
[5] mean(::Base.#abs2, ::AutoGrad.Rec{Array{Array{Float64,N} where N,1}}) at ./statistics.jl:29
[6] loss(::AutoGrad.Rec{Array{Array{Float64,N} where N,1}}, ::Array{Float64,2}, ::Array{Float64,2}) at ./REPL[8]:1
[7] forward_pass(::Function, ::Tuple{Array{Array{Float64,N} where N,1},Array{Float64,2},Array{Float64,2}}, ::Array{Any,1}, ::Int64) at /home/ngphuoc/.julia/v0.6/AutoGrad/src/core.jl:88
[8] (::AutoGrad.##gradfun#1#3{#loss,Int64})(::Array{Any,1}, ::Function, ::Array{Array{Float64,N} where N,1}, ::Vararg{Any,N} where N) at /home/ngphuoc/.julia/v0.6/AutoGrad/src/core.jl:39
[9] (::AutoGrad.#gradfun#2)(::Array{Array{Float64,N} where N,1}, ::Vararg{Any,N} where N) at /home/ngphuoc/.julia/v0.6/AutoGrad/src/core.jl:39
loss(m,x,y) = mean(abs2, pred(m,x) .- y) + mean(sum.(abs2.(m)))
∇ = grad(loss)
∇(m,x,y) # Error
ERROR: MethodError: no method matching start(::AutoGrad.Broadcasted{AutoGrad.Rec{Array{Array{Float64,N} where N,1}}})
Closest candidates are:
start(::SimpleVector) at essentials.jl:258
start(::Base.MethodList) at reflection.jl:560
start(::ExponentialBackOff) at error.jl:107
...
Stacktrace:
[1] mapfoldl(::Base.#identity, ::Function, ::AutoGrad.Broadcasted{AutoGrad.Rec{Array{Array{Float64,N} where N,1}}}) at ./reduce.jl:67
[2] (::##1#2)(::AutoGrad.Broadcasted{AutoGrad.Rec{Array{Array{Float64,N} where N,1}}}) at ./<missing>:0
[3] broadcast(::Function, ::AutoGrad.Rec{Array{Array{Float64,N} where N,1}}) at /home/ngphuoc/.julia/v0.6/AutoGrad/src/unfuse.jl:35
[4] loss(::AutoGrad.Rec{Array{Array{Float64,N} where N,1}}, ::Array{Float64,2}, ::Array{Float64,2}) at ./REPL[16]:1
[5] forward_pass(::Function, ::Tuple{Array{Array{Float64,N} where N,1},Array{Float64,2},Array{Float64,2}}, ::Array{Any,1}, ::Int64) at /home/ngphuoc/.julia/v0.6/AutoGrad/src/core.jl:88
[6] (::AutoGrad.##gradfun#1#3{#loss,Int64})(::Array{Any,1}, ::Function, ::Array{Array{Float64,N} where N,1}, ::Vararg{Any,N} where N) at /home/ngphuoc/.julia/v0.6/AutoGrad/src/core.jl:39
[7] (::AutoGrad.#gradfun#2)(::Array{Array{Float64,N} where N,1}, ::Vararg{Any,N} where N) at /home/ngphuoc/.julia/v0.6/AutoGrad/src/core.jl:39
from autograd.jl.
CarloLucibello
commented on May 18, 2024
The last 2 examples work for me on master in this form
loss(m,x,y) = mean(abs2, pred(m,x) .- y) + mean(sum.(abs2, m))
loss(m,x,y) = mean(abs2, pred(m,x) .- y) + mean(mean.(abs2, m))from autograd.jl.
CarloLucibello
commented on May 18, 2024
On julia 0.7 and on the branch of PR #78 the first example now gives the following error:
julia> using AutoGrad, Knet, Statistics
julia> u = [rand(2,3), rand(2)]
2-element Array{Array{Float64,N} where N,1}:
[0.920865 0.820558 0.804323; 0.873658 0.993891 0.010203]
[0.362208, 0.69425]
julia> v = [rand(1,2), rand(1)]
2-element Array{Array{Float64,N} where N,1}:
[0.454112 0.564783]
[0.274256]
julia> m = Dict(:u=>u, :v=>v)
Dict{Symbol,Array{Array{Float64,N} where N,1}} with 2 entries:
:v => Array{Float64,N} where N[[0.454112 0.564783], [0.274256]]
:u => Array{Float64,N} where N[[0.920865 0.820558 0.804323; 0.873658 0.993891 0.010203], [0.362208, 0.69425]]
julia> x,y = rand(3,4),rand(1,4)
([0.916855 0.51435 0.784969 0.451743; 0.36581 0.904977 0.912647 0.385057; 0.828762 0.730146 0.321203 0.251844], [0.383026 0.280737 0.599814 0.245899])
julia> pred(m,x) = foldl((x,w)->w[1]*x .+ w[2], [m[:u],m[:v]], init=x)
pred (generic function with 1 method)
julia> l2(ws) = mean(mean.(abs2, ws))
l2 (generic function with 1 method)
julia> loss(m,x,y) = mean(abs2, pred(m,x) .- y) + mean(l2.(collect(values(m))))
loss (generic function with 1 method)
julia> loss(m,x,y)
3.84326612440536
julia> grad(loss)(m,x,y)
ERROR: MethodError: no method matching sum_outgrads(::Array{Array{Float64,N} where N,1}, ::Array{Any,1})
Closest candidates are:
sum_outgrads(::Nothing, ::Any) at /home/carlo/.julia/dev/AutoGrad/src/core.jl:491
sum_outgrads(::AbstractArray{T,N} where N, ::AbstractArray{T,N} where N) where T at /home/carlo/.julia/dev/AutoGrad/src/core.jl:478
sum_outgrads(::Rec, ::Any) at /home/carlo/.julia/dev/AutoGrad/src/core.jl:482
...
Stacktrace:
[1] sum_outgrads(::Dict{Symbol,Array{Array{Float64,N} where N,1}}, ::AutoGrad.UngetIndex) at /home/carlo/.julia/dev/AutoGrad/src/getindex.jl:92
[2] backward_pass(::Rec{Dict{Symbol,Array{Array{Float64,N} where N,1}}}, ::Rec{Float64}, ::Array{AutoGrad.Node,1}) at /home/carlo/.julia/dev/AutoGrad/src/core.jl:246
[3] (::getfield(AutoGrad, Symbol("##gradfun#1#2")){typeof(loss),Int64})(::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}, ::Function, ::Dict{Symbol,Array{Array{Float64,N} where N,1}}, ::Vararg{Any,N} where N) at /home/carlo/.julia/dev/AutoGrad/src/core.jl:40
[4] (::getfield(AutoGrad, Symbol("#gradfun#3")){getfield(AutoGrad, Symbol("##gradfun#1#2")){typeof(loss),Int64}})(::Dict{Symbol,Array{Array{Float64,N} where N,1}}, ::Vararg{Any,N} where N) at /home/carlo/.julia/dev/AutoGrad/src/core.jl:39
[5] top-level scope at none:0The gradient of each of the two terms of the loss
loss(m,x,y) = mean(abs2, pred(m,x) .- y)
loss(m,x,y) = mean(l2.(collect(values(m))))is computed correctly, the problem arises when they are summed.
from autograd.jl.
CarloLucibello
commented on May 18, 2024
Relaxing the signature
sum_outgrads(a::AbstractArray{T},b::AbstractArray{T}) where T = ...to
sum_outgrads(a::AbstractArray{T},b::AbstractArray) where T = ...solves the problem
julia> loss(m,x,y) = mean(abs2, pred(m,x) .- y) + mean(l2.(collect(values(m))))
loss (generic function with 1 method)
julia> grad(loss)(m,x,y)
Dict{Symbol,Array{Array{Float64,N} where N,1}} with 2 entries:
:v => Array{Float64,N} where N[[6.68047 6.28584], [5.20279]]
:u => Array{Float64,N} where N[[2.23497 2.9622 1.94912; 1.88972 2.05421 1.44816], [4.49943, 3.18227]]but this is a bad hack. We have to understand why inference is failing and we have an Array{Any} in this error
ERROR: MethodError: no method matching sum_outgrads(::Array{Array{Float64,N} where N,1}, ::Array{Any,1})from autograd.jl.
denizyuret
commented on May 18, 2024
Fixed in latest master, please test and close.
from autograd.jl.
LiWang1
commented on May 18, 2024
I have another problem, here is the code:
using DifferentialEquations
#using__ ForwardDiff
#using DiffEqDiffTools
using AutoGrad # for BAD
A = [1.0 0 0 -5
4 -2 4 -3
-4 0 0 1
5 2 -2 4]
u0 = rand(4, 1)
tspan = (0.0, 1.0)
f(u, p, t) = p*u
prob = ODEProblem(f, u0, tspan, A)
sol = solve(prob)
x_obs = range(0, stop=1,length = 2000)
y_obs = [sol(x_obs[i]) + 0.01randn(4) for i in 1:length(x_obs)]
function solver(para)
problem = ODEProblem(f, u0, tspan, para)
_problem = remake(problem;u0=convert.(eltype(para),problem.u0),p=para)
solution = solve(_problem, Tsit5())
return (solution)
end
function loss(para)
solution = solver(para)
L = 0.0
for i in 1:length(x_obs)
L += sum((solution(x_obs[i]) .- y_obs[i]).^2)
end
return (L)
end
grad = AutoGrad.grad(loss)(A)
And the problem is :
Stacktrace:
[1] setproperty!(::OrdinaryDiffEq.ODEIntegrator{Tsit5,false,Array{Float64,2},Float64,Param{Array{Float64,2}},Float64,Float64,Float64,Array{Array{Float64,2},1},ODESolution{Float64,3,Array{Array{Float64,2},1},Nothing,Nothing,Array{Float64,1},Array{Array{Array{Float64,2},1},1},ODEProblem{Array{Float64,2},Tuple{Float64,Float64},false,Param{Array{Float64,2}},ODEFunction{false,typeof(f),LinearAlgebra.UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}},DiffEqBase.StandardODEProblem},Tsit5,OrdinaryDiffEq.InterpolationData{ODEFunction{false,typeof(f),LinearAlgebra.UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},Array{Array{Float64,2},1},Array{Float64,1},Array{Array{Array{Float64,2},1},1},OrdinaryDiffEq.Tsit5ConstantCache{Float64,Float64}},DiffEqBase.DEStats},ODEFunction{false,typeof(f),LinearAlgebra.UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},OrdinaryDiffEq.Tsit5ConstantCache{Float64,Float64},OrdinaryDiffEq.DEOptions{Float64,Float64,Float64,Float64,typeof(DiffEqBase.ODE_DEFAULT_NORM),typeof(LinearAlgebra.opnorm),CallbackSet{Tuple{},Tuple{}},typeof(DiffEqBase.ODE_DEFAULT_ISOUTOFDOMAIN),typeof(DiffEqBase.ODE_DEFAULT_PROG_MESSAGE),typeof(DiffEqBase.ODE_DEFAULT_UNSTABLE_CHECK),DataStructures.BinaryHeap{Float64,DataStructures.LessThan},DataStructures.BinaryHeap{Float64,DataStructures.LessThan},Nothing,Nothing,Int64,Array{Float64,1},Array{Float64,1},Array{Float64,1}},Array{Float64,2},Float64,Nothing}, ::Symbol, ::AutoGrad.Result{Array{Float64,2}}) at ./Base.jl:21
[2] initialize!(::OrdinaryDiffEq.ODEIntegrator{Tsit5,false,Array{Float64,2},Float64,Param{Array{Float64,2}},Float64,Float64,Float64,Array{Array{Float64,2},1},ODESolution{Float64,3,Array{Array{Float64,2},1},Nothing,Nothing,Array{Float64,1},Array{Array{Array{Float64,2},1},1},ODEProblem{Array{Float64,2},Tuple{Float64,Float64},false,Param{Array{Float64,2}},ODEFunction{false,typeof(f),LinearAlgebra.UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}},DiffEqBase.StandardODEProblem},Tsit5,OrdinaryDiffEq.InterpolationData{ODEFunction{false,typeof(f),LinearAlgebra.UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},Array{Array{Float64,2},1},Array{Float64,1},Array{Array{Array{Float64,2},1},1},OrdinaryDiffEq.Tsit5ConstantCache{Float64,Float64}},DiffEqBase.DEStats},ODEFunction{false,typeof(f),LinearAlgebra.UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},OrdinaryDiffEq.Tsit5ConstantCache{Float64,Float64},OrdinaryDiffEq.DEOptions{Float64,Float64,Float64,Float64,typeof(DiffEqBase.ODE_DEFAULT_NORM),typeof(LinearAlgebra.opnorm),CallbackSet{Tuple{},Tuple{}},typeof(DiffEqBase.ODE_DEFAULT_ISOUTOFDOMAIN),typeof(DiffEqBase.ODE_DEFAULT_PROG_MESSAGE),typeof(DiffEqBase.ODE_DEFAULT_UNSTABLE_CHECK),DataStructures.BinaryHeap{Float64,DataStructures.LessThan},DataStructures.BinaryHeap{Float64,DataStructures.LessThan},Nothing,Nothing,Int64,Array{Float64,1},Array{Float64,1},Array{Float64,1}},Array{Float64,2},Float64,Nothing}, ::OrdinaryDiffEq.Tsit5ConstantCache{Float64,Float64}) at /Users/wangli/.julia/packages/OrdinaryDiffEq/xJCph/src/perform_step/low_order_rk_perform_step.jl:565
[3] #__init#335(::Array{Float64,1}, ::Array{Float64,1}, ::Array{Float64,1}, ::Nothing, ::Bool, ::Bool, ::Bool, ::Bool, ::Nothing, ::Bool, ::Bool, ::Float64, ::Float64, ::Float64, ::Bool, ::Bool, ::Rational{Int64}, ::Nothing, ::Nothing, ::Rational{Int64}, ::Int64, ::Int64, ::Int64, ::Rational{Int64}, ::Bool, ::Int64, ::Nothing, ::Nothing, ::Int64, ::typeof(DiffEqBase.ODE_DEFAULT_NORM), ::typeof(LinearAlgebra.opnorm), ::typeof(DiffEqBase.ODE_DEFAULT_ISOUTOFDOMAIN), ::typeof(DiffEqBase.ODE_DEFAULT_UNSTABLE_CHECK), ::Bool, ::Bool, ::Bool, ::Bool, ::Bool, ::Bool, ::Bool, ::Int64, ::String, ::typeof(DiffEqBase.ODE_DEFAULT_PROG_MESSAGE), ::Nothing, ::Bool, ::Bool, ::Bool, ::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}, ::typeof(DiffEqBase.__init), ::ODEProblem{Array{Float64,2},Tuple{Float64,Float64},false,Param{Array{Float64,2}},ODEFunction{false,typeof(f),LinearAlgebra.UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}},DiffEqBase.StandardODEProblem}, ::Tsit5, ::Array{Array{Float64,2},1}, ::Array{Float64,1}, ::Array{Any,1}, ::Type{Val{true}}) at /Users/wangli/.julia/packages/OrdinaryDiffEq/xJCph/src/solve.jl:356
[4] __init(::ODEProblem{Array{Float64,2},Tuple{Float64,Float64},false,Param{Array{Float64,2}},ODEFunction{false,typeof(f),LinearAlgebra.UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}},DiffEqBase.StandardODEProblem}, ::Tsit5, ::Array{Array{Float64,2},1}, ::Array{Float64,1}, ::Array{Any,1}, ::Type{Val{true}}) at /Users/wangli/.julia/packages/OrdinaryDiffEq/xJCph/src/solve.jl:66 (repeats 4 times)
[5] #__solve#334 at /Users/wangli/.julia/packages/OrdinaryDiffEq/xJCph/src/solve.jl:4 [inlined]
[6] __solve at /Users/wangli/.julia/packages/OrdinaryDiffEq/xJCph/src/solve.jl:4 [inlined]
[7] #solve_call#433(::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}, ::typeof(DiffEqBase.solve_call), ::ODEProblem{Array{Float64,2},Tuple{Float64,Float64},false,Param{Array{Float64,2}},ODEFunction{false,typeof(f),LinearAlgebra.UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}},DiffEqBase.StandardODEProblem}, ::Tsit5) at /Users/wangli/.julia/packages/DiffEqBase/4V8I6/src/solve.jl:40
[8] solve_call at /Users/wangli/.julia/packages/DiffEqBase/4V8I6/src/solve.jl:37 [inlined]
[9] #solve#434 at /Users/wangli/.julia/packages/DiffEqBase/4V8I6/src/solve.jl:57 [inlined]
[10] solve(::ODEProblem{Array{Float64,2},Tuple{Float64,Float64},false,Param{Array{Float64,2}},ODEFunction{false,typeof(f),LinearAlgebra.UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}},DiffEqBase.StandardODEProblem}, ::Tsit5) at /Users/wangli/.julia/packages/DiffEqBase/4V8I6/src/solve.jl:45
[11] solver(::Param{Array{Float64,2}}) at /Users/wangli/masterthesis/models /scalable_model.jl:36
[12] loss(::Param{Array{Float64,2}}) at /Users/wangli/masterthesis/models /scalable_model.jl:42
[13] #differentiate#3(::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}, ::typeof(AutoGrad.differentiate), ::Function, ::Param{Array{Float64,2}}) at /Users/wangli/.julia/packages/AutoGrad/pTNVv/src/core.jl:144
[14] differentiate(::Function, ::Param{Array{Float64,2}}) at /Users/wangli/.julia/packages/AutoGrad/pTNVv/src/core.jl:135
[15] (::getfield(AutoGrad, Symbol("##gradfun#6#8")){typeof(loss),Int64,Bool})(::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}, ::getfield(AutoGrad, Symbol("#gradfun#7")){getfield(AutoGrad, Symbol("##gradfun#6#8")){typeof(loss),Int64,Bool}}, ::Array{Float64,2}) at /Users/wangli/.julia/packages/AutoGrad/pTNVv/src/core.jl:225
[16] (::getfield(AutoGrad, Symbol("#gradfun#7")){getfield(AutoGrad, Symbol("##gradfun#6#8")){typeof(loss),Int64,Bool}})(::Array{Float64,2}) at /Users/wangli/.julia/packages/AutoGrad/pTNVv/src/core.jl:221
[17] top-level scope at REPL[52]:1
[18] eval(::Module, ::Any) at ./boot.jl:330
[19] eval_user_input(::Any, ::REPL.REPLBackend) at /Users/sabae/buildbot/worker/package_macos64/build/usr/share/julia/stdlib/v1.2/REPL/src/REPL.jl:86
[20] macro expansion at /Users/sabae/buildbot/worker/package_macos64/build/usr/share/julia/stdlib/v1.2/REPL/src/REPL.jl:118 [inlined]
[21] (::getfield(REPL, Symbol("##26#27")){REPL.REPLBackend})() at ./task.jl:268
ERROR: MethodError: Cannot `convert` an object of type AutoGrad.Result{Array{Float64,2}} to an object of type Array{Float64,2}
Closest candidates are:
convert(::Type{Array{T,N}}, ::StaticArrays.SizedArray{S,T,N,M} where M) where {T, S, N} at /Users/wangli/.julia/packages/StaticArrays/DBECI/src/SizedArray.jl:62
convert(::Type{Array{T,N}}, ::FillArrays.Zeros{V,N,Axes} where Axes) where {T, V, N} at /Users/wangli/.julia/packages/FillArrays/LYCik/src/FillArrays.jl:320
convert(::Type{Array{T,N}}, ::FillArrays.Ones{V,N,Axes} where Axes) where {T, V, N} at /Users/wangli/.julia/packages/FillArrays/LYCik/src/FillArrays.jl:320
...
Stacktrace:
[1] #differentiate#3(::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}, ::typeof(AutoGrad.differentiate), ::Function, ::Param{Array{Float64,2}}) at /Users/wangli/.julia/packages/AutoGrad/pTNVv/src/core.jl:148
[2] differentiate(::Function, ::Param{Array{Float64,2}}) at /Users/wangli/.julia/packages/AutoGrad/pTNVv/src/core.jl:135
[3] (::getfield(AutoGrad, Symbol("##gradfun#6#8")){typeof(loss),Int64,Bool})(::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}, ::getfield(AutoGrad, Symbol("#gradfun#7")){getfield(AutoGrad, Symbol("##gradfun#6#8")){typeof(loss),Int64,Bool}}, ::Array{Float64,2}) at /Users/wangli/.julia/packages/AutoGrad/pTNVv/src/core.jl:225
[4] (::getfield(AutoGrad, Symbol("#gradfun#7")){getfield(AutoGrad, Symbol("##gradfun#6#8")){typeof(loss),Int64,Bool}})(::Array{Float64,2}) at /Users/wangli/.julia/packages/AutoGrad/pTNVv/src/core.jl:221
[5] top-level scope at REPL[52]:1
caused by [exception 1]
MethodError: Cannot `convert` an object of type AutoGrad.Result{Array{Float64,2}} to an object of type Array{Float64,2}
Closest candidates are:
convert(::Type{Array{T,N}}, ::StaticArrays.SizedArray{S,T,N,M} where M) where {T, S, N} at /Users/wangli/.julia/packages/StaticArrays/DBECI/src/SizedArray.jl:62
convert(::Type{Array{T,N}}, ::FillArrays.Zeros{V,N,Axes} where Axes) where {T, V, N} at /Users/wangli/.julia/packages/FillArrays/LYCik/src/FillArrays.jl:320
convert(::Type{Array{T,N}}, ::FillArrays.Ones{V,N,Axes} where Axes) where {T, V, N} at /Users/wangli/.julia/packages/FillArrays/LYCik/src/FillArrays.jl:320
...
Stacktrace:
[1] setproperty!(::OrdinaryDiffEq.ODEIntegrator{Tsit5,false,Array{Float64,2},Float64,Param{Array{Float64,2}},Float64,Float64,Float64,Array{Array{Float64,2},1},ODESolution{Float64,3,Array{Array{Float64,2},1},Nothing,Nothing,Array{Float64,1},Array{Array{Array{Float64,2},1},1},ODEProblem{Array{Float64,2},Tuple{Float64,Float64},false,Param{Array{Float64,2}},ODEFunction{false,typeof(f),LinearAlgebra.UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}},DiffEqBase.StandardODEProblem},Tsit5,OrdinaryDiffEq.InterpolationData{ODEFunction{false,typeof(f),LinearAlgebra.UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},Array{Array{Float64,2},1},Array{Float64,1},Array{Array{Array{Float64,2},1},1},OrdinaryDiffEq.Tsit5ConstantCache{Float64,Float64}},DiffEqBase.DEStats},ODEFunction{false,typeof(f),LinearAlgebra.UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},OrdinaryDiffEq.Tsit5ConstantCache{Float64,Float64},OrdinaryDiffEq.DEOptions{Float64,Float64,Float64,Float64,typeof(DiffEqBase.ODE_DEFAULT_NORM),typeof(LinearAlgebra.opnorm),CallbackSet{Tuple{},Tuple{}},typeof(DiffEqBase.ODE_DEFAULT_ISOUTOFDOMAIN),typeof(DiffEqBase.ODE_DEFAULT_PROG_MESSAGE),typeof(DiffEqBase.ODE_DEFAULT_UNSTABLE_CHECK),DataStructures.BinaryHeap{Float64,DataStructures.LessThan},DataStructures.BinaryHeap{Float64,DataStructures.LessThan},Nothing,Nothing,Int64,Array{Float64,1},Array{Float64,1},Array{Float64,1}},Array{Float64,2},Float64,Nothing}, ::Symbol, ::AutoGrad.Result{Array{Float64,2}}) at ./Base.jl:21
[2] initialize!(::OrdinaryDiffEq.ODEIntegrator{Tsit5,false,Array{Float64,2},Float64,Param{Array{Float64,2}},Float64,Float64,Float64,Array{Array{Float64,2},1},ODESolution{Float64,3,Array{Array{Float64,2},1},Nothing,Nothing,Array{Float64,1},Array{Array{Array{Float64,2},1},1},ODEProblem{Array{Float64,2},Tuple{Float64,Float64},false,Param{Array{Float64,2}},ODEFunction{false,typeof(f),LinearAlgebra.UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}},DiffEqBase.StandardODEProblem},Tsit5,OrdinaryDiffEq.InterpolationData{ODEFunction{false,typeof(f),LinearAlgebra.UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},Array{Array{Float64,2},1},Array{Float64,1},Array{Array{Array{Float64,2},1},1},OrdinaryDiffEq.Tsit5ConstantCache{Float64,Float64}},DiffEqBase.DEStats},ODEFunction{false,typeof(f),LinearAlgebra.UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},OrdinaryDiffEq.Tsit5ConstantCache{Float64,Float64},OrdinaryDiffEq.DEOptions{Float64,Float64,Float64,Float64,typeof(DiffEqBase.ODE_DEFAULT_NORM),typeof(LinearAlgebra.opnorm),CallbackSet{Tuple{},Tuple{}},typeof(DiffEqBase.ODE_DEFAULT_ISOUTOFDOMAIN),typeof(DiffEqBase.ODE_DEFAULT_PROG_MESSAGE),typeof(DiffEqBase.ODE_DEFAULT_UNSTABLE_CHECK),DataStructures.BinaryHeap{Float64,DataStructures.LessThan},DataStructures.BinaryHeap{Float64,DataStructures.LessThan},Nothing,Nothing,Int64,Array{Float64,1},Array{Float64,1},Array{Float64,1}},Array{Float64,2},Float64,Nothing}, ::OrdinaryDiffEq.Tsit5ConstantCache{Float64,Float64}) at /Users/wangli/.julia/packages/OrdinaryDiffEq/xJCph/src/perform_step/low_order_rk_perform_step.jl:565
[3] #__init#335(::Array{Float64,1}, ::Array{Float64,1}, ::Array{Float64,1}, ::Nothing, ::Bool, ::Bool, ::Bool, ::Bool, ::Nothing, ::Bool, ::Bool, ::Float64, ::Float64, ::Float64, ::Bool, ::Bool, ::Rational{Int64}, ::Nothing, ::Nothing, ::Rational{Int64}, ::Int64, ::Int64, ::Int64, ::Rational{Int64}, ::Bool, ::Int64, ::Nothing, ::Nothing, ::Int64, ::typeof(DiffEqBase.ODE_DEFAULT_NORM), ::typeof(LinearAlgebra.opnorm), ::typeof(DiffEqBase.ODE_DEFAULT_ISOUTOFDOMAIN), ::typeof(DiffEqBase.ODE_DEFAULT_UNSTABLE_CHECK), ::Bool, ::Bool, ::Bool, ::Bool, ::Bool, ::Bool, ::Bool, ::Int64, ::String, ::typeof(DiffEqBase.ODE_DEFAULT_PROG_MESSAGE), ::Nothing, ::Bool, ::Bool, ::Bool, ::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}, ::typeof(DiffEqBase.__init), ::ODEProblem{Array{Float64,2},Tuple{Float64,Float64},false,Param{Array{Float64,2}},ODEFunction{false,typeof(f),LinearAlgebra.UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}},DiffEqBase.StandardODEProblem}, ::Tsit5, ::Array{Array{Float64,2},1}, ::Array{Float64,1}, ::Array{Any,1}, ::Type{Val{true}}) at /Users/wangli/.julia/packages/OrdinaryDiffEq/xJCph/src/solve.jl:356
[4] __init(::ODEProblem{Array{Float64,2},Tuple{Float64,Float64},false,Param{Array{Float64,2}},ODEFunction{false,typeof(f),LinearAlgebra.UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}},DiffEqBase.StandardODEProblem}, ::Tsit5, ::Array{Array{Float64,2},1}, ::Array{Float64,1}, ::Array{Any,1}, ::Type{Val{true}}) at /Users/wangli/.julia/packages/OrdinaryDiffEq/xJCph/src/solve.jl:66 (repeats 4 times)
[5] #__solve#334 at /Users/wangli/.julia/packages/OrdinaryDiffEq/xJCph/src/solve.jl:4 [inlined]
[6] __solve at /Users/wangli/.julia/packages/OrdinaryDiffEq/xJCph/src/solve.jl:4 [inlined]
[7] #solve_call#433(::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}, ::typeof(DiffEqBase.solve_call), ::ODEProblem{Array{Float64,2},Tuple{Float64,Float64},false,Param{Array{Float64,2}},ODEFunction{false,typeof(f),LinearAlgebra.UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}},DiffEqBase.StandardODEProblem}, ::Tsit5) at /Users/wangli/.julia/packages/DiffEqBase/4V8I6/src/solve.jl:40
[8] solve_call at /Users/wangli/.julia/packages/DiffEqBase/4V8I6/src/solve.jl:37 [inlined]
[9] #solve#434 at /Users/wangli/.julia/packages/DiffEqBase/4V8I6/src/solve.jl:57 [inlined]
[10] solve(::ODEProblem{Array{Float64,2},Tuple{Float64,Float64},false,Param{Array{Float64,2}},ODEFunction{false,typeof(f),LinearAlgebra.UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}},DiffEqBase.StandardODEProblem}, ::Tsit5) at /Users/wangli/.julia/packages/DiffEqBase/4V8I6/src/solve.jl:45
[11] solver(::Param{Array{Float64,2}}) at /Users/wangli/masterthesis/models /scalable_model.jl:36
[12] loss(::Param{Array{Float64,2}}) at /Users/wangli/masterthesis/models /scalable_model.jl:42
[13] #differentiate#3(::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}, ::typeof(AutoGrad.differentiate), ::Function, ::Param{Array{Float64,2}}) at /Users/wangli/.julia/packages/AutoGrad/pTNVv/src/core.jl:144
[14] differentiate(::Function, ::Param{Array{Float64,2}}) at /Users/wangli/.julia/packages/AutoGrad/pTNVv/src/core.jl:135
[15] (::getfield(AutoGrad, Symbol("##gradfun#6#8")){typeof(loss),Int64,Bool})(::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}, ::getfield(AutoGrad, Symbol("#gradfun#7")){getfield(AutoGrad, Symbol("##gradfun#6#8")){typeof(loss),Int64,Bool}}, ::Array{Float64,2}) at /Users/wangli/.julia/packages/AutoGrad/pTNVv/src/core.jl:225
[16] (::getfield(AutoGrad, Symbol("#gradfun#7")){getfield(AutoGrad, Symbol("##gradfun#6#8")){typeof(loss),Int64,Bool}})(::Array{Float64,2}) at /Users/wangli/.julia/packages/AutoGrad/pTNVv/src/core.jl:221
[17] top-level scope at REPL[52]:1
from autograd.jl.
denizyuret
commented on May 18, 2024
This seems to be a different issue. Reopening to investigate when I find time. setproperty! suggests we are playing with object properties. Type mismatch suggests the code might be trying to put a Result where an Array is expected. Should try new AutoGrad interface if possible.
from autograd.jl.
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from autograd.jl.