Comments (7)
ranjanan
commented on June 4, 2024
Pasting the stack trace here for reference:
julia> hessian_vector_product(g, x, v)#seems to fail during the coarse solve in AlgebraicMultigrid
ERROR: MethodError: no method matching svd!(::Array{ForwardDiff.Dual{ForwardDiff.Tag{var"#1#2"{typeof(g),Array{Float64,1},Array{Float64,1}},Float64},Float64,1},2}; full=false, alg=LinearAlgebra.DivideAndConque
r())
Closest candidates are:
svd!(::LinearAlgebra.AbstractTriangular; kwargs...) at D:\buildbot\worker\package_win64\build\usr\share\julia\stdlib\v1.5\LinearAlgebra\src\triangular.jl:2672
svd!(::StridedArray{T, 2}; full, alg) where T<:Union{Complex{Float32}, Complex{Float64}, Float32, Float64} at D:\buildbot\worker\package_win64\build\usr\share\julia\stdlib\v1.5\LinearAlgebra\src\svd.jl:93
svd!(::StridedArray{T, 2}, ::StridedArray{T, 2}) where T<:Union{Complex{Float32}, Complex{Float64}, Float32, Float64} at D:\buildbot\worker\package_win64\build\usr\share\julia\stdlib\v1.5\LinearAlgebra\src\s
vd.jl:363 got unsupported keyword arguments "full", "alg"
...
Stacktrace:
[1] svd(::Array{ForwardDiff.Dual{ForwardDiff.Tag{var"#1#2"{typeof(g),Array{Float64,1},Array{Float64,1}},Float64},Float64,1},2}; full::Bool, alg::LinearAlgebra.DivideAndConquer) at D:\buildbot\worker\package_w
in64\build\usr\share\julia\stdlib\v1.5\LinearAlgebra\src\svd.jl:158
[2] pinv(::Array{ForwardDiff.Dual{ForwardDiff.Tag{var"#1#2"{typeof(g),Array{Float64,1},Array{Float64,1}},Float64},Float64,1},2}; atol::Float64, rtol::Float64) at D:\buildbot\worker\package_win64\build\usr\sha
re\julia\stdlib\v1.5\LinearAlgebra\src\dense.jl:1356
[3] pinv at D:\buildbot\worker\package_win64\build\usr\share\julia\stdlib\v1.5\LinearAlgebra\src\dense.jl:1335 [inlined]
[4] #adjoint#762 at C:\Users\Ranjan Anantharaman\.julia\packages\Zygote\rqvFi\src\lib\array.jl:400 [inlined]
[5] adjoint at .\none:0 [inlined]
[6] _pullback at C:\Users\Ranjan Anantharaman\.julia\packages\ZygoteRules\6nssF\src\adjoint.jl:47 [inlined]
[7] Pinv at C:\Users\Ranjan Anantharaman\.julia\packages\AlgebraicMultigrid\RU7pA\src\multilevel.jl:57 [inlined]
[8] _pullback(::Zygote.Context, ::Type{AlgebraicMultigrid.Pinv{ForwardDiff.Dual{ForwardDiff.Tag{var"#1#2"{typeof(g),Array{Float64,1},Array{Float64,1}},Float64},Float64,1}}}, ::SparseArrays.SparseMatrixCSC{For
wardDiff.Dual{ForwardDiff.Tag{var"#1#2"{typeof(g),Array{Float64,1},Array{Float64,1}},Float64},Float64,1},Int64}) at C:\Users\Ranjan Anantharaman\.julia\packages\Zygote\rqvFi\src\compiler\interface2.jl:0
[9] Pinv at C:\Users\Ranjan Anantharaman\.julia\packages\AlgebraicMultigrid\RU7pA\src\multilevel.jl:59 [inlined]
[10] _pullback(::Zygote.Context, ::Type{AlgebraicMultigrid.Pinv}, ::SparseArrays.SparseMatrixCSC{ForwardDiff.Dual{ForwardDiff.Tag{var"#1#2"{typeof(g),Array{Float64,1},Array{Float64,1}},Float64},Float64,1},Int
64}) at C:\Users\Ranjan Anantharaman\.julia\packages\Zygote\rqvFi\src\compiler\interface2.jl:0
[11] #ruge_stuben#13 at C:\Users\Ranjan Anantharaman\.julia\packages\AlgebraicMultigrid\RU7pA\src\classical.jl:44 [inlined]
[12] _pullback(::Zygote.Context, ::AlgebraicMultigrid.var"##ruge_stuben#13", ::AlgebraicMultigrid.Classical{Float64}, ::AlgebraicMultigrid.RS, ::AlgebraicMultigrid.GaussSeidel{AlgebraicMultigrid.SymmetricSwee
p}, ::AlgebraicMultigrid.GaussSeidel{AlgebraicMultigrid.SymmetricSweep}, ::Int64, ::Int64, ::Type{AlgebraicMultigrid.Pinv}, ::typeof(AlgebraicMultigrid.ruge_stuben), ::SparseArrays.SparseMatrixCSC{ForwardDiff.
Dual{ForwardDiff.Tag{var"#1#2"{typeof(g),Array{Float64,1},Array{Float64,1}},Float64},Float64,1},Int64}, ::Type{Val{1}}) at C:\Users\Ranjan Anantharaman\.julia\packages\Zygote\rqvFi\src\compiler\interface2.jl:0
[13] ruge_stuben at C:\Users\Ranjan Anantharaman\.julia\packages\AlgebraicMultigrid\RU7pA\src\classical.jl:20 [inlined] (repeats 2 times)
[14] _pullback(::Zygote.Context, ::typeof(AlgebraicMultigrid.ruge_stuben), ::SparseArrays.SparseMatrixCSC{ForwardDiff.Dual{ForwardDiff.Tag{var"#1#2"{typeof(g),Array{Float64,1},Array{Float64,1}},Float64},Float
64,1},Int64}) at C:\Users\Ranjan Anantharaman\.julia\packages\Zygote\rqvFi\src\compiler\interface2.jl:0
[15] g at .\REPL[20]:4 [inlined]
[16] _pullback(::Zygote.Context, ::typeof(g), ::Array{ForwardDiff.Dual{ForwardDiff.Tag{var"#1#2"{typeof(g),Array{Float64,1},Array{Float64,1}},Float64},Float64,1},1}) at C:\Users\Ranjan Anantharaman\.julia\pac
kages\Zygote\rqvFi\src\compiler\interface2.jl:0
[17] _pullback(::Function, ::Array{ForwardDiff.Dual{ForwardDiff.Tag{var"#1#2"{typeof(g),Array{Float64,1},Array{Float64,1}},Float64},Float64,1},1}) at C:\Users\Ranjan Anantharaman\.julia\packages\Zygote\rqvFi\
src\compiler\interface.jl:38
[18] pullback(::Function, ::Array{ForwardDiff.Dual{ForwardDiff.Tag{var"#1#2"{typeof(g),Array{Float64,1},Array{Float64,1}},Float64},Float64,1},1}) at C:\Users\Ranjan Anantharaman\.julia\packages\Zygote\rqvFi\s
rc\compiler\interface.jl:44
[19] gradient(::Function, ::Array{ForwardDiff.Dual{ForwardDiff.Tag{var"#1#2"{typeof(g),Array{Float64,1},Array{Float64,1}},Float64},Float64,1},1}) at C:\Users\Ranjan Anantharaman\.julia\packages\Zygote\rqvFi\s
rc\compiler\interface.jl:53
[20] (::var"#1#2"{typeof(g),Array{Float64,1},Array{Float64,1}})(::Array{ForwardDiff.Dual{ForwardDiff.Tag{var"#1#2"{typeof(g),Array{Float64,1},Array{Float64,1}},Float64},Float64,1},1}) at .\REPL[10]:1
[21] vector_mode_dual_eval at C:\Users\Ranjan Anantharaman\.julia\packages\ForwardDiff\sdToQ\src\apiutils.jl:37 [inlined]
[22] vector_mode_jacobian(::var"#1#2"{typeof(g),Array{Float64,1},Array{Float64,1}}, ::Array{Float64,1}, ::ForwardDiff.JacobianConfig{ForwardDiff.Tag{var"#1#2"{typeof(g),Array{Float64,1},Array{Float64,1}},Floa
t64},Float64,1,Array{ForwardDiff.Dual{ForwardDiff.Tag{var"#1#2"{typeof(g),Array{Float64,1},Array{Float64,1}},Float64},Float64,1},1}}) at C:\Users\Ranjan Anantharaman\.julia\packages\ForwardDiff\sdToQ\src\jacob
ian.jl:140
[23] jacobian(::Function, ::Array{Float64,1}, ::ForwardDiff.JacobianConfig{ForwardDiff.Tag{var"#1#2"{typeof(g),Array{Float64,1},Array{Float64,1}},Float64},Float64,1,Array{ForwardDiff.Dual{ForwardDiff.Tag{var"
#1#2"{typeof(g),Array{Float64,1},Array{Float64,1}},Float64},Float64,1},1}}, ::Val{true}) at C:\Users\Ranjan Anantharaman\.julia\packages\ForwardDiff\sdToQ\src\jacobian.jl:17
[24] jacobian(::Function, ::Array{Float64,1}, ::ForwardDiff.JacobianConfig{ForwardDiff.Tag{var"#1#2"{typeof(g),Array{Float64,1},Array{Float64,1}},Float64},Float64,1,Array{ForwardDiff.Dual{ForwardDiff.Tag{var"
#1#2"{typeof(g),Array{Float64,1},Array{Float64,1}},Float64},Float64,1},1}}) at C:\Users\Ranjan Anantharaman\.julia\packages\ForwardDiff\sdToQ\src\jacobian.jl:15 (repeats 2 times)
[25] hessian_vector_product(::Function, ::Array{Float64,1}, ::Array{Float64,1}) at .\REPL[10]:1
[26] top-level scope at REPL[23]:1from algebraicmultigrid.jl.
ChrisRackauckas
commented on June 4, 2024
Do https://github.com/JuliaLinearAlgebra/GenericSVD.jl and see if that brings in the right dispatch. You may need to remove the kwargs from the svd! call in order to have the available dispatch.
from algebraicmultigrid.jl.
omalled
commented on June 4, 2024
Here's an updated version with using GenericSVD that produces a StackOverflowError instead of the MethodError in the old stack trace:
using Test
import AlgebraicMultigrid
import ForwardDiff
using GenericSVD
import LinearAlgebra
import SparseArrays
import Zygote
hessian_vector_product(f, x, v) = ForwardDiff.jacobian(s->Zygote.gradient(f, x + s[1] * v)[1], [0.0])[:]
n = 4
A = randn(n, n)
hessian = A + A'
f(x) = LinearAlgebra.dot(x, A * x)
x = randn(n)
v = randn(n)
hvp2 = hessian * v
hvp1 = hessian_vector_product(f, x, v)
@test hvp1 ≈ hvp2#the hessian_vector_product plausibly works!
function g(x)
k = x[1:n + 1]
B = SparseArrays.spdiagm(0=>k[1:end - 1] + k[2:end], -1=>-k[2:end - 1], 1=>-k[2:end - 1])
ml = AlgebraicMultigrid.ruge_stuben(B)
return sum(AlgebraicMultigrid.solve(ml, x[N + 2:end]))
end
x = randn(2 * n + 1)
v = randn(2 * n + 1)
hessian_vector_product(g, x, v)#stack overflow
Here's the stack trace:
┌ Warning: keyword `alg` ignored in generic svd!
└ @ GenericSVD ~/.julia/packages/GenericSVD/cT5Cu/src/GenericSVD.jl:12
ERROR: StackOverflowError:
Stacktrace:
[1] givensAlgorithm(::ForwardDiff.Dual{ForwardDiff.Tag{var"#7#8"{typeof(g),Array{Float64,1},Array{Float64,1}},Float64},Float64,1}, ::ForwardDiff.Dual{ForwardDiff.Tag{var"#7#8"{typeof(g),Array{Float64,1},Array{Float64,1}},Float64},Float64,1}) at /Users/julia/buildbot/worker/package_macos64/build/usr/share/julia/stdlib/v1.5/LinearAlgebra/src/givens.jl:251 (repeats 79984 times)
Should I open an issue over at GenericSVD?
from algebraicmultigrid.jl.
ChrisRackauckas
commented on June 4, 2024
yeah that looks like some type assumption was violated in GenericSVD
from algebraicmultigrid.jl.
ViralBShah
commented on June 4, 2024
@ranjanan Whenever you have a moment - let's get this one done.
from algebraicmultigrid.jl.
ViralBShah
commented on June 4, 2024
Also pinging @DhairyaLGandhi. This is a bit urgent to resolve.
from algebraicmultigrid.jl.
ranjanan
commented on June 4, 2024
JuliaLinearAlgebra/GenericSVD.jl#25 (comment)
from algebraicmultigrid.jl.
Related Issues (20)
- Support custom interpolations
- Multithreading not working when using OpenBLAS HOT 9
- Creating multigrid hierarchy (e.g., `ruge_stuben`) allocates too much memory
- Increase test coverage with MatrixDepot.jl
- Info about upcoming removal of packages in the General registry HOT 4
- Info about upcoming removal of packages in the General registry HOT 1
- Dependency cleanup HOT 4
- verbose HOT 1
- TagBot trigger issue HOT 6
- Got unsupported keyword arguments "strength", "max_levels", "max_coarse" HOT 1
- User-defined near null space HOT 3
- Move from Travis to Github Actions HOT 3
- Optimization-Based Algebraic Multigrid Coarsening Using Reinforcement Learning (NeurIPS 2021)
- Avoid printing numerical values of Coarse Solver when summarizing MultiLevel solver HOT 2
- Printed Grid Complexity is wrong (just copies Operator Complexity) HOT 2
- Support for CuSparse HOT 3
- Use sparse LU as a faster coarse solver than Pinv HOT 4
- Default ruge_stuben solver diverges for a Poisson problem, due to problematic interpolation HOT 3
- Inf on preconditioner \ b HOT 2
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from algebraicmultigrid.jl.