JuliaDiff / JuliaDiff/Diffractor.jl

Error when computing jacobian for broadcasted function

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Description

Simple broadcasting errors for me. See below.
I am on Julia 1.10.0-beta2+0.x64.linux.gnu

within the following environment:

Status ~/.julia/environments/v1.10/Project.toml
[c29ec348] AbstractDifferentiation v0.5.2
[9f5e2b26] Diffractor v0.2.1
[31c24e10] Distributions v0.25.100

This shows the issue (verified that I get the same problem with gradient from AbstractDifferentiation.jl):

using Diffractor: DiffractorForwardBackend
using AbstractDifferentiation: derivative, jacobian
jacobian(DiffractorForwardBackend(), θ -> sin(θ[1]) + sin(θ[2]), [0.3, 0.8])  #  works
jacobian(DiffractorForwardBackend(), θ -> sum(sin, θ), [0.3, 0.8])                # works
jacobian(DiffractorForwardBackend(), θ -> sum(sin.(θ)), [0.3, 0.8])               # errors

last command errors with (truncated error):

ERROR: MethodError: no method matching Diffractor.TangentBundle{1, Float64, Diffractor.TaylorTangent{Tuple{Float64}}}(::Float64, ::Tuple{Float64})
Stacktrace:
[1] createinstance(::Type{Diffractor.TangentBundle{1, Float64, Diffractor.TaylorTangent{Tuple{Float64}}}}, ::Float64, ::Vararg{Float64})
@ Diffractor ~/.julia/packages/Diffractor/vzpvE/src/tangent.jl:351
[2] _getindex
@ Diffractor ~/.julia/packages/StructArrays/dNQpc/src/structarray.jl:353 [inlined]
[3] getindex
@ Diffractor ~/.julia/packages/StructArrays/dNQpc/src/structarray.jl:348 [inlined]
[4] iterate
@ Diffractor ./abstractarray.jl:1212 [inlined]
[5] iterate
@ Diffractor ./abstractarray.jl:1210 [inlined]
[6] iterate
@ Diffractor ./generator.jl:44 [inlined]
[7] _collect
@ Diffractor ./array.jl:852 [inlined]
[8] collect_similar
@ Diffractor ./array.jl:761 [inlined]
[9] map
@ Diffractor ./abstractarray.jl:3273 [inlined]
[10] rebundle(A::StructArrays.StructVector{Diffractor.TangentBundle{1, Float64, Diffractor.TaylorTangent{Tuple{Float64}}}, Tuple{Vector{Float64}, Vector{Float64}}, Int64})
@ Diffractor ~/.julia/packages/Diffractor/vzpvE/src/tangent.jl:375
[11] (::Diffractor.∂☆{1})(zc::ZeroBundle{1, typeof(copy)}, bc::Diffractor.CompositeBundle{1, Base.Broadcast.Broadcasted{…}, Tuple{…}})
@ Diffractor ~/.julia/packages/Diffractor/vzpvE/src/stage1/broadcast.jl:27
[12] materialize
@ Diffractor ./broadcast.jl:903 [inlined]
[13] (::Diffractor.∂☆internal{1})(::ZeroBundle{1, Type{Base.Broadcast.Broadcasted}}, ::Vararg{Diffractor.AbstractTangentBundle{1}})
@ Diffractor ~/.julia/packages/Diffractor/vzpvE/src/stage1/forward.jl:121 [inlined]
[14] ∂☆
@ Diffractor ~/.julia/packages/Diffractor/vzpvE/src/stage1/forward.jl:160 [inlined]
[15] #61
@ Diffractor ./REPL[45]:1 [inlined]
[16] (::Diffractor.∂☆recurse{1})(::ZeroBundle{1, var"#61#62"}, ::Diffractor.TangentBundle{1, Vector{Float64}, Diffractor.TaylorTangent{Tuple{Vector{Float64}}}})
@ Diffractor ~/.julia/packages/Diffractor/vzpvE/src/stage1/recurse_fwd.jl:0
[17] (::Diffractor.∂☆internal{1})(::ZeroBundle{1, var"#61#62"}, ::Vararg{Diffractor.AbstractTangentBundle{1}})
@ Diffractor ~/.julia/packages/Diffractor/vzpvE/src/stage1/forward.jl:121
[18] (::Diffractor.∂☆{1})(::ZeroBundle{1, var"#61#62"}, ::Vararg{Diffractor.AbstractTangentBundle{1}})
@ Diffractor ~/.julia/packages/Diffractor/vzpvE/src/stage1/forward.jl:160
[19] (::Diffractor.var"#pushforward#359"{var"#61#62", Tuple{Vector{Float64}}})(vs::Tuple{Vector{Float64}})
@ Diffractor ~/.julia/packages/Diffractor/vzpvE/src/AbstractDifferentiation.jl:13
[20] (::Diffractor.var"#358#362"{Diffractor.var"#pushforward#359"{var"#61#62", Tuple{Vector{Float64}}}})(cols::Vector{Float64})
@ Diffractor ~/.julia/packages/AbstractDifferentiation/eEkWP/src/AbstractDifferentiation.jl:526
[21] mapslices(f::Diffractor.var"#358#362"{Diffractor.var"#pushforward#359"{var"#61#62", Tuple{Vector{Float64}}}}, A::Matrix{Float64}; dims::Int64)
@ Base ./abstractarray.jl:3179
[22] jacobian(b::DiffractorForwardBackend, f::Function, args::Vector{Float64})
@ Diffractor ~/.julia/packages/AbstractDifferentiation/eEkWP/src/AbstractDifferentiation.jl:524
[23] top-level scope
@ REPL[45]:1
Some type information was truncated. Use show(err) to see complete types.

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Research direction

Reproduce the three jacobian calls with Julia 1.10.0-beta2 and the listed AbstractDifferentiation and Diffractor versions. Start with src/stage1/broadcast.jl:27 and the createinstance/rebundle paths at src/tangent.jl:351 and :375, then compare the working and failing broadcast cases. Done means the elementwise broadcasted sin expression computes its jacobian without the reported TangentBundle MethodError.

Written by the indexing model from the issue text.

Assessment

Tech stack
julia
Domain
machine-learning
Issue type
Bug
Difficulty
3/5
Estimated time
1-2 days
Activity status
Stale
Clarity
Mostly clear
Newbie friendliness
35/100

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