Gradient missing for `begin ... end`

[roflmaostc]

julia> f1(x) = sum(@tullio res[i, j] := begin           
                       x[i+2, j] - 2 * x[i+1, j] + x[i, j]
                   end)
f1 (generic function with 1 method)

julia> gradient(f1, x)
ERROR: no gradient definition here!
Stacktrace:
 [1] error(s::String)
   @ Base ./error.jl:33
 [2] (::Tullio.var"#215#216"{Tullio.Eval{var"#ℳ𝒶𝓀ℯ#11"{var"#𝒜𝒸𝓉!#10"}, Nothing}, Tuple{Matrix{Float64}}, Matrix{Float64}})(Δ::FillArrays.Fill{Float64, 2, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}})
   @ Tullio ~/.julia/packages/Tullio/bgqFi/src/grad/zygote.jl:7
 [3] (::Tullio.var"#85#back#217"{Tullio.var"#215#216"{Tullio.Eval{var"#ℳ𝒶𝓀ℯ#11"{var"#𝒜𝒸𝓉!#10"}, Nothing}, Tuple{Matrix{Float64}}, Matrix{Float64}}})(Δ::FillArrays.Fill{Float64, 2, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}})
   @ Tullio ~/.julia/packages/ZygoteRules/OjfTt/src/adjoint.jl:59
 [4] Pullback
   @ ./REPL[39]:1 [inlined]
 [5] (::typeof(∂(f1)))(Δ::Float64)
   @ Zygote ~/.julia/packages/Zygote/KpME9/src/compiler/interface2.jl:0
 [6] (::Zygote.var"#41#42"{typeof(∂(f1))})(Δ::Float64)
   @ Zygote ~/.julia/packages/Zygote/KpME9/src/compiler/interface.jl:40
 [7] gradient(f::Function, args::Matrix{Float64})
   @ Zygote ~/.julia/packages/Zygote/KpME9/src/compiler/interface.jl:49
 [8] top-level scope
   @ REPL[40]:1

julia> f1(x) = sum(@tullio res[i, j] := x[i+2, j] - 2 * x[i+1, j] + x[i, j])
f1 (generic function with 1 method)

julia> gradient(f1, x)
([1.0 1.0 … 1.0 1.0; -1.0 -1.0 … -1.0 -1.0; … ; -1.0 -1.0 … -1.0 -1.0; 1.0 1.0 … 1.0 1.0],)

I encountered this because my equation got quite lengthy.

[mcabbott]

I suppose here it could sort this out, but in general when it sees begin ... end it concludes that you wanted an expression too complicated for the (very simple) symbolic differentiation here to handle. It can still handle them with dual numbers (and often this is no less efficient):

julia> using Zygote, ForwardDiff

julia> f1(x) = sum(@tullio res[i, j] := begin           
                       a = x[i+2, j] 
                       b = - 2 * x[i+1, j]
                       c = x[i, j]
                       a+b+c
                   end grad=Dual)
f1 (generic function with 1 method)

julia> gradient(f1, x)
([1.0 1.0 … 1.0 1.0; -1.0 -1.0 … -1.0 -1.0; … ; -1.0 -1.0 … -1.0 -1.0; 1.0 1.0 … 1.0 1.0],)