# Can't use Zygote.gradient on Symbolics.jl Matrix Functions
**URL:** https://discourse.julialang.org/t/cant-use-zygote-gradient-on-symbolics-jl-matrix-functions/59225
**Category:** General Usage
**Tags:** zygote, symbolic
**Created:** [April 13, 2021, 6:41pm UTC](https://discourse.julialang.org/t/cant-use-zygote-gradient-on-symbolics-jl-matrix-functions/59225 "2021-04-13T18:41:00Z")
**Posts on this page:** 4
**Page:** 1
### Author:  [@jbu](https://discourse.julialang.org/u/jbu)
#### Post date: [April 13, 2021, 6:41pm UTC](https://discourse.julialang.org/t/cant-use-zygote-gradient-on-symbolics-jl-matrix-functions/59225/1 "2021-04-13T18:41:00Z")
When using a matrix function generated using Symbolics.jl variables, I get the error `Mutating arrays is not supported` when trying to autodifferentiate through the matrix function with either `Zygote.gradient` or `Zygote.jacobian`. Minimum working example is below, and full error stacktrace is at the bottom of this post.
> **MWE**
>
> ```julia
> using Symbolics, Zygote
> @variables x, y
>
> M = [x y]
> M_expr = build_function(M, [x,y])
> M_func = eval(M_expr[1])
>
> Zygote.gradient(z->sum(M_func(z)), [1,2]) # Error; full stacktrace at bottom of post
>
> ```
>
> Julia version is 1.6.0.
> Symbolics.jl is v0.1.12
> Zygote.jl is v0.6.7
Is it possible to create matrix functions using Symbolics.jl that do not use mutation? Otherwise I’d like to know how to properly use Zygote.jl with matrix functions generated by Symbolics.jl.
> **Additional Details:**
>
> If I perform the `sum()` function on the symbolic matrix before calling `build_function`, then Zygote can take the gradient:
>
> ```julia
> M_sum = sum(M)
> M_sum_expr = build_function(M_sum, [x,y])
> M_sum_func = eval(M_sum_expr)
>
> Zygote.gradient(M_sum_func, [1,2]) # This works
>
> ```
>
> I don’t think I have this option however–I’m trying to autodifferentiate through a DifferentialEquations.jl ODE of the form \dot{x} = A(p)x + Bu, y = C(p)x, where p contains the parameters I’m trying to differentiate with respect to. As far as I’m aware, the functions A(), C() can’t be passed into an ODE solver in their symbolic form, but I could be wrong.
> **Full Stacktrace**
>
> ```julia
> ERROR: Mutating arrays is not supported
> Stacktrace:
> [1] error(s::String)
> @ Base ./error.jl:33
> [2] (::Zygote.var"#399#400")(#unused#::Nothing)
> @ Zygote ~/.julia/packages/Zygote/fjuG8/src/lib/array.jl:58
> [3] (::Zygote.var"#2253#back#401"{Zygote.var"#399#400"})(Δ::Nothing)
> @ Zygote ~/.julia/packages/ZygoteRules/OjfTt/src/adjoint.jl:59
> [4] Pullback
> @ ~/.julia/packages/SymbolicUtils/aNxjZ/src/code.jl:383 [inlined]
> [5] (::Zygote.var"#178#179"{Tuple{Tuple{Nothing, Nothing, Nothing}, Tuple{Nothing, Nothing}}, typeof(∂(_create_array))})(Δ::FillArrays.Fill{Int64, 2, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}})
> @ Zygote ~/.julia/packages/Zygote/fjuG8/src/lib/lib.jl:194
> [6] #1686#back
> @ ~/.julia/packages/ZygoteRules/OjfTt/src/adjoint.jl:59 [inlined]
> [7] Pullback
> @ ~/.julia/packages/SymbolicUtils/aNxjZ/src/code.jl:394 [inlined]
> [8] Pullback
> @ ~/.julia/packages/SymbolicUtils/aNxjZ/src/code.jl:371 [inlined]
> [9] (::typeof(∂(#11)))(Δ::FillArrays.Fill{Int64, 2, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}})
> @ Zygote ~/.julia/packages/Zygote/fjuG8/src/compiler/interface2.jl:0
> [10] Pullback
> @ ./REPL[38]:1 [inlined]
> [11] (::typeof(∂(#13)))(Δ::Int64)
> @ Zygote ~/.julia/packages/Zygote/fjuG8/src/compiler/interface2.jl:0
> [12] (::Zygote.var"#41#42"{typeof(∂(#13))})(Δ::Int64)
> @ Zygote ~/.julia/packages/Zygote/fjuG8/src/compiler/interface.jl:41
> [13] gradient(f::Function, args::Vector{Int64})
> @ Zygote ~/.julia/packages/Zygote/fjuG8/src/compiler/interface.jl:59
> [14] top-level scope
> @ REPL[38]:1
>
> ```
---
### Author:  [@ChrisRackauckas](https://discourse.julialang.org/u/ChrisRackauckas)
#### Post date: [April 13, 2021, 6:45pm UTC](https://discourse.julialang.org/t/cant-use-zygote-gradient-on-symbolics-jl-matrix-functions/59225/2 "2021-04-13T18:45:15Z")
Open an issue for this.
---
### Author:  [@jbu](https://discourse.julialang.org/u/jbu)
#### Post date: [April 13, 2021, 6:56pm UTC](https://discourse.julialang.org/t/cant-use-zygote-gradient-on-symbolics-jl-matrix-functions/59225/3 "2021-04-13T18:56:26Z")
Done. Issue has been opened [here](https://github.com/JuliaSymbolics/Symbolics.jl/issues/199).
As an update, `ForwardDiff.gradient` works with `M_func` as defined above. For example:
```julia
julia> ForwardDiff.gradient(z->sum(M_func(z)),[1,2])
2-element Vector{Int64}:
1
1
```
I’m under the impression that Zygote is often the preferred package though.
---
### Author:  [@ChrisRackauckas](https://discourse.julialang.org/u/ChrisRackauckas)
#### Post date: [April 13, 2021, 10:30pm UTC](https://discourse.julialang.org/t/cant-use-zygote-gradient-on-symbolics-jl-matrix-functions/59225/4 "2021-04-13T22:30:16Z")
Depends on the details of the function. But I think @dhairyagandhi96 just fixed this.