# Zygote dropgrad for all function numerical arguments that are non-differentiable (e.g., Int)

**URL:** <https://discourse.julialang.org/t/zygote-dropgrad-for-all-function-numerical-arguments-that-are-non-differentiable-e-g-int/63563>\
**Category:** General Usage\
**Tags:** zygote\
**Created:** [June 25, 2021, 4:53pm UTC](https://discourse.julialang.org/t/zygote-dropgrad-for-all-function-numerical-arguments-that-are-non-differentiable-e-g-int/63563 "2021-06-25T16:53:38Z")\
**Posts on this page:** 2\
**Page:** 1

<div class="post-metadata">

**Author:** ![mhmodzoka](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/mhmodzoka/32/25582_2.png) [@mhmodzoka](https://discourse.julialang.org/u/mhmodzoka)\
**Post date:** [June 25, 2021, 4:53pm UTC](https://discourse.julialang.org/t/zygote-dropgrad-for-all-function-numerical-arguments-that-are-non-differentiable-e-g-int/63563/1 "2021-06-25T16:53:38Z")

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Is there a way I can tell Zygote to automatically apply `Zygote.dropgrad()` for all function numerical arguments that are non-differentiable?

Example:

```julia
julia> import Zygote

julia> # naiively defining my function, and using Zygote to calculate the gradient
       f(n::Integer,x::Real) = sin(n*x)
f (generic function with 1 method)

julia> Zygote.gradient(f, 100, π)
(3.141592653589793, 100.0)

```

Zygote performs automatic differentiation with respect to `n`, although `n` is an integer and `f` can’t be differentiated with respect to `n`.

I hope Zygote can apply `Zygote.dropgrad()` to all arguments that are Integers, like `n`:

```julia
julia> f(n::Integer,x::Real) = sin(Zygote.dropgrad(n) * x)
f (generic function with 1 method)

julia> Zygote.gradient(f, 100, π)
(nothing, 100.0)

```

---

<div class="post-metadata">

**Author:** ![mcabbott](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/mcabbott/32/6603_2.png) [@mcabbott](https://discourse.julialang.org/u/mcabbott)\
**Post date:** [June 25, 2021, 5:18pm UTC](https://discourse.julialang.org/t/zygote-dropgrad-for-all-function-numerical-arguments-that-are-non-differentiable-e-g-int/63563/2 "2021-06-25T17:18:27Z")

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It would not be crazy to regard all integers as categorical and only floats as differentiable, but most of Julia isn’t fussy about such things, and I think Zygote just follows that & promotes:

```julia
julia> sin(1) == sin(1.0)
true

julia> gradient(sin, 1) == gradient(sin, 1.0) # this seems very Julian
true

julia> sin(ForwardDiff.Dual(1,true))
Dual{Nothing}(0.8414709848078965,0.5403023058681398)

julia> gradient(x -> abs(sin(x + 0*im)), 1) # this we should fix
(0.5403023058681398 + 0.0im,)

```

Changing all integers would, for instance, break almost every example in Zygote’s readme, which I think is evidence that it would be surprising.

But I think you’re proposing only that a function whose signature specifies `::Integer` or similar should give no gradient. That would probably be a good idea, someone would just need to figure out how to implement it.

```julia
julia> f(n::Integer,x::Real) = sin(n*x); # has no specific rule

julia> gradient(f, 7, 0)
(0.0, 7.0) # now
(nothing, 7.0) # proposed

julia> f(ForwardDiff.Dual(7,true), 0) # disallowed here
ERROR: MethodError: no method matching f(::ForwardDiff.Dual{Nothing, Int64, 1}, ::Int64)

julia> gradient(getindex, [5,4,3], 2) # this has a rule
([0, 1, 0], nothing)

```