# Precompiling a function for \`ForwardDiff\` call

**URL:** <https://discourse.julialang.org/t/precompiling-a-function-for-forwarddiff-call/113041>\
**Category:** New to Julia\
**Created:** [April 16, 2024, 9:50pm UTC](https://discourse.julialang.org/t/precompiling-a-function-for-forwarddiff-call/113041 "2024-04-16T21:50:20Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![liamh](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/liamh/32/208541_2.png) [@liamh](https://discourse.julialang.org/u/liamh)\
**Post date:** [April 16, 2024, 9:50pm UTC](https://discourse.julialang.org/t/precompiling-a-function-for-forwarddiff-call/113041/1 "2024-04-16T21:50:20Z")

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I am using `ForwardDiff` on my own function. While I expect that the first call spends a substantial amount of time compiling, subsequent identical calls are also spending 99.8+% of the time compiling. I have tried reviewing [the precompiling tutorial](https://julialang.org/blog/2021/01/precompile_tutorial/), but I am lost.

```julia
julia> @time ForwardDiff.derivative(x -> myfn(arg1, arg2, arg3, x), 1.0)
  4.547712 seconds (6.87 M allocations: 457.011 MiB, 9.40% gc time, 99.89% compilation time)
julia> @time ForwardDiff.derivative(x -> myfn(arg1, arg2, arg3, x), 1.0)
  2.553844 seconds (4.39 M allocations: 290.645 MiB, 2.95% gc time, 99.84% compilation time)
julia> @time ForwardDiff.derivative(x -> myfn(arg1, arg2, arg3, x), 1.0)
  2.692889 seconds (4.39 M allocations: 290.668 MiB, 7.38% gc time, 99.85% compilation time)
julia> @time ForwardDiff.derivative(x -> myfn(arg1, arg2, arg3, x), 1.0)
  2.601984 seconds (4.39 M allocations: 290.660 MiB, 3.31% gc time, 99.85% compilation time)

```

If I know that all the arguments (including `x`) will be `Float64` or `Vector{Float64}`, can I precompile this? Currently, I have declared my function in the [`where T` style](https://discourse.julialang.org/t/error-with-forwarddiff-no-method-matching-float64/41905/2); would it help if that was different?

Clearly a possible 4 msec runtime is a lot more attractive than 2.5+ seconds for a function I expect to call hundreds of millions of times.

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**Author:** ![danielwe](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/danielwe/32/35657_2.png) [@danielwe](https://discourse.julialang.org/u/danielwe)\
**Post date:** [April 16, 2024, 11:47pm UTC](https://discourse.julialang.org/t/precompiling-a-function-for-forwarddiff-call/113041/2 "2024-04-16T23:47:01Z")

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Welcome! You’re defining a new anonymous function `x -> myfn(arg1, arg2, arg3, x)` on every line, which must be compiled, and also requires the compilation of a corresponding method of `ForwardDiff.derivative`. To avoid this, wrap a function around the expression you’re benchmarking:

```julia-repl
julia> f(y) = ForwardDiff.derivative(x -> myfn(arg1, arg2, arg3, x), y);

julia> @time f(1.0)

```

Also, `arg1`, `arg2` and `arg3` seem to be global variables, so for performance, make sure they are declared `const`.