# Inconsistent llvm code
**URL:** https://discourse.julialang.org/t/inconsistent-llvm-code/794
**Category:** General Usage
**Created:** [December 7, 2016, 10:45pm UTC](https://discourse.julialang.org/t/inconsistent-llvm-code/794 "2016-12-07T22:45:37Z")
**Posts on this page:** 7
**Page:** 1
### Author:  [@augustinyiptong](https://discourse.julialang.org/u/augustinyiptong)
#### Post date: [December 7, 2016, 10:45pm UTC](https://discourse.julialang.org/t/inconsistent-llvm-code/794/1 "2016-12-07T22:45:37Z")
Hi, i am using julia 0.5.0
If i do:
```julia
import Distributions
@code_llvm Distributions.quantile(Distributions.Normal(), 0.5)
```
I get:
```julia
; Function Attrs: uwtable
define double @julia_quantile_71921(%Normal*, double) #0 {
top:
%2 = fmul double %1, 2.000000e+00
%3 = call double @julia_erfcinv_71923(double %2) #1
%4 = getelementptr inbounds %Normal, %Normal* %0, i64 0, i32 0
%5 = load double, double* %4, align 8
%6 = getelementptr inbounds %Normal, %Normal* %0, i64 0, i32 1
%7 = load double, double* %6, align 8
%8 = fmul double %3, 0xBFF6A09E667F3BCD
%9 = fmul double %8, %7
%10 = fadd double %5, %9
ret double %10
}
```
However if i do:
```julia
import Optim
import Distributions
@code_llvm Distributions.quantile(Distributions.Normal(), 0.5)
```
I get:
```julia
; Function Attrs: uwtable
define %jl_value_t* @julia_quantile_71921(%Normal*, double) #0 {
top:
%2 = call %jl_value_t*** @jl_get_ptls_states() #2
%3 = alloca [14 x %jl_value_t*], align 8
%.sub = getelementptr inbounds [14 x %jl_value_t*], [14 x %jl_value_t*]* %3, i64 0, i64 0
%4 = getelementptr [14 x %jl_value_t*], [14 x %jl_value_t*]* %3, i64 0, i64 2
%5 = getelementptr [14 x %jl_value_t*], [14 x %jl_value_t*]* %3, i64 0, i64 9
%6 = getelementptr [14 x %jl_value_t*], [14 x %jl_value_t*]* %3, i64 0, i64 12
%7 = getelementptr [14 x %jl_value_t*], [14 x %jl_value_t*]* %3, i64 0, i64 6
%8 = bitcast %jl_value_t** %4 to i8*
call void @llvm.memset.p0i8.i32(i8* %8, i8 0, i32 96, i32 8, i1 false)
%9 = bitcast [14 x %jl_value_t*]* %3 to i64*
store i64 24, i64* %9, align 8
%10 = getelementptr [14 x %jl_value_t*], [14 x %jl_value_t*]* %3, i64 0, i64 1
%11 = bitcast %jl_value_t*** %2 to i64*
%12 = load i64, i64* %11, align 8
%13 = bitcast %jl_value_t** %10 to i64*
store i64 %12, i64* %13, align 8
store %jl_value_t **%.sub, %jl_value_t*** %2, align 8
%14 = getelementptr [14 x %jl_value_t*], [14 x %jl_value_t*]* %3, i64 0, i64 11
%15 = getelementptr [14 x %jl_value_t*], [14 x %jl_value_t*]* %3, i64 0, i64 10
%16 = getelementptr [14 x %jl_value_t*], [14 x %jl_value_t*]* %3, i64 0, i64 5
%17 = getelementptr [14 x %jl_value_t*], [14 x %jl_value_t*]* %3, i64 0, i64 4
%18 = getelementptr [14 x %jl_value_t*], [14 x %jl_value_t*]* %3, i64 0, i64 3
%19 = getelementptr [14 x %jl_value_t*], [14 x %jl_value_t*]* %3, i64 0, i64 8
%20 = getelementptr [14 x %jl_value_t*], [14 x %jl_value_t*]* %3, i64 0, i64 7
%21 = getelementptr [14 x %jl_value_t*], [14 x %jl_value_t*]* %3, i64 0, i64 13
%22 = getelementptr inbounds %Normal, %Normal* %0, i64 0, i32 1
%23 = fmul double %1, 2.000000e+00
%24 = call %jl_value_t* @julia_erfcinv_71923(double %23) #1
store %jl_value_t* %24, %jl_value_t** %21, align 8
store %jl_value_t* inttoptr (i64 2150928800 to %jl_value_t*), %jl_value_t** %6, align 8
%25 = call %jl_value_t* @jl_apply_generic(%jl_value_t** %6, i32 2)
store %jl_value_t* %25, %jl_value_t** %20, align 8
store %jl_value_t* inttoptr (i64 2150931984 to %jl_value_t*), %jl_value_t** %7, align 8
store %jl_value_t* inttoptr (i64 2152744728 to %jl_value_t*), %jl_value_t** %19, align 8
%26 = call %jl_value_t* @jl_apply_generic(%jl_value_t** %7, i32 3)
store %jl_value_t* %26, %jl_value_t** %16, align 8
store %jl_value_t* inttoptr (i64 2152744696 to %jl_value_t*), %jl_value_t** %4, align 8
%27 = bitcast %jl_value_t*** %2 to i8*
%28 = call %jl_value_t* @jl_gc_pool_alloc(i8* %27, i32 1488, i32 16)
%29 = getelementptr inbounds %jl_value_t, %jl_value_t* %28, i64 -1, i32 0
store %jl_value_t* inttoptr (i64 2148805808 to %jl_value_t*), %jl_value_t** %29, align 8
%30 = bitcast %Normal* %0 to i64*
%31 = load i64, i64* %30, align 8
%32 = bitcast %jl_value_t* %28 to i64*
store i64 %31, i64* %32, align 8
store %jl_value_t* %28, %jl_value_t** %18, align 8
%33 = call %jl_value_t* @jl_gc_pool_alloc(i8* %27, i32 1488, i32 16)
%34 = getelementptr inbounds %jl_value_t, %jl_value_t* %33, i64 -1, i32 0
store %jl_value_t* inttoptr (i64 2148805808 to %jl_value_t*), %jl_value_t** %34, align 8
%35 = bitcast double* %22 to i64*
%36 = load i64, i64* %35, align 8
%37 = bitcast %jl_value_t* %33 to i64*
store i64 %36, i64* %37, align 8
store %jl_value_t* %33, %jl_value_t** %17, align 8
%38 = call %jl_value_t* @jl_apply_generic(%jl_value_t** %4, i32 4)
store %jl_value_t* %38, %jl_value_t** %14, align 8
store %jl_value_t* inttoptr (i64 2148851360 to %jl_value_t*), %jl_value_t** %5, align 8
store %jl_value_t* inttoptr (i64 2147425520 to %jl_value_t*), %jl_value_t** %15, align 8
%39 = call %jl_value_t* @jl_apply_generic(%jl_value_t** %5, i32 3)
%40 = load i64, i64* %13, align 8
store i64 %40, i64* %11, align 8
ret %jl_value_t* %39
}
```
Performance takes a big hit. Any thoughts?
Thanks, Gus
---
### Author:  [@vchuravy](https://discourse.julialang.org/u/vchuravy)
#### Post date: [December 7, 2016, 10:52pm UTC](https://discourse.julialang.org/t/inconsistent-llvm-code/794/2 "2016-12-07T22:52:26Z")
The LLVM IR is a bit to low level to easily see the problem. Take a look at `@code_warntype` to check the results of type inference.
`%3 = call double @julia_erfcinv_71923(double %2) #1`
vs
`%24 = call %jl_value_t* @julia_erfcinv_71923(double %23) #1`
Looks like the result of the erfc became type instable after importing Optim. Please format your posts and but code pieces in “`” so that it gets formatted properly
---
### Author:  [@yuyichao](https://discourse.julialang.org/u/yuyichao)
#### Post date: [December 7, 2016, 10:52pm UTC](https://discourse.julialang.org/t/inconsistent-llvm-code/794/3 "2016-12-07T22:52:30Z")
Likely a fixed bug. [https://github.com/JuliaLang/julia/issues/18465](https://github.com/JuliaLang/julia/issues/18465) You should also check `@code_warntype` first and quote your code.
---
### Author:  [@augustinyiptong](https://discourse.julialang.org/u/augustinyiptong)
#### Post date: [December 8, 2016, 1:26am UTC](https://discourse.julialang.org/t/inconsistent-llvm-code/794/4 "2016-12-08T01:26:51Z")
Here is the results of `@code_warntype`:
Without Optim:
```julia
Variables:
#self#::Base.#quantile
d::Distributions.Normal{Float64}
q::Float64
Body:
begin
# meta: location c:\program files\resolver_julia\packages\v0.5\StatsFuns\src\distrs\norm.jl norminvcdf 35
# meta: location c:\program files\resolver_julia\packages\v0.5\StatsFuns\src\distrs\norm.jl norminvcdf 34
SSAValue(0) = $(Expr(:invoke, LambdaInfo for erfcinv(::Float64), :(StatsFuns.erfcinv), :((Base.box)(Base.Float64,(Base.mul_float)((Base.box)(Float64,(Base.sitofp)(Float64,2)),q)))))
# meta: pop location
# meta: pop location
return (Base.box)(Base.Float64,(Base.add_float)((Core.getfield)(d::Distributions.Normal{Float64},:μ)::Float64,(Base.box)(Base.Float64,(Base.mul_float)((Core.getfield)(d::Distributions.Normal{Float64},:σ)::Float64,(Base.box)(Base.Float64,(Base.mul_float)((Base.box)(Base.Float64,(Base.neg_float)(SSAValue(0))),$(QuoteNode(1.4142135623730951))))))))
end::Float64
```
With Optim:
```julia
Variables:
#self#::Base.#quantile
d::Distributions.Normal{Float64}
q::Float64
Body:
begin
return (Distributions.convert)(Distributions.Real,(StatsFuns.xval)((Core.getfield)(d::Distributions.Normal{Float64},:μ)::Float64,(Core.getfield)(d::Distributions.Normal{Float64},:σ)::Float64,(-($(Expr(:invoke, LambdaInfo for erfcinv(::Float64), :(StatsFuns.erfcinv), :((Base.box)(Base.Float64,(Base.mul_float)((Base.box)(Float64,(Base.sitofp)(Float64,2)),q))))))::ANY * StatsFuns.sqrt2)::ANY)::ANY)::ANY
end::ANY
```
Thanks for your help
---
### Author:  [@vchuravy](https://discourse.julialang.org/u/vchuravy)
#### Post date: [December 8, 2016, 1:39am UTC](https://discourse.julialang.org/t/inconsistent-llvm-code/794/5 "2016-12-08T01:39:10Z")
Just for future info: As you can see with `@code_warntype` Julia inferred the second version to return `ANY` instead `Float64` indicating a problem.
Could you try to build Julia from [the `release-0.5` branch](https://github.com/JuliaLang/julia/commits/release-0.5)?
> [@yuyichao](#):
>
> Likely a fixed bug. [Break type stability of Rational with unrelated function (0.5-specific) · Issue #18465 · JuliaLang/julia · GitHub](https://github.com/JuliaLang/julia/issues/18465)
---
### Author:  [@ChrisRackauckas](https://discourse.julialang.org/u/ChrisRackauckas)
#### Post date: [December 8, 2016, 1:52am UTC](https://discourse.julialang.org/t/inconsistent-llvm-code/794/6 "2016-12-08T01:52:55Z")
This is known
>
>
> \`\`\` julia
> import Optim
> function test\_norm(R::Vector{Float64})
> \# r = Base….LinAlg.vecnorm2(R)
> r = norm(R)
> return r
> end
> R1 = rand(3);
> @code\_warntype test\_norm(R1)
> \`\`\`
>
> Output is
>
> \`\`\`
> Variables:
> #self#::#test\_norm
> R::Array{Float64,1}
> r::Any
>
> Body:
> begin
> r::Any = $(Expr(:invoke, LambdaInfo for vecnorm(::Array{Float64,1}, ::Int64), :(Base.LinAlg.vecnorm), :(R), 2)) # line 4:
> return r::Any
> end::Any
> \`\`\`
>
> Comments:
> \- With \`vecnorm2\` the type of \`r\` is inferred correctly.
> \- If I don't \`import Optim\` first, then the type is also inferred correctly.
> \- This happens on \`v0.5-rc4\`, but not on \`v0.4.6\`
> \- It happens on \`Optim\` \`master\` as well as the current release.
---
### Author:  [@augustinyiptong](https://discourse.julialang.org/u/augustinyiptong)
#### Post date: [December 8, 2016, 2:17am UTC](https://discourse.julialang.org/t/inconsistent-llvm-code/794/7 "2016-12-08T02:17:01Z")
I am not setup for doing a build unfortunately. But it looks like this has been / will be resolved in the next release. Thanks again