# Oddities with loop with dummy variable in Tullio

**URL:** https://discourse.julialang.org/t/oddities-with-loop-with-dummy-variable-in-tullio/91484
**Category:** General Usage
**Tags:** question, bug, tullio
**Created:** [December 9, 2022, 5:23pm UTC](https://discourse.julialang.org/t/oddities-with-loop-with-dummy-variable-in-tullio/91484 "2022-12-09T17:23:18Z")
**Posts on this page:** 3
**Page:** 1

<div class="post-metadata">

### Author: ![Andrea\_Pagnani](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/andrea_pagnani/32/3136_2.png) [@Andrea\_Pagnani](https://discourse.julialang.org/u/Andrea_Pagnani)
#### Post date: [December 9, 2022, 5:23pm UTC](https://discourse.julialang.org/t/oddities-with-loop-with-dummy-variable-in-tullio/91484/1 "2022-12-09T17:23:18Z")

</div>

Hi there

I spotred possibly a strange bug using Tullio on CPU. Here is my package status (Julia-1.8.2)

```julia
[bdcacae8] LoopVectorization v0.12.141
[bc48ee85] Tullio v0.3.5

```

And my minimal non-wroking example

```julia
using LoopVectorization, Tullio
function mytest_tullio(wdelta,probnew)
    q = size(wdelta,3)
    @tullio mat[a,b,j] := wdelta[j,m,a]*probnew[m,a] (a in 1:q, b in 1:q)
end

```

to generate the error (at least on a linux machine see below for more info)

```julia
julia> a = rand(2,3,4); b= rand(3,4); mytest_tullio(a,b)

```

here below the output

> **raised error**
>
> ```julia
> ERROR: MethodError: no method matching reduced_add(::Float64, ::VectorizationBase.Vec{8, Float64})
> Closest candidates are:
> reduced_add(::Union{Bool, Float16, Float32, Float64, Int16, Int32, Int64, Int8, UInt16, UInt32, UInt64, UInt8, SIMDTypes.Bit}, ::Union{Bool, Float16, Float32, Float64, Int16, Int32, Int64, Int8, UInt16, UInt32, UInt64, UInt8, SIMDTypes.Bit}) at ~/.julia/packages/VectorizationBase/e4FnQ/src/llvm_intrin/intrin_funcs.jl:690
> reduced_add(::VectorizationBase.AbstractSIMD{W}, ::VectorizationBase.AbstractSIMD{W}) where W at ~/.julia/packages/VectorizationBase/e4FnQ/src/llvm_intrin/intrin_funcs.jl:691
> reduced_add(::VectorizationBase.AbstractSIMD, ::VectorizationBase.AbstractSIMD) at ~/.julia/packages/VectorizationBase/e4FnQ/src/llvm_intrin/intrin_funcs.jl:692
> Stacktrace:
> [1] macro expansion
> @ ~/.julia/packages/LoopVectorization/DDH6Z/src/reconstruct_loopset.jl:965 [inlined]
> [2] _turbo_!(::Val{(false, 0, 0, 0, false, 8, 64, 32, 64, 0x0000000000000001, 1, true)}, ::Val{(Symbol("##DROPPED#CONSTANT##"), Symbol("##DROPPED#CONSTANT##"), LoopVectorization.OperationStruct(0x00000000000000000000000000000000, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, LoopVectorization.constant, 0x0001, 0x00), Symbol("##GLOBAL##CONSTANT##"), :nothing, LoopVectorization.OperationStruct(0x00000000000000000000000000000000, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, LoopVectorization.constant, 0x0002, 0x00), Symbol("##DROPPED#CONSTANT##"), Symbol("##DROPPED#CONSTANT##"), LoopVectorization.OperationStruct(0x00000000000000000000000000000000, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, LoopVectorization.constant, 0x0003, 0x00), :numericconstant, Symbol("###zero###18###"), LoopVectorization.OperationStruct(0x00000000000000000000000000000123, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, LoopVectorization.constant, 0x0004, 0x00), :LoopVectorization, :conditionalload, LoopVectorization.OperationStruct(0x00000000000000000000000000000321, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, 0x00000000000000000000000000000006, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, LoopVectorization.memload, 0x0005, 0x01), :LoopVectorization, :LOOPCONSTANTINSTRUCTION, LoopVectorization.OperationStruct(0x00000000000000000000000000000000, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, LoopVectorization.constant, 0x0006, 0x00), :LoopVectorization, :getindex, LoopVectorization.OperationStruct(0x00000000000000000000000000000143, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, LoopVectorization.memload, 0x0007, 0x02), :LoopVectorization, :getindex, LoopVectorization.OperationStruct(0x00000000000000000000000000000043, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, LoopVectorization.memload, 0x0008, 0x03), :numericconstant, Symbol("###reduction##zero###29###"), LoopVectorization.OperationStruct(0x00000000000000000000000000000321, 0x00000000000000000000000000000000, 0x00000000000000000000000000000004, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, LoopVectorization.constant, 0x0009, 0x00), :LoopVectorization, :vfmadd_fast, LoopVectorization.OperationStruct(0x00000000000000000000000000000143, 0x00000000000000000000000000000004, 0x00000000000000000000000000000000, 0x00000000000000000000000700080009, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, LoopVectorization.compute, 0x0009, 0x00), :LoopVectorization, :reduced_add, LoopVectorization.OperationStruct(0x00000000000000000000000000000321, 0x00000000000000000000000000000004, 0x00000000000000000000000000000000, 0x000000000000000000000000000a0005, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, LoopVectorization.compute, 0x0005, 0x00), :LoopVectorization, :setindex!, LoopVectorization.OperationStruct(0x00000000000000000000000000000321, 0x00000000000000000000000000000004, 0x00000000000000000000000000000000, 0x0000000000000000000000000000000b, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, 0x00000000000000000000000000000000, LoopVectorization.memstore, 0x000a, 0x01))}, ::Val{(LoopVectorization.ArrayRefStruct{:ℛ, Symbol("##vptr##_ℛ")}(0x00000000000000000000000000010101, 0x00000000000000000000000000030201, 0x00000000000000000000000000000000, 0x00000000000000000000000000010101), LoopVectorization.ArrayRefStruct{:wdelta, Symbol("##vptr##_wdelta")}(0x00000000000000000000000000010101, 0x00000000000000000000000000010403, 0x00000000000000000000000000000000, 0x00000000000000000000000000010101), LoopVectorization.ArrayRefStruct{:probnew, Symbol("##vptr##_probnew")}(0x00000000000000000000000000000101, 0x00000000000000000000000000000403, 0x00000000000000000000000000000000, 0x00000000000000000000000000000101))}, ::Val{(0, (), (1, 3, 6), (), (), ((4, LoopVectorization.IntOrFloat), (9, LoopVectorization.IntOrFloat)), ())}, ::Val{(:j, :b, :a, :m)}, ::Val{Tuple{NTuple{4, Static.OptionallyStaticUnitRange{Static.StaticInt{0}, Int64}}, Tuple{LayoutPointers.GroupedStridedPointers{Tuple{Ptr{Float64}, Ptr{Float64}, Ptr{Float64}}, (1, 1, 1), (0, 0, 0), ((1, 2, 3), (1, 2, 3), (1, 2)), ((1, 2, 3), (4, 5, 6), (7, 8)), Tuple{Static.StaticInt{8}, Int64, Int64, Static.StaticInt{8}, Int64, Int64, Static.StaticInt{8}, Int64}, NTuple{8, Static.StaticInt{0}}}, Bool}}}, ::Int64, ::Int64, ::Int64, ::Int64, ::Ptr{Float64}, ::Ptr{Float64}, ::Ptr{Float64}, ::Int64, ::Int64, ::Int64, ::Int64, ::Int64, ::Bool)
> @ LoopVectorization ~/.julia/packages/LoopVectorization/DDH6Z/src/reconstruct_loopset.jl:965
> [3] 𝒜𝒸𝓉!
> @ ~/.julia/packages/Tullio/NGyNM/src/macro.jl:1093 [inlined]
> [4] tile_halves(fun!::AttentionBasedPlmDCA.var"#𝒜𝒸𝓉!#814", ::Type{Array{Float64}}, As::Tuple{OffsetArrays.OffsetArray{Float64, 3, Array{Float64, 3}}, Array{Float64, 3}, Matrix{Float64}, UnitRange{Int64}}, Is::Tuple{UnitRange{Int64}, UnitRange{Int64}, UnitRange{Int64}}, Js::Tuple{UnitRange{Int64}}, keep::Nothing, final::Bool)
> @ Tullio ~/.julia/packages/Tullio/NGyNM/src/threads.jl:139
> [5] tile_halves
> @ ~/.julia/packages/Tullio/NGyNM/src/threads.jl:136 [inlined]
> [6] threader
> @ ~/.julia/packages/Tullio/NGyNM/src/threads.jl:65 [inlined]
> [7] (::AttentionBasedPlmDCA.var"#ℳ𝒶𝓀ℯ#817"{AttentionBasedPlmDCA.var"#𝒜𝒸𝓉!#814"})(wdelta::Array{Float64, 3}, probnew::Matrix{Float64}, ≪1:q≫::UnitRange{Int64})
> @ AttentionBasedPlmDCA ~/CODE/AttentionBasedPlmDCA.jl/src/wipattention.jl:140
> [8] (::Tullio.Eval{AttentionBasedPlmDCA.var"#ℳ𝒶𝓀ℯ#817"{AttentionBasedPlmDCA.var"#𝒜𝒸𝓉!#814"}, AttentionBasedPlmDCA.var"#8942#∇ℳ𝒶𝓀ℯ#816"{AttentionBasedPlmDCA.var"#∇𝒜𝒸𝓉!#815"}})(::Array{Float64, 3}, ::Vararg{Any})
> @ Tullio ~/.julia/packages/Tullio/NGyNM/src/eval.jl:20
> [9] mytest_tullio(wdelta::Array{Float64, 3}, probnew::Matrix{Float64})
> @ AttentionBasedPlmDCA ~/CODE/AttentionBasedPlmDCA.jl/src/wipattention.jl:303
> [10] top-level scope
> @ REPL[17]:1
> 
> ```

On my mac it does not error but produces a strange

```julia
julia> mytest_tullio(a,b) # on mac os x
4×4×2 OffsetArray(::Array{Float64, 3}, 1:4, 1:4, 1:2) with eltype Float64 with indices 1:4×1:4×1:2:
[:, :, 1] =
 0.718021 0.718021 0.718021 0.718021
 0.747013 0.747013 0.747013 0.747013
 0.596645 0.596645 0.596645 0.596645
 0.852708 0.852708 0.852708 0.852708

[:, :, 2] =
 0.33938 0.33938 0.33938 0.33938
 0.93867 0.93867 0.93867 0.93867
 0.341837 0.341837 0.341837 0.341837
 0.26481 0.26481 0.26481 0.26481

```

Which is an offset array (???) rather than a `4x4x2 Array{Float64,3}`.

This is just a minimal nonworking example; I could do it in another way. But I find it odd:

1. The error on linux
2. The strange offset array output on mac

Before bothering the Tullio devs, maybe some of you see some evident mistakes on my side.

Thanks

A

---

<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: [December 9, 2022, 5:57pm UTC](https://discourse.julialang.org/t/oddities-with-loop-with-dummy-variable-in-tullio/91484/2 "2022-12-09T17:57:59Z")

</div>

With Tullio alone, ideally it should notice that `b in 1:q` has a literal `1`, and hence can be made `OneTo(q)`. Failing that, it ought to notice that OffsetArrays is not loaded, and hence it can’t make an array with UnitRange axis, so it should insert something like `first(1:b)==1 ? OneTo(b) : error("you really need OffsetArrays")`, but it also seems to miss that check.

I think that’s a bug. The work-around is to define `b_range = axes(wdelta,3)` and write `b in b_range`.

```julia
julia> function mytest_tullio(wdelta,probnew)
           q = size(wdelta,3)
           @tullio mat[a,b,j] := wdelta[j,m,a] * probnew[m,a] (b in 1:q) verbose=true
       end
...

julia> a = rand(2,3,4); b= rand(3,4); mytest_tullio(a,b)
┌ Info: left index ranges
│ a = Base.OneTo(4)
│ b = 1:4
└ j = Base.OneTo(2)
┌ Info: reduction index ranges
└ m = Base.OneTo(3)
ERROR: MethodError: no method matching similar(::Array{Float64, 3}, ::Type{Float64}, ::Tuple{Base.OneTo{Int64}, UnitRange{Int64}, Base.OneTo{Int64}})

```

The error with LoopVectorization is separate. It ought to be possible to reproduce this without Tullio by writing out the loops; calling the macro with `verbose=2` will print everything including the generated loops.

---

<div class="post-metadata">

### Author: ![Andrea\_Pagnani](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/andrea_pagnani/32/3136_2.png) [@Andrea\_Pagnani](https://discourse.julialang.org/u/Andrea_Pagnani)
#### Post date: [December 9, 2022, 6:10pm UTC](https://discourse.julialang.org/t/oddities-with-loop-with-dummy-variable-in-tullio/91484/3 "2022-12-09T18:10:05Z")

</div>

Thanks @mcabbott (I did not want to bother the devs … but here it goes 😉 )

Just to understand:

The fact that it errors on Linux (and not on Mac) is (possibly) due to LoopVectorization.

The fact it produces an OffsetArray is just a “corner-case” not spotted by Tullio, right?

Anyway thanks a lot for your answer and most of all for the beautiful Tullio package!!!

A