# Vectorize function for two same size CuArray

**URL:** <https://discourse.julialang.org/t/vectorize-function-for-two-same-size-cuarray/100983>\
**Category:** GPU\
**Tags:** question, cuda, flux\
**Created:** [June 29, 2023, 3:50pm UTC](https://discourse.julialang.org/t/vectorize-function-for-two-same-size-cuarray/100983 "2023-06-29T15:50:20Z")\
**Posts on this page:** 3\
**Page:** 1

<div class="post-metadata">

**Author:** ![kuanchiun](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/kuanchiun/32/51145_2.png) [@kuanchiun](https://discourse.julialang.org/u/kuanchiun)\
**Post date:** [June 29, 2023, 3:50pm UTC](https://discourse.julialang.org/t/vectorize-function-for-two-same-size-cuarray/100983/1 "2023-06-29T15:50:20Z")

</div>

Hello! I’m rookie for Julia programming language.

I’m trying to create the loss function, which calculate cosine similarity for each row pair in x, x̂.  
The simply code is in below.

```julia
using Flux, CUDA, LinearAlgebra
CUDA.allowscalar(false)

function get_model(device)
    # Some process to create model

    return model |> device
end

function cosine_similarity(x, y)
    return dot(x, y) / (norm(x) * norm(y))
end

function predict(x, model)
    return model(x)

function eval_loss(x, model)
    x̂ = predict(x)

    cos_sim = cosine_similarity.(eachrow(x), eachrow(x̂))
    cos_loss = sum(1 .- maximum(cos_sim, dims = 1))

    return cos_loss
end

function test(x, device)
    model = get_model(device)
    x = x |> device
    
    ps = Flux.params(model)

    return Flux.gradient(() -> eval_loss(x, model), ps)
end

test(x, cpu)
test(x, gpu)

```

Basically, it worked perfectly on cpu, but when I changed device to gpu, it appeared the CUDA scalar indexing error.

> Scalar indexing is disallowed.  
> Invocation of getindex resulted in scalar indexing of a GPU array.  
> This is typically caused by calling an iterating implementation of a method.  
> Such implementations _do not_ execute on the GPU, but very slowly on the CPU,  
> and therefore are only permitted from the REPL for prototyping purposes.  
> If you did intend to index this array, annotate the caller with @allowscalar.

After debugging, I realized that the problem is because of the vectorize function.

> cos\_sim = cosine\_similarity.(eachrow(x), eachrow(x̂))

I have no idea how to solve it, I had tried `map`, `broadcast`, but the problem is still existed, is there any method to solve this problem?

Many thanks!

===============================  
Edited:

Split x and x̂ from matrix to vector, and rewrite cosine similarity solved my problem. Thanks a lot!

---

<div class="post-metadata">

**Author:** ![devel-chm](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/devel-chm/32/3572_2.png) [@devel-chm](https://discourse.julialang.org/u/devel-chm)\
**Post date:** [June 29, 2023, 3:55pm UTC](https://discourse.julialang.org/t/vectorize-function-for-two-same-size-cuarray/100983/2 "2023-06-29T15:55:40Z")

</div>

Maybe start with [documentation or training/tutorial](https://juliagpu.org/learn/)?

---

<div class="post-metadata">

**Author:** ![maleadt](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/maleadt/32/10097_2.png) [@maleadt](https://discourse.julialang.org/u/maleadt)\
**Post date:** [June 30, 2023, 6:06am UTC](https://discourse.julialang.org/t/vectorize-function-for-two-same-size-cuarray/100983/3 "2023-06-30T06:06:36Z")

</div>

`eachrow` and similar do not work with GPU implementations of array operations (like `broadcast`).