# Zygote backward pass creates \`ReshapedArray{Adjoint{CuArray}}\` which cannot be materialized on GPU

**URL:** https://discourse.julialang.org/t/zygote-backward-pass-creates-reshapedarray-adjoint-cuarray-which-cannot-be-materialized-on-gpu/136450
**Category:** Specific Domains
**Tags:** question
**Created:** [March 29, 2026, 4:55pm UTC](https://discourse.julialang.org/t/zygote-backward-pass-creates-reshapedarray-adjoint-cuarray-which-cannot-be-materialized-on-gpu/136450 "2026-03-29T16:55:48Z")
**Posts on this page:** 1
**Page:** 1

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### Author: ![anscoil](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/anscoil/32/7561_2.png) [@anscoil](https://discourse.julialang.org/u/anscoil)
#### Post date: [March 29, 2026, 4:55pm UTC](https://discourse.julialang.org/t/zygote-backward-pass-creates-reshapedarray-adjoint-cuarray-which-cannot-be-materialized-on-gpu/136450/1 "2026-03-29T16:55:48Z")

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Hi,

I noticed a problem with Zygote and CUDA when optimizing a loss function containing adjoints of reshaped arrays.

### Preliminary observation

First, note that creating a reshaped array of a `CuArray` adjoint fails when scalar indexing is disabled:

```julia
using CUDA
CUDA.allowscalar(false)
A = cu(randn(Float32, 5, 2, 5))
reshape(A', 10, 5)

```

```julia-auto
ERROR: Scalar indexing is disallowed.
Invocation of getindex resulted in scalar indexing of a GPU array.

```

So `Base.ReshapedArray{T, N, Adjoint{T, CuArray}}` is a type that Julia can construct but cannot materialize on GPU — even `copy` raises the scalar indexing error.

### The problem with Zygote

Zygote silently creates this type in the backward pass. Here is a minimal example:

```julia
using CUDA, Zygote
CUDA.allowscalar(false)

A = cu(randn(Float32, 5, 2, 5))
B = cu(randn(Float32, 5, 2, 3))

f(A, B) = sum(real(reshape(A, 10, 5)' * reshape(B, 10, 3)))
Zygote.gradient(f, A, B) # scalar indexing error

```

Zygote composes `reshape` and `adjoint` lazily in the backward pass, producing a `Base.ReshapedArray{T, N, Adjoint{T, CuArray}}` tangent that cannot be used on GPU.

### Workaround

In my code, I capture this type in a custom `ChainRulesCore` `projectTo` and materialize it with:

```julia
function materialize(x::Base.ReshapedArray{T, N, <:Adjoint{T, <:AbstractArray}}) where {T, N}
    adj_materialized = copy(parent(x))
    reshape(adj_materialized, size(x))
end

materialize(x) = x

```

The key insight is that `copy(parent(x))` materializes the `Adjoint` first (CUDA knows how to copy an `Adjoint{CuArray}`), and then `reshape` can be applied safely.

### Question

Is this a known limitation of CUDA.jl with composed array wrappers, or something that could be fixed?

Thanks,  
Nicolas