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ensure sum(f,x) works on GPU #1004

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Jun 21, 2021
Merged

ensure sum(f,x) works on GPU #1004

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8f4354f
add tests to see if sum(f,x) is broken on GPU
oxinabox Jun 20, 2021
075f530
add back old way of doing sum(f, xs) for CuArrays only
oxinabox Jun 20, 2021
22602b3
Qualified names
oxinabox Jun 20, 2021
7776fdd
don't test gradient of abs at 0
oxinabox Jun 20, 2021
bd8c5fb
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oxinabox Jun 20, 2021
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2 changes: 1 addition & 1 deletion Project.toml
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
name = "Zygote"
uuid = "e88e6eb3-aa80-5325-afca-941959d7151f"
version = "0.6.13"
version = "0.6.14"

[deps]
AbstractFFTs = "621f4979-c628-5d54-868e-fcf4e3e8185c"
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9 changes: 8 additions & 1 deletion src/lib/broadcast.jl
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Expand Up @@ -254,7 +254,14 @@ end
placeholder = similar(xs)
sum(xs, dims = dims), Δ -> (placeholder .= Δ,)
end


# Make sure sum(f, ::CuArray) uses broadcase through forward-mode defined above
# Not the ChainRules.rrule which will use the Zygote.Context and thus not be GPU compatible
@adjoint function sum(f, xs::CUDA.CuArray; kws...)
@assert !haskey(kws, :init) # TODO add init support (julia 1.6)
return pullback(__context__, (f, xs) -> sum(f.(xs); kws...), f, xs)
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You probably don't need the context manager here

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@oxinabox oxinabox Jun 21, 2021 •
edited
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This is exactly the old code.
I don't really know what it is doing returning the context<

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I think keeping it is better.
It should end up hitting the same code either way, but I can imagine if some specialization are added then the errors we would get are more confusing if we don't include the __context__

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Might be slightly tidier to call broadcast_forward directly, instead of going back to pullback. But this is a quick fix to repair an accidental break so the most direct restoration is fine too.

end

@adjoint function Base.convert(::Type{T}, xs::Array) where {T<:CUDA.CuArray}
Base.convert(T, xs), Δ -> (nothing, Base.convert(Array, Δ),)
end
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11 changes: 11 additions & 0 deletions test/cuda.jl
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Expand Up @@ -26,6 +26,17 @@ end
@test g_gpu |> collect ≈ g
end

@testset "sum(f, x)" begin
a = Float32.([-1.5, -9.0, 2.4, -1.3, 0.01])
a_gpu = a |> cu

f(x) = sum(abs, x)
g = gradient(f, a)[1]
g_gpu = gradient(f, a_gpu)[1]
@test g_gpu isa CuArray
@test g_gpu |> collect ≈ g
end

@testset "jacobian" begin
v1 = cu(collect(1:3f0))

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