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Support conv_direct! on custom datatypes #592

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Jun 19, 2024
Merged

Support conv_direct! on custom datatypes #592

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3336842
Add test for unusual input datatypes
adrhill Jun 17, 2024
0e318fd
Add fix to `conv_direct!`
adrhill Jun 17, 2024
68e15a9
Only set y to zero if beta is false or zero
adrhill Jun 17, 2024
8f87596
Test output eltype
adrhill Jun 17, 2024
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5 changes: 5 additions & 0 deletions src/impl/conv_direct.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -81,6 +81,11 @@ function conv_direct!(
# Use `calc_padding_regions` to determine where we do or don't need to worry about padding
padded_regions, central_region = calc_padding_regions(cdims)

# Set outputs to zero to support custom datatypes (https://github.com/FluxML/NNlib.jl/issues/490)
if iszero(beta)
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y = fill!(y, zero(yT))
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As I mentioned in the original issue, naive fill!/fill doesn't necessarily work with e.g. BigInt:

julia> a = Vector{BigInt}(undef, 2)
2-element Vector{BigInt}:
 #undef
 #undef

julia> fill!(a, zero(BigInt))
2-element Vector{BigInt}:
 0
 0

julia> a[1] ===a[2]
true

I don't know how important these kinds of usecases are for NNlib.jl though.

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Is that problematic here?

julia> a = Vector{BigInt}(undef, 2)
2-element Vector{BigInt}:
 #undef
 #undef

julia> fill!(a, zero(BigInt))
2-element Vector{BigInt}:
 0
 0

julia> for (i, ai) in enumerate(a)
         a[i] = i + 0 * a[i]
       end

julia> a
2-element Vector{BigInt}:
 1
 2

end

# Start with the central region
w_region, h_region, d_region = central_region
@inbounds for batch in 1:size(x, 5),
Expand Down
39 changes: 39 additions & 0 deletions test/conv.jl
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Expand Up @@ -2,6 +2,7 @@ using NNlib, Test
using NNlib: input_size, kernel_size, channels_in, channels_out, channel_multiplier,
stride, padding, dilation, flipkernel, output_size,
groupcount
using Random: AbstractRNG, SamplerType

@testset "ConvDims" begin
for T in (DenseConvDims, DepthwiseConvDims)
Expand Down Expand Up @@ -865,6 +866,44 @@ end
@test size(NNlib.∇conv_filter_direct!(w, x, y, cdims)) == w_size
end

# https://github.com/FluxML/NNlib.jl/issues/490
# https://github.com/FluxML/NNlib.jl/issues/405
@testset "conv_direct! - Unusual input types" begin
# Create test type that can't be indexed when undefined.
# This simulates the worst-case scenario for custom types.
struct MyFloat <: Real
set::Set{Float32}
end

# Test that direct indexing fails when undefined.
v = Array{MyFloat}(undef, 3)
@test_throws UndefRefError v[1]

# Define minimal set of functions required for conv_direct!
MyFloat(x::MyFloat) = x
MyFloat(x::Real) = MyFloat(Set(Float32(x)))

Base.:+(x::MyFloat, y::MyFloat) = MyFloat(only(x.set) + only(y.set))
Base.:*(x::MyFloat, y::MyFloat) = MyFloat(only(x.set) * only(y.set))
Base.promote_rule(::Type{MyFloat}, ::Type{Float32}) = MyFloat
Base.rand(::AbstractRNG, ::SamplerType{MyFloat}) = MyFloat(rand(Float32))
Base.zero(::MyFloat) = MyFloat(zero(Float32))
Base.zero(::Type{MyFloat}) = MyFloat(zero(Float32))

# Test conv_direct!
x_size = (6, 7, 8, 5, 3)
y_size = (5, 6, 7, 4, 3)
w_size = (2, 2, 2, 5, 4)
x = rand(MyFloat, x_size);
w = randn(Float32, w_size);
y = Array{MyFloat}(undef, y_size...);
cdims = DenseConvDims(x_size, w_size)
y_out = NNlib.conv_direct!(y, x, w, cdims)

@test eltype(y_out) == MyFloat
@test size(y_out) == y_size
end

@testset "AutoDiff: spatial_rank=$spatial_rank" for spatial_rank in (1, 2, 3)
x = rand(rng, repeat([5], spatial_rank)..., 3, 2)
w = rand(rng, repeat([3], spatial_rank)..., 3, 3)
Expand Down
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