Avoid materializing I, simplify _eyelike with CUDA and make it differentiable#1254
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DhairyaLGandhi wants to merge 4 commits intomasterfrom
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Avoid materializing I, simplify _eyelike with CUDA and make it differentiable#1254DhairyaLGandhi wants to merge 4 commits intomasterfrom
I, simplify _eyelike with CUDA and make it differentiable#1254DhairyaLGandhi wants to merge 4 commits intomasterfrom
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ToucheSir
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Jun 26, 2022
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ToucheSir
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Do you have a link or other reference to what inspired this PR? The changes make sense to me, but given the only place _eyelike is called is not AD-safe in the first place, I'm confused as to the need for making only it differentiable.
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| @adjoint (::Type{T})(xs::Array) where {T <: CUDA.CuArray} = | ||
| _eyelike(y::CUDA.CuVector{T}) where T = CUDA.CuArray(I(length(y))) |
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| _eyelike(y::CUDA.CuVector{T}) where T = CUDA.CuArray(I(length(y))) | |
| _eyelike(y::CUDA.CuVector) = CUDA.CuArray(I(length(y))) |
To avoid the unbound type param.
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| @adjoint (::Type{T})(xs::Array) where {T <: CUDA.CuArray} = | ||
| _eyelike(y::CUDA.CuVector{T}) where T = CUDA.CuArray(I(length(y))) | ||
| @adjoint (::Type{T})(xs::AbstractArray) where {T <: CUDA.CuArray} = |
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IIUC the intent of the PR, would we not want a dedicated path UniformScaling? Taking <:AbstractArray presumes we can convert back to that subtype, but the implementation can only convert back to Array at present. Adapt.jl may be able to help us here.
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