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Use similar in creation of DiffResults buffer #95

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merged 5 commits into from
Nov 22, 2022

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torfjelde
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@torfjelde torfjelde commented Nov 17, 2022

Using similar means that we get automatic compat with several other packages, e.g. ComponentArrays.jl (the demo from the docs):

julia> x = ComponentVector= 0.0, σ = 1.0);

julia>= ADgradient(:ForwardDiff, problem)
ForwardDiff AD wrapper for NormalPosterior{Float64}(100, -0.06359079034463508, 1.241856096172367), w/ chunk size 2

julia> LogDensityProblems.logdensity_and_gradient(ADgradient(:ForwardDiff, problem), x)[2]
ComponentVector{Float64}= -6.359079034463508, σ = 25.339988478902235)

julia> ForwardDiff.gradient(Base.Fix1(LogDensityProblems.logdensity, problem), x) 
ComponentVector{Float64}= -6.359079034463508, σ = 25.339988478902235)

Ref: TuringLang/AdvancedHMC.jl#301

@@ -14,7 +14,7 @@ Allocate a DiffResults buffer for a gradient, taking the element type of `x` int
function _diffresults_buffer(ℓ, x)
T = eltype(x)
S = T <: Real ? float(Real) : Float64 # heuristic
DiffResults.MutableDiffResult(zero(S), (Vector{S}(undef, dimension(ℓ)), ))
DiffResults.MutableDiffResult(zero(S), (similar(x, S), ))
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Should this still take into account dimension? I'm not sure but I assume there was a reason why it was used instead of x?

Suggested change
DiffResults.MutableDiffResult(zero(S), (similar(x, S), ))
DiffResults.MutableDiffResult(zero(S), (similar(x, S, dimension(ℓ)), ))

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Currently all it uses from xis its element type, and it takes the dimension from the first argument.

I think it could be done differently, using the dimension from x, and then maybe would not need to be an argument at all.

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using the dimension from x

So similar basically does this. And AFAIK this is only called from the logdensity_and_gradient method, meaning that x should always have the correct dimensions.

Using dimension from instead will sometimes result in loss of structure of the original type for x, e.g. ComponentArrays.

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I would recommend something like

function _diffresults_buffer(x)
    T = eltype(x)
    S = T <: Real ? float(Real) : Float64 # heuristic
    DiffResults.MutableDiffResult(zero(S), (similar(x, S), ))
end

with the caller modified accordingly.

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This is what I've impleented, right? Or are my eyes deceiving me?

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I think @tpapp's point is that the first argument of the function should be dropped if we do not use it anymore.

@@ -25,5 +25,6 @@ constructed with [`diffresults_buffer`](@ref). Gradient is not copied as caller
vector.
"""
function _diffresults_extract(diffresult::DiffResults.DiffResult)
# NOTE: Is this still needed?
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That's not affected by the change above, is it?

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I also don't understand why it would not be needed.

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Nah, it was a note by me so we I could ask why it's there:) As in, can we just remove it?

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What would you use instead?

The purpose was to collect everything related to extraction in a single function. I am open to suggestions, but simply removing it would break the package (the function is used).

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Ah, I thought the ::Real was the reason for it, and was thinking maybe this is now redundant because type-inference is better / DiffResults might have fixed whatever issue required the ::Real in the first place. If that's not the case and it's just for convenience, then I'll remove the comment:)

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I no longer recall the case for ::Real, so I guess you can remove it. We can always put it back if things break and then document why it is there.

Please also remove the comment, and then I am happy to merge.

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Done:)

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torfjelde commented Nov 18, 2022 via email

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LGTM, just remove ::Real and the added comment.

@@ -25,5 +25,6 @@ constructed with [`diffresults_buffer`](@ref). Gradient is not copied as caller
vector.
"""
function _diffresults_extract(diffresult::DiffResults.DiffResult)
# NOTE: Is this still needed?
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I no longer recall the case for ::Real, so I guess you can remove it. We can always put it back if things break and then document why it is there.

Please also remove the comment, and then I am happy to merge.

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LGTM, thanks!

@tpapp tpapp merged commit d4f03be into tpapp:master Nov 22, 2022
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3 participants