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Use lazy broadcasting for Statistics.var #443

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Merged
merged 2 commits into from
Jan 18, 2023
Merged

Use lazy broadcasting for Statistics.var #443

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e457a00
use lazy broadcasting for var
mcabbott Jan 4, 2023
077f26a
allocate once for mean(x; dims)
mcabbott Jan 4, 2023
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25 changes: 20 additions & 5 deletions src/host/statistics.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -2,13 +2,15 @@ using Statistics

function Statistics.varm(A::AbstractGPUArray{<:Real}, M::AbstractArray{<:Real};
dims, corrected::Bool=true)
T = float(eltype(A))
λ = convert(T, inv(_mean_denom(A, dims) - corrected))
#B = (A .- M).^2
# NOTE: the above broadcast promotes to Float64 and uses power_by_squaring...
B = broadcast(A, M) do a, m
B = Broadcast.broadcasted(A, M) do a, m
x = (a - m)
x*x
λ * x * x
end
sum(B, dims=dims)/(prod(size(A)[[dims...]])::Int-corrected)
sum(Broadcast.instantiate(B); dims)
end

Statistics.stdm(A::AbstractGPUArray{<:Real},m::AbstractArray{<:Real}, dim::Int; corrected::Bool=true) =
Expand All @@ -23,8 +25,17 @@ Statistics._std(A::AbstractGPUArray, corrected::Bool, mean, ::Colon) =
# Revert https://github.com/JuliaLang/Statistics.jl/pull/25
Statistics._mean(A::AbstractGPUArray, ::Colon) = sum(A) / length(A)
Statistics._mean(f, A::AbstractGPUArray, ::Colon) = sum(f, A) / length(A)
Statistics._mean(A::AbstractGPUArray, dims) = mean!(Base.reducedim_init(t -> t/2, +, A, dims), A)
Statistics._mean(f, A::AbstractGPUArray, dims) = sum(f, A, dims=dims) / mapreduce(i -> size(A, i), *, unique(dims); init=1)

function Statistics._mean(A::AbstractGPUArray, dims)
T = float(eltype(A))
λ = convert(T, inv(_mean_denom(A, dims)))
sum(Base.Fix1(*,λ), A; dims)
end
function Statistics._mean(f, A::AbstractGPUArray, dims)
T = float(eltype(A))
λ = convert(T, inv(_mean_denom(A, dims)))
sum(Base.Fix1(*,λ) ∘ f, A; dims)
end

function Statistics.covzm(x::AbstractGPUMatrix, vardim::Int=1; corrected::Bool=true)
C = Statistics.unscaled_covzm(x, vardim)
Expand All @@ -49,3 +60,7 @@ function Statistics.corzm(x::AbstractGPUMatrix, vardim::Int=1)
c = Statistics.unscaled_covzm(x, vardim)
return Statistics.cov2cor!(c, sqrt.(diag(c)))
end

_mean_denom(x::AbstractArray, dims::Integer) = size(x, dims)
_mean_denom(x::AbstractArray, dims::Colon) = length(x)
_mean_denom(x::AbstractArray, dims) = prod(size(x,d) for d in unique(dims); init=1)