[Merged by Bors] - Fix for type-instability in findranges
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bors try |
yebai
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Jul 14, 2021
| @inline function findranges(f_ranges, f_idcs) | ||
| return mapreduce(i -> f_ranges[i], vcat, f_idcs; init=Int[]) | ||
| results = Int[] |
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Since the size of results array is known, maybe pre-allocate memory for results here to improve efficiency?
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It's not known though, since these are ranges that being concatenated, not just 1 Int per element in f_ranges. We could of course compute it by doing something like map(length, f_ranges), but can we always guarantee that f_idcs holds all the f_ranges? I wasn't sure about this, so preferred to leave it with size not specified.
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Jul 14, 2021
TuringLang/Turing.jl#1661 With this PR: ```julia julia> using Turing, DynamicPPL, ReverseDiff julia> Turing.setadbackend(:reversediff); julia> @model function demo(x, ::Type{TV} = Vector{Float64}) where {TV} m = TV(undef, length(x)) m ~ MvNormal(length(x), 1.0) x ~ MvNormal(m, 1.0) end; julia> m = demo(randn(2)); julia> spl = DynamicPPL.Sampler(NUTS(0.65)) Sampler{NUTS{Turing.Core.ReverseDiffAD{false}, (), AdvancedHMC.DiagEuclideanMetric}}(NUTS{Turing.Core.ReverseDiffAD{false}, (), AdvancedHMC.DiagEuclideanMetric}(-1, 0.65, 10, 1000.0, 0.0), DynamicPPL.Selector(0x000f976df8fc2a48, :default, false)) julia> vi = DynamicPPL.VarInfo(m); julia> θ = vi[spl]; julia> new_vi = VarInfo(vi, spl, ReverseDiff.track(θ)); julia> eltype(new_vi, spl) # (✓) inference succeeds ReverseDiff.TrackedReal{Float64, Float64, ReverseDiff.TrackedArray{Float64, Float64, 1, Vector{Float64}, Vector{Float64}}} ```
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TuringLang/Turing.jl#1661
With this PR: