ntuple for inference #168
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ntuple for inference #168
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On this example:
```julia
using DifferentialEquations, SnoopCompile
function lorenz(du,u,p,t)
du[1] = 10.0(u[2]-u[1])
du[2] = u[1]*(28.0-u[3]) - u[2]
du[3] = u[1]*u[2] - (8/3)*u[3]
end
u0 = [1.0;0.0;0.0]
tspan = (0.0,100.0)
prob = ODEProblem(lorenz,u0,tspan)
alg = Rodas5(chunk_size = Val{3}())
tinf = @snoopi_deep solve(prob,alg)
solve(prob,alg)
```
we saw just two inference triggers:
```julia
julia> itrigs = inference_triggers(tinf)
2-element Vector{InferenceTrigger}:
Inference triggered to call setindex!(::Vector{Tuple{Float64, Float64, Float64}}, ::Tuple{Bool, Bool, Bool}, ::Int64) from generate_chunked_partials (C:\Users\accou\.julia\dev\SparseDiffTools\src\differentiation\compute_jacobian_ad.jl:75) with specialization SparseDiffTools.generate_chunked_partials(::Vector{Float64}, ::UnitRange{Int64}, ::Val{3})
Inference triggered to call OrdinaryDiffEq.jacobian2W!(::Matrix{Float64}, ::LinearAlgebra.UniformScaling{Bool}, ::Float64, ::Matrix{Float64}, ::Bool) called from toplevel
```
The second one is clear: okay, just use ntuple on like 75 right? So I did that, and it made the function more type-unstable? Look at the itrigs after that:
```julia
julia> itrigs = inference_triggers(tinf)
6-element Vector{InferenceTrigger}:
Inference triggered to call hcat(::BitMatrix, ::BitMatrix) from generate_chunked_partials (C:\Users\accou\.julia\dev\SparseDiffTools\src\differentiation\compute_jacobian_ad.jl:67) with specialization SparseDiffTools.generate_chunked_partials(::Vector{Float64}, ::UnitRange{Int64}, ::Val{3})
Inference triggered to call Vector{Tuple{Float64, Float64, Float64}}(::UndefInitializer, ::Int64) from generate_chunked_partials (C:\Users\accou\.julia\dev\SparseDiffTools\src\differentiation\compute_jacobian_ad.jl:73) with specialization SparseDiffTools.generate_chunked_partials(::Vector{Float64}, ::UnitRange{Int64}, ::Val{3})
Inference triggered to call ntuple(::SparseDiffTools.var"#15#16"{3, Int64, Int64}, ::Val{3}) from generate_chunked_partials (C:\Users\accou\.julia\dev\SparseDiffTools\src\differentiation\compute_jacobian_ad.jl:75) with specialization SparseDiffTools.generate_chunked_partials(::Vector{Float64}, ::UnitRange{Int64}, ::Val{3})
Inference triggered to call setindex!(::Vector{Tuple{Float64, Float64, Float64}}, ::Tuple{Bool, Bool, Bool}, ::Int64) from generate_chunked_partials (C:\Users\accou\.julia\dev\SparseDiffTools\src\differentiation\compute_jacobian_ad.jl:75) with specialization SparseDiffTools.generate_chunked_partials(::Vector{Float64}, ::UnitRange{Int64}, ::Val{3})
Inference triggered to call setindex!(::Vector{Vector{Tuple{Float64, Float64, Float64}}}, ::Vector{Tuple{Float64, Float64, Float64}}, ::Int64) from generate_chunked_partials (C:\Users\accou\.julia\dev\SparseDiffTools\src\differentiation\compute_jacobian_ad.jl:77) with specialization SparseDiffTools.generate_chunked_partials(::Vector{Float64}, ::UnitRange{Int64}, ::Val{3})
Inference triggered to call OrdinaryDiffEq.jacobian2W!(::Matrix{Float64}, ::LinearAlgebra.UniformScaling{Bool}, ::Float64, ::Matrix{Float64}, ::Bool) called from toplevel
```
@chriselrod or @YingboMa could I get some Cthulhu magic to look at what's going on there?
This was referenced Dec 8, 2021
YingboMa
reviewed
Dec 8, 2021
Co-authored-by: Yingbo Ma <[email protected]>
Codecov Report
@@ Coverage Diff @@
## master #168 +/- ##
==========================================
+ Coverage 78.86% 78.89% +0.02%
==========================================
Files 14 14
Lines 743 744 +1
==========================================
+ Hits 586 587 +1
Misses 157 157
Continue to review full report at Codecov.
|
|
Nope, @YingboMa that also causes: julia> itrigs = inference_triggers(tinf)
6-element Vector{InferenceTrigger}:
Inference triggered to call hcat(::BitMatrix, ::BitMatrix) from generate_chunked_partials (C:\Users\accou\.julia\dev\SparseDiffTools\src\differentiation\compute_jacobian_ad.jl:67) with specialization SparseDiffTools.generate_chunked_partials(::Vector{Float64}, ::UnitRange{Int64}, ::Val{3})
Inference triggered to call Vector{Tuple{Float64, Float64, Float64}}(::UndefInitializer, ::Int64) from generate_chunked_partials (C:\Users\accou\.julia\dev\SparseDiffTools\src\differentiation\compute_jacobian_ad.jl:73) with specialization SparseDiffTools.generate_chunked_partials(::Vector{Float64}, ::UnitRange{Int64}, ::Val{3})
Inference triggered to call ntuple(::SparseDiffTools.var"#15#16"{3, Int64, Int64}, ::Val{3}) from generate_chunked_partials (C:\Users\accou\.julia\dev\SparseDiffTools\src\differentiation\compute_jacobian_ad.jl:75) with specialization SparseDiffTools.generate_chunked_partials(::Vector{Float64}, ::UnitRange{Int64}, ::Val{3})
Inference triggered to call setindex!(::Vector{Tuple{Float64, Float64, Float64}}, ::Tuple{Bool, Bool, Bool}, ::Int64) from generate_chunked_partials (C:\Users\accou\.julia\dev\SparseDiffTools\src\differentiation\compute_jacobian_ad.jl:75) with specialization SparseDiffTools.generate_chunked_partials(::Vector{Float64}, ::UnitRange{Int64}, ::Val{3})
Inference triggered to call setindex!(::Vector{Vector{Tuple{Float64, Float64, Float64}}}, ::Vector{Tuple{Float64, Float64, Float64}}, ::Int64) from generate_chunked_partials (C:\Users\accou\.julia\dev\SparseDiffTools\src\differentiation\compute_jacobian_ad.jl:77) with specialization SparseDiffTools.generate_chunked_partials(::Vector{Float64}, ::UnitRange{Int64}, ::Val{3})
Inference triggered to call OrdinaryDiffEq.jacobian2W!(::Matrix{Float64}, ::UniformScaling{Bool}, ::Float64, ::Matrix{Float64}, ::Bool) called from toplevel |
|
I'll try to play with this locally and submit a PR. |
chriselrod
approved these changes
Dec 8, 2021
|
Actually, this seems better: https://github.com/JuliaDiff/SparseDiffTools.jl/pull/169/files |
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On this example:
we saw just two inference triggers:
The second one is clear: okay, just use ntuple on like 75 right? So I did that, and it made the function more type-unstable? Look at the itrigs after that:
@chriselrod or @YingboMa could I get some Cthulhu magic to look at what's going on there?