From 57ac5dc6c9eb7815101797bc80ab0d61e223cf74 Mon Sep 17 00:00:00 2001 From: jeremie Date: Thu, 26 Oct 2023 00:43:37 -0400 Subject: [PATCH] MLJ test GPU launcher fixes --- ext/EvoTreesCUDAExt/fit-utils.jl | 3 ++- ext/EvoTreesCUDAExt/predict.jl | 6 ++++-- figures/gaussian-sinus-binary.png | Bin 84095 -> 84224 bytes figures/logistic-sinus-binary.png | Bin 83100 -> 82906 bytes figures/quantiles-sinus-binary.png | Bin 81600 -> 81043 bytes figures/regression-sinus-binary-gpu.png | Bin 79422 -> 79260 bytes figures/regression-sinus-binary.png | Bin 80209 -> 78041 bytes figures/regression-sinus-oblivious-gpu.png | Bin 80390 -> 80720 bytes figures/regression-sinus-oblivious.png | Bin 80659 -> 79067 bytes figures/regression-sinus2-binary.png | Bin 78144 -> 77967 bytes figures/regression-sinus2-oblivious.png | Bin 78809 -> 78492 bytes src/MLJ.jl | 4 ++-- src/predict.jl | 3 ++- test/MLJ.jl | 10 ++++++++++ test/runtests.jl | 1 + 15 files changed, 21 insertions(+), 6 deletions(-) diff --git a/ext/EvoTreesCUDAExt/fit-utils.jl b/ext/EvoTreesCUDAExt/fit-utils.jl index 5dd6ae86..02de1b1c 100644 --- a/ext/EvoTreesCUDAExt/fit-utils.jl +++ b/ext/EvoTreesCUDAExt/fit-utils.jl @@ -30,8 +30,9 @@ function update_hist_gpu!(h, h∇_cpu, h∇, ∇, x_bin, is, js, jsc) max_blocks = config.blocks k = size(h∇, 1) ty = max(1, min(length(js), fld(max_threads, k))) - tx = max(1, min(length(is), fld(max_threads, k * ty))) + tx = min(64, max(1, min(length(is), fld(max_threads, k * ty)))) threads = (k, ty, tx) + max_blocks = min(65535, max_blocks * fld(max_threads, prod(threads))) by = cld(length(js), ty) bx = min(cld(max_blocks, by), cld(length(is), tx)) blocks = (1, by, bx) diff --git a/ext/EvoTreesCUDAExt/predict.jl b/ext/EvoTreesCUDAExt/predict.jl index 6ae81645..e983dd4e 100644 --- a/ext/EvoTreesCUDAExt/predict.jl +++ b/ext/EvoTreesCUDAExt/predict.jl @@ -149,17 +149,19 @@ function EvoTrees.predict!( pred .= max.(T(-15), pred .- maximum(pred, dims=1)) end -# prediction from single tree - assign each observation to its final leaf +# prediction for EvoTree model function predict( m::EvoTree{L,K}, data, ::Type{<:EvoTrees.GPU}; ntree_limit=length(m.trees)) where {L,K} - pred = CUDA.zeros(K, size(data, 1)) + Tables.istable(data) ? data = Tables.columntable(data) : nothing ntrees = length(m.trees) ntree_limit > ntrees && error("ntree_limit is larger than number of trees $ntrees.") x_bin = CuArray(EvoTrees.binarize(data; fnames=m.info[:fnames], edges=m.info[:edges])) + nobs = size(x_bin, 1) + pred = CUDA.zeros(K, nobs) feattypes = CuArray(m.info[:feattypes]) for i = 1:ntree_limit EvoTrees.predict!(pred, m.trees[i], x_bin, feattypes) diff --git a/figures/gaussian-sinus-binary.png b/figures/gaussian-sinus-binary.png index 991b99f6ddc8fa6dd4fca54fd67f0313cfa24a95..ef5b99a77f5473f1321a931823c91f74fd0de897 100644 GIT binary patch literal 84224 zcmZs@cRbbq8$T@bWaJpx>qtme2+2Go*~vIYR!BA($KE8N$ll4`WMz|lBq2MSWUpk0 z`|A6|Ecrfv9orZHJ2Ul{5qEj%Qg 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z<>;wzlglhP9f>C-#>czk?M;>~8u*1nLn_KbUJlSB;?-RCoQd3A3fl~S?*S$p^2I84 z9P$_lIRjIMJtR;pqNAhnqZ+{;t{YnU-tpcEUN9}z$Qgfc526aW33qf9PPl}o2C1tl zhRa{HzfE4bj@KEI^$mFJF;~r$55BvlU~>BL*$qgH*B5{8zMLR_2dGQM0+b`zlJp#N zeZIRZ^F|$4;5qNliV?Q)0MHY?`$e37p)6uExI}i8Rj*%ZjZv1-Tv~T1aVQ&7OAII3Zo)WorQzQ!xdU zvuSB2xlh`yG67`iAEwIl|9@&Q@@_@t{+9eyG5)f#{>SwE|NVmvuhaBJ1~T1ArWE+4 ND0fLVi)7^Ue*w%}SAPHi diff --git a/src/MLJ.jl b/src/MLJ.jl index 781015f8..300d359b 100644 --- a/src/MLJ.jl +++ b/src/MLJ.jl @@ -42,7 +42,7 @@ function MMI.update( end function predict(::EvoTreeRegressor, fitresult, A) - pred = vec(predict(fitresult, A)) + pred = predict(fitresult, A) return pred end @@ -52,7 +52,7 @@ function predict(::EvoTreeClassifier, fitresult, A) end function predict(::EvoTreeCount, fitresult, A) - λs = vec(predict(fitresult, A)) + λs = predict(fitresult, A) return [Distributions.Poisson(λ) for λ ∈ λs] end diff --git a/src/predict.jl b/src/predict.jl index 300ebe66..9c753b20 100644 --- a/src/predict.jl +++ b/src/predict.jl @@ -86,10 +86,11 @@ function predict( ::Type{<:Device}=CPU; ntree_limit=length(m.trees)) where {L,K} + Tables.istable(data) ? data = Tables.columntable(data) : nothing ntrees = length(m.trees) ntree_limit > ntrees && error("ntree_limit is larger than number of trees $ntrees.") x_bin = binarize(data; fnames=m.info[:fnames], edges=m.info[:edges]) - nobs = Tables.istable(data) ? length(Tables.getcolumn(data, 1)) : size(data, 1) + nobs = size(x_bin, 1) pred = zeros(Float32, K, nobs) for i = 1:ntree_limit predict!(pred, m.trees[i], x_bin, m.info[:feattypes]) diff --git a/test/MLJ.jl b/test/MLJ.jl index 2cdc1ddd..3d1b8e06 100644 --- a/test/MLJ.jl +++ b/test/MLJ.jl @@ -377,3 +377,13 @@ mach.model.nrounds += 10 fit!(mach, rows=train, verbosity=1) report(mach) + +@testset "MLJ - rowtables - EvoTreeRegressor" begin + X, y = make_regression(1000, 5) + X = Tables.rowtable(X) + booster = EvoTreeRegressor() + # smoke tests: + mach = machine(booster, X, y) |> fit! + fit!(mach) + predict(mach, X) +end diff --git a/test/runtests.jl b/test/runtests.jl index a4228a49..e043aa38 100644 --- a/test/runtests.jl +++ b/test/runtests.jl @@ -2,6 +2,7 @@ using Statistics using EvoTrees using EvoTrees: predict using CategoricalArrays +using Tables using Random using Test