From e5aad60fc253f9c687fc3c584cd2d5d39c37f888 Mon Sep 17 00:00:00 2001 From: "jeremie.desgagne.bouchard" Date: Thu, 3 Aug 2023 14:38:38 -0400 Subject: [PATCH] cleanup high depth perf improvements --- Project.toml | 2 +- README.md | 20 ++--- benchmarks/regressor.jl | 14 ++-- docs/src/assets/regression-sinus-binary.png | Bin 80758 -> 80209 bytes experiments/readme_plots_cpu.jl | 26 +++---- experiments/readme_plots_gpu.jl | 2 +- figures/regression-sinus-binary-gpu.png | Bin 79099 -> 79422 bytes figures/regression-sinus-binary.png | Bin 80758 -> 80209 bytes figures/regression-sinus-oblivious-gpu.png | Bin 79486 -> 80541 bytes figures/regression-sinus-oblivious.png | Bin 80712 -> 80659 bytes figures/regression-sinus2-binary.png | Bin 78044 -> 78144 bytes figures/regression-sinus2-oblivious.png | Bin 78550 -> 78809 bytes src/fit-utils.jl | 77 +++++--------------- src/fit.jl | 44 ++++++----- src/gpu/fit.jl | 43 +++++------ 15 files changed, 93 insertions(+), 135 deletions(-) diff --git a/Project.toml b/Project.toml index cde30189..a78cda25 100644 --- a/Project.toml +++ b/Project.toml @@ -1,7 +1,7 @@ name = "EvoTrees" uuid = "f6006082-12f8-11e9-0c9c-0d5d367ab1e5" authors = ["jeremiedb "] -version = "0.15.1" +version = "0.15.2" [deps] BSON = "fbb218c0-5317-5bc6-957e-2ee96dd4b1f0" diff --git a/README.md b/README.md index 785776bf..fcbe6a98 100644 --- a/README.md +++ b/README.md @@ -46,27 +46,27 @@ Code to reproduce is availabe in [`benchmarks/regressor.jl`](https://github.com/ - Julia: v1.9.1. - Algorithms - XGBoost: v2.3.0 (Using the `hist` algorithm). - - EvoTrees: v0.15.0. + - EvoTrees: v0.15.2. ### Training: | Dimensions / Algo | XGBoost CPU | EvoTrees CPU | XGBoost GPU | EvoTrees GPU | |---------------------|:-----------:|:------------:|:-----------:|:------------:| -| 100K x 100 | 2.33s | 1.09s | 0.90s | 2.72s | -| 500K x 100 | 10.7s | 2.96s | 1.84s | 3.65s | -| 1M x 100 | 20.9s | 6.48s | 3.10s | 4.45s | -| 5M x 100 | 108s | 35.8s | 12.9s | 12.7s | -| 10M x 100 | 216s | 71.6s | 25.5s | 23.0s | +| 100K x 100 | 2.34s | 1.01s | 0.90s | 2.61s | +| 500K x 100 | 10.7s | 3.95s | 1.84s | 3.41s | +| 1M x 100 | 21.1s | 6.57s | 3.10s | 4.47s | +| 5M x 100 | 108s | 36.1s | 12.9s | 12.5s | +| 10M x 100 | 218s | 72.6s | 25.5s | 23.0s | ### Inference: | Dimensions / Algo | XGBoost CPU | EvoTrees CPU | XGBoost GPU | EvoTrees GPU | |---------------------|:------------:|:------------:|:-----------:|:------------:| -| 100K x 100 | 0.151s | 0.053s | NA | 0.036s | -| 500K x 100 | 0.628s | 0.276s | NA | 0.169s | -| 1M x 100 | 1.26s | 0.558s | NA | 0.334s | +| 100K x 100 | 0.151s | 0.058s | NA | 0.045s | +| 500K x 100 | 0.647s | 0.248s | NA | 0.172s | +| 1M x 100 | 1.26s | 0.573s | NA | 0.327s | | 5M x 100 | 6.04s | 2.87s | NA | 1.66s | -| 10M x 100 | 12.4s | 5.71s | NA | 3.31s | +| 10M x 100 | 12.4s | 5.71s | NA | 3.40s | ## MLJ Integration diff --git a/benchmarks/regressor.jl b/benchmarks/regressor.jl index 278462bd..b78b38af 100644 --- a/benchmarks/regressor.jl +++ b/benchmarks/regressor.jl @@ -8,12 +8,17 @@ using BenchmarkTools using Random: seed! import CUDA +### v.0.15.1 +# desktop | 1e6 | depth 11 | cpu: 37.2s +# desktop | 10e6 | depth 11 | cpu + +### perf depth # desktop | 1e6 | depth 11 | cpu: 28s gpu: 73 sec | xgboost: 26s # desktop | 10e6 | depth 11 | cpu 205s gpu: 109 sec | xgboost 260s nobs = Int(1e6) num_feat = Int(100) nrounds = 200 -max_depth = 11 +max_depth = 6 tree_type = "binary" T = Float64 nthread = Base.Threads.nthreads() @@ -120,14 +125,11 @@ device = "cpu" # @time m_evo = fit_evotree(params_evo; x_train, y_train, device, verbosity, print_every_n=100); @info "train - eval" @time m_evo = fit_evotree(params_evo; x_train, y_train, x_eval=x_train, y_eval=y_train, metric=metric_evo, device, verbosity, print_every_n=100); -# using Plots -# plot(m_evo, 2) - @time m_evo = fit_evotree(params_evo; x_train, y_train, x_eval=x_train, y_eval=y_train, metric=metric_evo, device, verbosity, print_every_n=100); @info "predict" @time pred_evo = m_evo(x_train); @time pred_evo = m_evo(x_train); -@btime m_evo($x_train); +# @btime m_evo($x_train); @info "EvoTrees GPU" device = "gpu" @@ -142,4 +144,4 @@ CUDA.@time m_evo = fit_evotree(params_evo; x_train, y_train, x_eval=x_train, y_e @info "predict" CUDA.@time pred_evo = m_evo(x_train; device); CUDA.@time pred_evo = m_evo(x_train; device); -@btime m_evo($x_train; device); +# @btime m_evo($x_train; device); diff --git a/docs/src/assets/regression-sinus-binary.png b/docs/src/assets/regression-sinus-binary.png index b442732c563039854e23ec7b186023bc37849d7a..c81dc366049aebdaab5eb018fc6d173320e27f0f 100644 GIT binary patch literal 80209 zcmaHTcRba9{I79v>|;x^l90U<;$(-Atn84Lot?e+2q8p9lB}%kl_ZHnR(7%yviE)Y z-ut_c``7jDTgJiXyg%>v>-Ai(6Q!x4L`uX!goTAgs-i5fjfI7ykA;Puh{S=0*lveFEzb!5O zxZ+rl^~w5JQ&!PZ7F)q(wmpVr^=Y`b`^gF7%9793d3r1+#(Et5ja5xXr3O;q`6CSAn>dVDrV~H86Sf2myZxXSTp##LK&Q=X3olR$}yjKhZ_*&j0;^pL`#n(o_q}{dY^V9C(m81KW??r8X`* 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within a depth + n_next = Int[] + if depth < params.max_depth for n_id in eachindex(n_current) n = n_current[n_id] @@ -89,18 +91,15 @@ function grow_tree!( update_hist!(L, nodes[n].h, ∇, x_bin, nodes[n].is, js) end end - end - - @threads :static for n ∈ sort(n_current) - update_gains!(nodes[n], js, params, feattypes, monotone_constraints) + @threads :static for n ∈ sort(n_current) + update_gains!(nodes[n], js, params, feattypes, monotone_constraints) + end end for n ∈ sort(n_current) if depth == params.max_depth || nodes[n].∑[end] <= params.min_weight pred_leaf_cpu!(tree.pred, n, nodes[n].∑, params, ∇, nodes[n].is) - popfirst!(n_next) else - # update_gains!(nodes[n], js, params, feattypes, monotone_constraints) best = findmax(findmax.(nodes[n].gains)) best_gain = best[1][1] best_bin = best[1][2] @@ -110,12 +109,8 @@ function grow_tree!( tree.cond_bin[n] = best_bin tree.feat[n] = best_feat tree.cond_float[n] = edges[tree.feat[n]][tree.cond_bin[n]] - end - tree.split[n] = tree.cond_bin[n] != 0 - if !tree.split[n] - pred_leaf_cpu!(tree.pred, n, nodes[n].∑, params, ∇, nodes[n].is) - popfirst!(n_next) - else + tree.split[n] = best_bin != 0 + _left, _right = split_set_threads!( out, left, @@ -127,12 +122,14 @@ function grow_tree!( feattypes[best_feat], offset, ) + offset += length(nodes[n].is) nodes[n<<1].is, nodes[n<<1+1].is = _left, _right nodes[n<<1].∑ .= nodes[n].hL[best_feat][:, best_bin] nodes[n<<1+1].∑ .= nodes[n].hR[best_feat][:, best_bin] nodes[n<<1].gain = get_gain(params, nodes[n<<1].∑) nodes[n<<1+1].gain = get_gain(params, nodes[n<<1+1].∑) + if length(_right) >= length(_left) push!(n_next, n << 1) push!(n_next, n << 1 + 1) @@ -140,7 +137,8 @@ function grow_tree!( push!(n_next, n << 1 + 1) push!(n_next, n << 1) end - popfirst!(n_next) + else + pred_leaf_cpu!(tree.pred, n, nodes[n].∑, params, ∇, nodes[n].is) end end end @@ -177,17 +175,18 @@ function grow_otree!( end end - # reset - n_next = [1] - n_current = copy(n_next) + # initialize + n_current = [1] depth = 1 # initialize summary stats nodes[1].∑ .= dropdims(sum(Float64, view(∇, :, nodes[1].is), dims=2), dims=2) nodes[1].gain = get_gain(params, nodes[1].∑) + # grow while there are remaining active nodes while length(n_current) > 0 && depth <= params.max_depth offset = 0 # identifies breakpoint for each node set within a depth + n_next = Int[] min_weight_flag = false for n in n_current @@ -197,7 +196,6 @@ function grow_otree!( for n in n_current # @info "length(nodes[n].is)" length(nodes[n].is) depth n pred_leaf_cpu!(tree.pred, n, nodes[n].∑, params, ∇, nodes[n].is) - popfirst!(n_next) end else # update histograms @@ -217,6 +215,9 @@ function grow_otree!( update_hist!(L, nodes[n].h, ∇, x_bin, nodes[n].is, js) end end + @threads :static for n ∈ n_current + update_gains!(nodes[n], js, params, feattypes, monotone_constraints) + end # initialize gains for node 1 in which all gains of a given depth will be accumulated if depth > 1 @@ -227,7 +228,6 @@ function grow_otree!( gain = 0 # update gains based on the aggregation of all nodes of a given depth. One gains matrix per depth (vs one per node in binary trees). for n ∈ sort(n_current) - update_gains!(nodes[n], js, params, feattypes, monotone_constraints) if n > 1 # accumulate gains in node 1 for j in js nodes[1].gains[j] .+= nodes[n].gains[j] @@ -281,12 +281,10 @@ function grow_otree!( push!(n_next, n << 1 + 1) push!(n_next, n << 1) end - popfirst!(n_next) end else for n in n_current pred_leaf_cpu!(tree.pred, n, nodes[n].∑, params, ∇, nodes[n].is) - popfirst!(n_next) end end end diff --git a/src/gpu/fit.jl b/src/gpu/fit.jl index 30d9bd00..b6751599 100644 --- a/src/gpu/fit.jl +++ b/src/gpu/fit.jl @@ -61,16 +61,18 @@ function grow_tree!( end # initialize - n_next = [1] - n_current = copy(n_next) + n_current = [1] depth = 1 # initialize summary stats nodes[1].∑ .= Vector(vec(sum(∇[:, nodes[1].is], dims=2))) nodes[1].gain = get_gain(params, nodes[1].∑) # should use a GPU version? - # grow while there are remaining active nodes - TO DO histogram substraction hits issue on GPU + + # grow while there are remaining active nodes while length(n_current) > 0 && depth <= params.max_depth offset = 0 # identifies breakpoint for each node set within a depth + n_next = Int[] + if depth < params.max_depth for n_id in eachindex(n_current) n = n_current[n_id] @@ -88,34 +90,26 @@ function grow_tree!( update_hist_gpu!(nodes[n].h, h∇, ∇, x_bin, nodes[n].is, jsg, js) end end + @threads :static for n ∈ sort(n_current) + update_gains!(nodes[n], js, params, feattypes, monotone_constraints) + end end - # grow while there are remaining active nodes for n ∈ sort(n_current) if depth == params.max_depth || nodes[n].∑[end] <= params.min_weight pred_leaf_cpu!(tree.pred, n, nodes[n].∑, params, ∇, nodes[n].is) - popfirst!(n_next) else - # @info "gain & max" - update_gains!(nodes[n], js, params, feattypes, monotone_constraints) best = findmax(findmax.(nodes[n].gains)) best_gain = best[1][1] best_bin = best[1][2] best_feat = best[2] - # if best_gain > nodes[n].gain + params.gamma && best_gain > nodes[n].gains[best_feat][end] + params.gamma if best_gain > nodes[n].gain + params.gamma tree.gain[n] = best_gain - nodes[n].gain tree.cond_bin[n] = best_bin tree.feat[n] = best_feat tree.cond_float[n] = edges[tree.feat[n]][tree.cond_bin[n]] - end - tree.split[n] = tree.cond_bin[n] != 0 - if !tree.split[n] - pred_leaf_cpu!(tree.pred, n, nodes[n].∑, params, ∇, nodes[n].is) - popfirst!(n_next) - else - # @info "split" best_bin typeof(nodes[n].is) length(nodes[n].is) - # @info "split typeof" typeof(out) typeof(left) typeof(nodes[n].is) typeof(x_bin) + tree.split[n] = best_bin != 0 + _left, _right = split_set_threads_gpu!( out, left, @@ -127,12 +121,14 @@ function grow_tree!( feattypes[best_feat], offset, ) + offset += length(nodes[n].is) nodes[n<<1].is, nodes[n<<1+1].is = _left, _right nodes[n<<1].∑ .= nodes[n].hL[best_feat][:, best_bin] nodes[n<<1+1].∑ .= nodes[n].hR[best_feat][:, best_bin] nodes[n<<1].gain = get_gain(params, nodes[n<<1].∑) nodes[n<<1+1].gain = get_gain(params, nodes[n<<1+1].∑) + if length(_right) >= length(_left) push!(n_next, n << 1) push!(n_next, n << 1 + 1) @@ -140,8 +136,8 @@ function grow_tree!( push!(n_next, n << 1 + 1) push!(n_next, n << 1) end - # @info "split post" length(_left) length(_right) - popfirst!(n_next) + else + pred_leaf_cpu!(tree.pred, n, nodes[n].∑, params, ∇, nodes[n].is) end end end @@ -182,8 +178,7 @@ function grow_otree!( end # initialize - n_next = [1] - n_current = copy(n_next) + n_current = [1] depth = 1 # initialize summary stats @@ -193,6 +188,7 @@ function grow_otree!( # grow while there are remaining active nodes while length(n_current) > 0 && depth <= params.max_depth offset = 0 # identifies breakpoint for each node set within a depth + n_next = Int[] min_weight_flag = false for n in n_current @@ -202,7 +198,6 @@ function grow_otree!( for n in n_current # @info "length(nodes[n].is)" length(nodes[n].is) depth n pred_leaf_cpu!(tree.pred, n, nodes[n].∑, params, ∇, nodes[n].is) - popfirst!(n_next) end else # update histograms @@ -222,6 +217,9 @@ function grow_otree!( update_hist_gpu!(nodes[n].h, h∇, ∇, x_bin, nodes[n].is, jsg, js) end end + @threads :static for n ∈ n_current + update_gains!(nodes[n], js, params, feattypes, monotone_constraints) + end # initialize gains for node 1 in which all gains of a given depth will be accumulated if depth > 1 @@ -232,7 +230,6 @@ function grow_otree!( gain = 0 # update gains based on the aggregation of all nodes of a given depth. One gains matrix per depth (vs one per node in binary trees). for n ∈ sort(n_current) - update_gains!(nodes[n], js, params, feattypes, monotone_constraints) if n > 1 # accumulate gains in node 1 for j in js nodes[1].gains[j] .+= nodes[n].gains[j] @@ -286,12 +283,10 @@ function grow_otree!( push!(n_next, n << 1 + 1) push!(n_next, n << 1) end - popfirst!(n_next) end else for n in n_current pred_leaf_cpu!(tree.pred, n, nodes[n].∑, params, ∇, nodes[n].is) - popfirst!(n_next) end end end

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