From 81f035cd376d73f44c914caa3ab24dadab57118d Mon Sep 17 00:00:00 2001 From: Vedant Puri Date: Wed, 9 Aug 2023 09:21:07 -0400 Subject: [PATCH] training on cpu --- examples/pdebench/burgers_bilinear.jl | 10 +++-- examples/pdebench/darcy2d.jl | 22 ++++++----- examples/pdebench/dump/plt_r2_test.png | Bin 0 -> 38153 bytes examples/pdebench/dump/plt_r2_train.png | Bin 0 -> 36545 bytes examples/pdebench/dump/plt_training.png | Bin 32580 -> 35083 bytes examples/pdebench/dump/plt_traj_test.png | Bin 0 -> 73232 bytes examples/pdebench/dump/plt_traj_train.png | Bin 0 -> 68693 bytes examples/pdebench/dump/statistics.txt | 20 +++++----- .../plt_r2_test.png | Bin 37075 -> 32730 bytes .../plt_r2_train.png | Bin 38416 -> 38696 bytes .../plt_training.png | Bin 36394 -> 23346 bytes .../plt_traj_test.png | Bin 73756 -> 78457 bytes .../plt_traj_train.png | Bin 69939 -> 77554 bytes .../statistics.txt | 22 +++++------ .../pdebench/model_darcy2D/plt_training.png | Bin 0 -> 35256 bytes .../pdebench/model_darcy2D/statistics.txt | 19 +++++++++ src/train.jl | 36 +++++++++++------- 17 files changed, 81 insertions(+), 48 deletions(-) create mode 100644 examples/pdebench/dump/plt_r2_test.png create mode 100644 examples/pdebench/dump/plt_r2_train.png create mode 100644 examples/pdebench/dump/plt_traj_test.png create mode 100644 examples/pdebench/dump/plt_traj_train.png create mode 100644 examples/pdebench/model_darcy2D/plt_training.png create mode 100644 examples/pdebench/model_darcy2D/statistics.txt diff --git a/examples/pdebench/burgers_bilinear.jl b/examples/pdebench/burgers_bilinear.jl index 78a8005c..642f0570 100644 --- a/examples/pdebench/burgers_bilinear.jl +++ b/examples/pdebench/burgers_bilinear.jl @@ -37,7 +37,7 @@ rng = Random.default_rng() Random.seed!(rng, 983254) N = 1024 -E = 100 +E = 20 # trajectories _K = 512 @@ -59,8 +59,8 @@ V = FourierSpace(N) ### #============================# -w = 32 # width -l = 8 +w = 16 # width +l = 4 m = (128,) # modes c = size(_data[1], 1) # in channels o = size(_data[2], 1) # out channels @@ -70,10 +70,12 @@ root = Chain( OpKernel(w, w, m, relu), OpKernel(w, w, m, relu), ) + branch = Chain( OpKernel(w, w, m, relu), # use_bias = true OpKernel(w, w, m, relu), ) + fuse = OpConvBilinear(w, w, l, m) project = Chain( @@ -91,7 +93,7 @@ NN = Chain( #============================# opt = Optimisers.Adam() -batchsize = 32 +batchsize = 64 learning_rates = (1f-2, 1f-3, 1f-4, 1f-5) nepochs = E .* (0.25, 0.25, 0.25, 0.25) .|> Int diff --git a/examples/pdebench/darcy2d.jl b/examples/pdebench/darcy2d.jl index e709e2de..a4b48600 100644 --- a/examples/pdebench/darcy2d.jl +++ b/examples/pdebench/darcy2d.jl @@ -30,18 +30,18 @@ import Lux: cpu, gpu using Tullio, Zygote using FFTW, LinearAlgebra -BLAS.set_num_threads(20) -FFTW.set_num_threads(40) +BLAS.set_num_threads(4) +FFTW.set_num_threads(8) rng = Random.default_rng() Random.seed!(rng, 345) N = 128 -E = 10 # epochs +E = 40 # trajectories -_K = 512 -K_ = 128 +_K = 128 +K_ = 32 # get data dir = @__DIR__ @@ -70,14 +70,16 @@ NN = Lux.Chain( ) opt = Optimisers.Adam() -batchsize = 32 -learning_rates = (1f-2, 1f-3,) -nepochs = E .* (0.10, 0.90,) .|> Int +batchsize = 16 +learning_rates = (1f-2, 1f-3, 5f-4, 2.5f-4,) +nepochs = E .* (0.25, 0.25, 0.25, 0.25,) .|> Int dir = joinpath(@__DIR__, "model_darcy2D") -device = Lux.gpu +device = Lux.cpu # Lux.gpu model, ST = train_model(rng, NN, _data, data_, V, opt; batchsize, learning_rates, nepochs, dir, device) -nothing +plot_training(ST...) + +# nothing # diff --git a/examples/pdebench/dump/plt_r2_test.png b/examples/pdebench/dump/plt_r2_test.png new file mode 100644 index 0000000000000000000000000000000000000000..7fdebdfb79438f7cbbc48ae910f34647123a0690 GIT binary patch literal 38153 zcmYg&1yGgU_w}VO-GX#?cQ+#4A|28o-Q6jOh;$<@E!`!cNC-%Gmwt> zW{hD?0aXKmnq+mZoF+ED)@K4_ZFn>+J)B?j=WkCRoJF>skzp9+DNe6ZEv-DQBGv== z>$vN}G*BW;2_s5No66WAy&P!0TC@+>+c{NxTpYu)A3uFMKkmiM5pd48o37M0O#W+O 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a/examples/pdebench/model_burgers1D_nu0.001_bilinear/statistics.txt +++ b/examples/pdebench/model_burgers1D_nu0.001_bilinear/statistics.txt @@ -1,19 +1,19 @@ - TRAIN LOSS: 0.00090023 TEST LOSS: 0.00972281 + TRAIN LOSS: 0.00579758 TEST LOSS: 0.01066288 #======================# TRAIN STATS -R² score: 0.9961141 -MSE (mean SQR error): 0.00132547 -RMSE (root mean SQR error): 0.03640705 -MAE (mean ABS error): 0.02396612 -maxAE (max ABS error) 0.30083415 +R² score: 0.9823463 +MSE (mean SQR error): 0.00589802 +RMSE (root mean SQR error): 0.07679854 +MAE (mean ABS error): 0.05752488 +maxAE (max ABS error) 0.3610783 #======================# #======================# TEST STATS -R² score: 0.9676699 -MSE (mean SQR error): 0.01123419 -RMSE (root mean SQR error): 0.10599148 -MAE (mean ABS error): 0.07846318 -maxAE (max ABS error) 0.3812811 +R² score: 0.96915275 +MSE (mean SQR error): 0.01066288 +RMSE (root mean SQR error): 0.10326123 +MAE (mean ABS error): 0.07668994 +maxAE (max ABS error) 0.4150022 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b/examples/pdebench/model_darcy2D/statistics.txt new file mode 100644 index 00000000..cfde5fb5 --- /dev/null +++ b/examples/pdebench/model_darcy2D/statistics.txt @@ -0,0 +1,19 @@ + TRAIN LOSS: 0.00020804 TEST LOSS: 8.247e-5 +#======================# +TRAIN STATS +R² score: 0.76355976 +MSE (mean SQR error): 0.0003289 +RMSE (root mean SQR error): 0.01813549 +MAE (mean ABS error): 0.00971096 +maxAE (max ABS error) 0.26131245 + +#======================# +#======================# +TEST STATS +R² score: 0.85738647 +MSE (mean SQR error): 0.00025149 +RMSE (root mean SQR error): 0.01585852 +MAE (mean ABS error): 0.00898928 +maxAE (max ABS error) 0.16604173 + +#======================# diff --git a/src/train.jl b/src/train.jl index 58e00e47..7f487781 100644 --- a/src/train.jl +++ b/src/train.jl @@ -59,20 +59,26 @@ function train_model( _stats = (p, st; io = io) -> statistics(NN, p, st, _loader; io) stats_ = (p, st; io = io) -> statistics(NN, p, st, loader_; io) - # full batch losses for CB + # full batch losses for cb_stats _loss = (p, st) -> minibatch_metric(NN, p, st, _loader, lossfun) loss_ = (p, st) -> minibatch_metric(NN, p, st, loader_, lossfun) # callback functions EPOCH = Int[] _LOSS = Float32[] + _LOSS_MINIBATCH = Float32[] LOSS_ = Float32[] # callback for printing statistics - CB = (p, st; io = io) -> callback(p, st; io, _loss, _stats, loss_, stats_) + cb_stats = (p, st; io = io) -> callback(p, st; io, _loss, _stats, loss_, stats_) + + # cb_batch = + + # early stopping: (need fullbatch validation loss for early stopping?) + # https://github.com/jeffheaton/app_deep_learning/blob/main/t81_558_class_03_4_early_stop.ipynb # callback for training - cb = (p, st, epoch, nepoch; io = io) -> callback(p, st; io, + cb_epoch = (p, st, epoch, nepoch; io = io) -> callback(p, st; io, _loss, _LOSS, loss_, LOSS_, EPOCH, epoch, nepoch, step = cbstep) @@ -86,7 +92,7 @@ function train_model( p, st = (p, st) |> device # print stats - CB(p, st) + cb_stats(p, st) println(io, "#======================#") println(io, "Starting Trainig Loop") @@ -107,19 +113,19 @@ function train_model( println(io, "#======================#") if device == Lux.gpu - CUDA.@time p, st, opt_st = optimize(NN, p, st, _loader, nepoch; lossfun, opt, opt_st, cb, io) + CUDA.@time p, st, opt_st = optimize(NN, p, st, _loader, nepoch; lossfun, opt, opt_st, cb_epoch, io) else - @time p, st, opt_st = optimize(NN, p, st, _loader, nepoch; lossfun, opt, opt_st, cb, io) + @time p, st, opt_st = optimize(NN, p, st, _loader, nepoch; lossfun, opt, opt_st, cb_epoch, io) end - CB(p, st) + cb_stats(p, st) end # TODO - output a train.log file with timings # save statistics statsfile = open(joinpath(dir, "statistics.txt"), "w") - CB(p, st; io = statsfile) + cb_stats(p, st; io = statsfile) close(statsfile) # transfer model to host device @@ -339,12 +345,13 @@ function optimize(NN, p, st, loader, nepochs; lossfun = mse, opt = Optimisers.Adam(), opt_st = nothing, - cb = nothing, + cb_batch = nothing, + cb_epoch = nothing, io::Union{Nothing, IO} = stdout, ) # print stats - !isnothing(cb) && cb(p, st, 0, nepochs; io) + !isnothing(cb_epoch) && cb_epoch(p, st, 0, nepochs; io) function loss(x, ŷ, p, st) y, st = NN(x, p, st) @@ -367,12 +374,13 @@ function optimize(NN, p, st, loader, nepochs; opt_st, p = Optimisers.update!(opt_st, p, g) println(io, "Epoch [$epoch / $nepochs]" * "\t Batch loss: $l") - - # GC.gc(false) + !isnothing(cb_batch) && cb_batch(p, st, step) end + # TODO - add stopping criteria for GD + println(io, "#=======================#") - !isnothing(cb) && cb(p, st, epoch, nepochs; io) + !isnothing(cb_epoch) && cb_epoch(p, st, epoch, nepochs; io) println(io, "#=======================#") end @@ -396,7 +404,7 @@ function plot_training(EPOCH, _LOSS, LOSS_; dir = nothing) xlabel = "Epochs", ylabel = "Loss (MSE)", ylims = (minimum(_LOSS) / 10, maximum(LOSS_) * 10)) - plot!(plt, EPOCH, _LOSS, w = 2.0, c = :green, label = "Train Dataset") + plot!(plt, EPOCH, _LOSS, w = 2.0, c = :green, label = "Train Dataset") # (; ribbon = (lower, upper)) plot!(plt, EPOCH, LOSS_, w = 2.0, c = :red, label = "Test Dataset") vline!(plt, EPOCH[z[2:end]], c = :black, w = 2.0, label = nothing)