From bc03743516774589e2a1abd8818d3d0ba183d0c8 Mon Sep 17 00:00:00 2001
From: Brian Groenke <brian.groenke@awi.de>
Date: Wed, 1 Jan 2025 19:04:04 +0100
Subject: [PATCH] Disable reverse-mode in autodiff example
---
examples/heat_simple_autodiff_grad.jl | 21 +++++++++++----------
1 file changed, 11 insertions(+), 10 deletions(-)
diff --git a/examples/heat_simple_autodiff_grad.jl b/examples/heat_simple_autodiff_grad.jl
index e3716a22..3c5d8681 100644
--- a/examples/heat_simple_autodiff_grad.jl
+++ b/examples/heat_simple_autodiff_grad.jl
@@ -8,7 +8,8 @@ using CryoGrid
# Set up forcings and boundary conditions similarly to other examples:
forcings = loadforcings(CryoGrid.Forcings.Samoylov_ERA_obs_fitted_1979_2014_spinup_extended_2044);
soilprofile, tempprofile = CryoGrid.SamoylovDefault
-soilprofile = SoilProfile(0.0u"m" => SimpleSoil())
+freezecurve = PainterKarra(swrc=VanGenuchten())
+soilprofile = SoilProfile(0.0u"m" => SimpleSoil(; freezecurve))
grid = CryoGrid.DefaultGrid_5cm
initT = initializer(:T, tempprofile)
tile = CryoGrid.SoilHeatTile(
@@ -43,10 +44,10 @@ using OrdinaryDiffEq
using Statistics
function loss(prob::CryoGridProblem, p)
newprob = remake(prob, p=p)
- # Here we specify the sensitivity algorithm. Note that this is only
- # necessary for reverse-mode autodiff with Zygote.
- # autojacvec = true uses ForwardDiff to calculate the jacobian;
- # enabling checkpointing (theoretically) reduces the memory cost of the backwards pass.
+ ## Here we specify the sensitivity algorithm. Note that this is only
+ ## necessary for reverse-mode autodiff with Zygote.
+ ## autojacvec = true uses ForwardDiff to calculate the jacobian;
+ ## enabling checkpointing (theoretically) reduces the memory cost of the backwards pass.
sensealg = InterpolatingAdjoint(autojacvec=true, checkpointing=true)
newsol = solve(newprob, Euler(), dt=300.0, sensealg=sensealg);
newout = CryoGridOutput(newsol)
@@ -57,8 +58,8 @@ end
pvec = vec(prob.p)
fd_grad = @time ForwardDiff.gradient(páµ¢ -> loss(prob, páµ¢), pvec)
-# We can also try with reverse-mode autodiff. This is generally slower for smaller numbers
-# of parmaeters (<100) but could be worthwhile for model configurations with high-dimensional
-# parameterizations.
-zy_grad = @time Zygote.gradient(páµ¢ -> loss(prob, páµ¢), pvec)
-@assert maximum(abs.(fd_grad .- zy_grad)) .< 1e-6 "Forward and reverse gradients don't match!"
+## We can also try with reverse-mode autodiff. This is generally slower for smaller numbers
+## of parmaeters (<100) but could be worthwhile for model configurations with high-dimensional
+## parameterizations.
+## zy_grad = @time Zygote.gradient(páµ¢ -> loss(prob, páµ¢), pvec)
+## @assert maximum(abs.(fd_grad .- zy_grad)) .< 1e-6 "Forward and reverse gradients don't match!"
--
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