[MITgcm-support] tutorial_global_oce_optim optimisation failed

Andrew McRae andrew.mcrae at physics.ox.ac.uk
Wed Jun 13 13:14:09 EDT 2018 

Hi Patrick,

Were you able to make any progress with this?  If not, do you have any
advice on debugging this?  (I'm getting lost in ctrl_unpack as to which
variable the control vector is even read into)

Thanks,
Andrew

On 5 May 2018 at 20:12, Patrick Heimbach <heimbach at mit.edu> wrote:

> A quick update:
>
> This tutorial works as advertised (in the manual), but not as "hoped".
> What I mean is that it has been developed and only ever fully tested and
> used  in optimization mode with TAF-generated code (and that's what's
> documented in the manual).
>
> Of course, it should not make a difference of whether we use TAF vs.
> OpenAD as long as gradients are correct. But as it turns out, with the
> OpenAD code there appears to be a little glitch. Gradient seems correct,
> and iteration 1 update is properly read in, but then not used (instead it
> is reset to zero). Oh well. I'll need to check where that happens, so stay
> tuned.
>
> p.
>
> > On May 4, 2018, at 10:11 AM, Andrew McRae <andrew.mcrae at physics.ox.ac.uk>
> wrote:
> >
> > And, still no luck(?)
> >
> > Running for a year (switching the commented and uncommented nTimeSteps
> and lastinterval declarations in data and data.cost), optim.x (lsopt+optim,
> not optim_m1qn3) now gives the output
> >
> >   cost function............... 0.60514949E+01
> >   norm of x................... 0.00000000E+00
> >   norm of g................... 0.23235517E+00
> >
> >   optimization stopped because :
> >   ifail =   4    the search direction is not a descent one
> >
> >
> > On 4 May 2018 at 13:58, Andrew McRae <andrew.mcrae at physics.ox.ac.uk>
> wrote:
> > On 4 May 2018 at 06:04, Patrick Heimbach <heimbach at mit.edu> wrote:
> > Hi Matt,
> >
> > as you indicated, all is still good, and I suspect the same you did
> regarding what might be at issue.
> >
> > I just downloaded latest MITgcm, re-ran adjoint, and conducted 2
> iterations (using lsopt).
> >
> > It still works "out of the box" ... if one realizes that a manual is
> part of that "box", and section 3.18 (old manual prior to readthedocs) has
> some description of this tutorial, thanks to dfer (admittedly somewhat out
> of date, but still mostly relevant). In particular it says there that the
> optimization has been conducted for a 1-year simulation.
> >
> > Okay, thanks.  I interpreted the manual footnote as "running a 1-year
> simulation will reproduce the scientifically-interesting graphs in the
> manual", not as "the default parameters are only useful for verifying
> correctness of the adjoint, but will break the optimisation routine".  I'll
> see if I have more success with the longer run.
> >
> >
> > Since we do not want to conduct 1-year integrations for *any* of the
> tutorials within our regression tests (these tests consist of 90 forward,
> 24 adjoint/TAF, 10 adjoint/OpenAD, and 16 tangent-linear/TAF
> configurations, each needing to be compiled and executed) we have shortened
> the number of time steps to 10 (= 10 days) to perform efficient nightly
> regression tests of the adjoint. Not changing the number of time steps
> leads to optimizing in the noise - in fact cost function goes up in that
> case.
> >
> > That the user's cost function does not change at all suggests a more
> basic problem though (hard to speculate what it might be).
> >
> > I made a quick test by extending nTimeSteps from 10 to 90 days, which
> leads to cost reduction as desired, namely, for:
> >  numiter=1,
> >  nfunc=3,
> >  fmin=5.74,
> > (values in data.optim that comes with tutorial_global_oce_optim)
> > I obtain following costs:
> > iter. 0: fc =  0.184199260445164D+02
> > iter. 1: fc =  0.130860446841901D+02
> > iter. 2: fc =  0.979374136987667D+01
> >
> > I did that test "by hand", i.e. not using the script cycsh also provided
> (see manual). Doing so by hand requires two more lines in data.ctrl:
> >  &CTRL_PACKNAMES
> >  costname='ecco_cost',
> >  ctrlname='ecco_ctrl',
> >
> > Since gradients produced with TAF are extremely similar (10+ digits?) to
> those produce with OpenAD (see results/ directory which has both TAF and
> OpenAD reference results), I expect it to work with OpenAD too (have not
> tested it right now).
> >
> > -Patrick
> >
> >
> >
> > > On May 2, 2018, at 12:34 PM, Andrew McRae <
> andrew.mcrae at physics.ox.ac.uk> wrote:
> > >
> > > Thanks for this.
> > >
> > > Just as a sanity check, before I involve optim_m1qn3 again, the output
> of my ./testreport -t tutorial_global_oce_optim -oad includes
> > >
> > > There were 16 decimal places of similarity for "ADM CostFct"
> > > There were 16 decimal places of similarity for "ADM Ad Grad"
> > > There were 0 decimal places of similarity for "ADM FD Grad"
> > >
> > > Should I be concerned about this?
> > >
> > > E.g. lines 2116-2118 of my output_oadm.txt file are
> > >
> > > (PID.TID 0000.0001)  ADM  ref_cost_function      =
> 6.20023228182329E+00
> > > (PID.TID 0000.0001)  ADM  adjoint_gradient       =
> -2.69091500991183E-06
> > > (PID.TID 0000.0001)  ADM  finite-diff_grad       =
> 0.00000000000000E+00
> > >
> > > But at least my cost function value is the same:
> > >
> > > (PID.TID 0000.0001)   local fc =  0.620023228182329D+01
> > > (PID.TID 0000.0001)  global fc =  0.620023228182329D+01
> > >
> > > Andrew
> > >
> > > On 2 May 2018 at 10:34, Martin Losch <Martin.Losch at awi.de> wrote:
> > > Hi Andrew,
> > >
> > > I won’t be able to help you much with the optim/lsopt code, because I
> would have to get it running again myself. But I do recommend using the
> MITgcm_contrib/mlosch/optim_m1qn3 code. It’s not very well documented,
> but I am attaching a skeleton script to illustrate how to use it. Please
> give it a try and if you find it useful, I can add this script to the
> repository.
> > >
> > > The two versions of the optimization routine are similar, both
> implement the same optimization algorithm (BFGS), but optim_m1qn3 uses a
> later version of the m1qn3 code, I think it’s easier to compile (only one
> Makefile) and I believe (but there’s debate about this) that it does the
> right thing as opposed to the optim/lsopt variant, which somehow truncates
> the optimization in each iteration. Having said that, I have used both in
> parallel, and the reduction of the cost function (which is really all we
> care about) is sometimes better with the optim_m1qn3 code, sometimes it is
> better with the optim/lsopt code. The optim_m1qn3 code is closer to the
> idea of the original m1qn3 code.
> > >
> > > Let me know if you can use my attached instructions.
> > >
> > > Martin
> > >
> > >
> > >
> > > > On 1. May 2018, at 00:00, Andrew McRae <
> andrew.mcrae at physics.ox.ac.uk> wrote:
> > > >
> > > > Right, but the cost function is the same value each time, the norm
> of x is 0 each time, and the norm of g is the same each time.  This
> suggests nothing is happening.  It's a bit ridiculous that one of the core
> tutorials simply isn't working out of the box...
> > > >
> > > > I will have a go at debugging.
> > > >
> > > > Andrew
> > > >
> > > > On 30 April 2018 at 22:54, Matthew Mazloff <mmazloff at ucsd.edu>
> wrote:
> > > > Well you are correct that its not actually taking a step because the
> dot product of the control is 0:
> > > >>> norm of x................... 0.00000000E+00
> > > > meaning the controls are all 0 still.
> > > >
> > > > However the gradients are non-zero
> > > >>> norm of g................... 0.12730927E-01
> > > > so the linesearch should step and
> > > > ecco_ctrl_MIT_CE_000.opt0001
> > > > should not be all zero.
> > > >
> > > > To debug this you could put a print statement in optim_writedata.F
> to see what it is writing…..
> > > >
> > > > I don’t know enough about this tutorial to be a bigger help, sorry
> > > >
> > > > Matt
> > > >
> > > >
> > > >> On Apr 30, 2018, at 2:50 PM, Andrew McRae <
> andrew.mcrae at physics.ox.ac.uk> wrote:
> > > >>
> > > >> Yes, I did.
> > > >>
> > > >> On 30 April 2018 at 22:42, Matthew Mazloff <mmazloff at ucsd.edu>
> wrote:
> > > >> This is still iteration 0. You have to update data.optim to tell it
> you are now at iteration 1
> > > >>
> > > >> Matt
> > > >>
> > > >>
> > > >>> On Apr 30, 2018, at 2:38 PM, Andrew McRae <
> andrew.mcrae at physics.ox.ac.uk> wrote:
> > > >>>
> > > >>> I tried a few steps of this, but the output of optim.x always has
> > > >>>
> > > >>>   cost function............... 0.62002323E+01
> > > >>>   norm of x................... 0.00000000E+00
> > > >>>   norm of g................... 0.12730927E-01
> > > >>>
> > > >>> near the end, with no decrease in the cost function.  So I guess
> it's not actually taking the step?
> > > >>>
> > > >>> Andrew
> > > >>>
> > > >>> On 27 April 2018 at 18:04, Andrew McRae <
> andrew.mcrae at physics.ox.ac.uk> wrote:
> > > >>> !!!  Okay...
> > > >>>
> > > >>> Yes, it produced the .opt0001 file.  I'll see how this goes.
> > > >>>
> > > >>> Thanks,
> > > >>> Andrew
> > > >>>
> > > >>> On 27 April 2018 at 17:57, Matthew Mazloff <mmazloff at ucsd.edu>
> wrote:
> > > >>> Hello
> > > >>>
> > > >>> Its been awhile, but I am pretty sure that is the normal output.
> It says “fail", but it did give you a new and ecco_ctrl_MIT_CE_000.opt0001
> (correct?) and if you unpack and run likely the cost will descend.
> > > >>>
> > > >>> I think it worked correctly. lsopt/optim are just confusing…but I
> think its working. I think all is good!
> > > >>>
> > > >>> Matt
> > > >>>
> > > >>>
> > > >>>
> > > >>>> On Apr 27, 2018, at 8:25 AM, Andrew McRae <
> andrew.mcrae at physics.ox.ac.uk> wrote:
> > > >>>>
> > > >>>> Just separating this from the other thread, I got the bundled
> MITgcm optim routine built (having made these changes, based on this thread
> from 2010 and this one from 2016).
> > > >>>>
> > > >>>> I use OpenAD to create the adjoint.
> > > >>>>
> > > >>>> My steps are:
> > > >>>> 1) in the build directory, run ../../../tools/genmake2 -oad
> -mods=../code_oad
> > > >>>> 2) run make depend and make adAll
> > > >>>> 3) copy input_oad/ into a new folder scratch/
> > > >>>> 4) within scratch/, run ./prepare_run
> > > >>>> 5) copy mitgcmuv_ad from build/ into scratch/, copy optim.x into
> scratch/OPTIM/
> > > >>>> 6) run ./mitgcmuv_ad
> > > >>>> 7) in scratch/OPTIM, create symlinks to ../data.optim and
> ../data.ctrl
> > > >>>> 8) copy the files ecco_cost_MIT_CE_000.opt0000 and
> ecco_ctrl_MIT_CE_000.opt0000 into the OPTIM subdirectory
> > > >>>> 9) run ./optim.x within the subdirectory
> > > >>>>
> > > >>>> The full output is attached, but I assume the optimisation failed
> since the last lines are
> > > >>>>
> > > >>>>   optimization stopped because :
> > > >>>>   ifail =   4    the search direction is not a descent one
> > > >>>>
> > > >>>> Any ideas?  (I guess this isn't something that is tested in the
> daily builds?)
> > > >>>>
> > > >>>> In the meantime, I'll try the m1qn3 routine as in the other
> thread, which should help distinguish between a problem with the
> optimisation routine or the gradient generated by mitgcmuv_ad.
> > > >>>>
> > > >>>> Andrew
> > > >>>> <out.txt>_______________________________________________
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