[MITgcm-support] tutorial_global_oce_optim optimisation failed

Andrew McRae andrew.mcrae at physics.ox.ac.uk
Wed Jun 20 13:28:34 EDT 2018 

Damn.  After doing this, the gradient written into ecco_cost seems to be
all 0.0.  Help?

Andrew

On 19 June 2018 at 15:37, Andrew McRae <andrew.mcrae at physics.ox.ac.uk>
wrote:

> Okay, I have
> 1) copied OPENAD_OPTIONS.h from pkg/openad to the code_oad/ subfolder of
> the tutorial, changing it to define ALLOW_OPENAD_ACTIVE_READ_XY
>
> Good news: the main body of tools/OAD_support/ad_template.active_read_xy.F
> (which is wrapped in #ifdef ALLOW_OPENAD_ACTIVE_READ_XY) now appears in
> external_Dummies_cb2m_oad.f
>
> Bad news: this gives a compile error in externalDummies_cb2m_oad.f of
> "Error: Unexpected '%' for nonderived-type variable 'active_var'".  This
> seems to be because active_var is declared as a REAL(w2f__8) in
> externalDummies_cb2m_oad.f, not a type(active).  The lines of code
> corresponding to
>
>       active_var = dummy + active_var
>       dummy = active_var(1,1,1,1) + dummy
>
> don't appear in the post-processed code [optimized out by the OpenAD
> toolchain, or something else?], which is probably why active_var doesn't
> become an active variable.  Therefore, I....
>
> 2) change the type of active_var to type(active) in the post-processed
> file (yuck).  make adAll continues from where it left off, and mitgcmuv_ad
> now compiles :)
>
> (I tried changing the type of this variable in
> pkg/openad/externalDummies.F
> <https://github.com/MITgcm/MITgcm/blob/master/pkg/openad/externalDummies.F#L285>,
> but this leads to a bork in the OpenAD toolchain)
>
> I can confirm the cost function changes from iteration to iteration, and
> I'll now test if the optimization works.  Hopefully you can find a more
> permanent solution to the above.
>
> Andrew
>
> On 19 June 2018 at 13:43, Andrew McRae <andrew.mcrae at physics.ox.ac.uk>
> wrote:
>
>> The active_read_xy routine used in OpenAD mode looks suspicious:
>> https://github.com/MITgcm/MITgcm/blob/master/pkg/openad/exte
>> rnalDummies.F#L269-L296
>>
>> 1) ALLOW_OPENAD_ACTIVE_READ_XY isn't defined for
>> tutorial_global_oce_optim; I guess it should be?
>>
>> 2) This routine seems to be basically a no-op anyway?  I guess
>> active_var_file should be read into active_var, or similar?
>>
>> Andrew
>>
>> On 18 June 2018 at 18:04, Andrew McRae <andrew.mcrae at physics.ox.ac.uk>
>> wrote:
>>
>>> Not sure if you've had a chance to look at this yet... the only time I
>>> can see tmpfld2d being written to (and not just initialised to 0.0 or 1.0)
>>> is in pkg/admtlm/bypassad.F line 96.  Presumably that package isn't
>>> switched on here.  I can't see xx_hfluxm being written to at all.
>>>
>>> A few lines above, active_read_xy is called with xx_hfluxm_dummy as the
>>> last argument... should this have been xx_hfluxm, perhaps?
>>> (xx_hfluxm_dummy is a single variable, while xx_hfluxm is an array, so this
>>> probably won't work as-is...)
>>>
>>> Andrew
>>>
>>> On 13 June 2018 at 23:18, Andrew McRae <andrew.mcrae at physics.ox.ac.uk>
>>> wrote:
>>>
>>>> Okay, thank you.  If do you have any advice on debugging this, do say.
>>>> I guess you already got as far as spotting that all the terms on the RHS of
>>>> https://github.com/MITgcm/MITgcm/blob/master/pkg/ctrl/ctrl_m
>>>> ap_forcing.F#L259 are zero.
>>>>
>>>> Andrew
>>>>
>>>> On 13 June 2018 at 21:36, Patrick Heimbach <heimbach at mit.edu> wrote:
>>>>
>>>>> Andrew,
>>>>>
>>>>> I have not been able to look into this due to various other
>>>>> commitments over the last couple of months.
>>>>>
>>>>> I'll be grounded for a while in Austin starting next week, and this
>>>>> will be near the top of my ToDo list.
>>>>>
>>>>> Patrick
>>>>>
>>>>> > On Jun 13, 2018, at 12:56 PM, Andrew McRae <
>>>>> andrew.mcrae at physics.ox.ac.uk> wrote:
>>>>> >
>>>>> > MITgcm built with OpenAD is not making use of the ecco_ctrl files
>>>>> for optimcycle >= 1.  The file apparently gets read in, but the contents
>>>>> get dropped on the floor somewhere.
>>>>> >
>>>>> > Andrew
>>>>> >
>>>>> > On 13 June 2018 at 18:51, Matthew Mazloff <mmazloff at ucsd.edu> wrote:
>>>>> > Hello
>>>>> >
>>>>> > Sorry, I lost track. What needs to be debugged? Can you please
>>>>> reiterate the problem?
>>>>> >
>>>>> > Thanks
>>>>> > Matt
>>>>> >
>>>>> >
>>>>> >> On Jun 13, 2018, at 10:14 AM, Andrew McRae <
>>>>> andrew.mcrae at physics.ox.ac.uk> wrote:
>>>>> >>
>>>>> >> 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>_______________________________________________
>>>>> >> > > >>>> MITgcm-support mailing list
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>>>>> >> > > >>>
>>>>> >> > > >>>
>>>>> >> > > >>>
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