BanditLearningforNMTHyperparameter Search

KevinDuh

May2018discussion

1

SpeedingupHyperparameter Search

Givenbudgetconstraints,howtodecidewhichruntokillbeforeconvergence?

2

K-armbanditproblem

- Eachrun/modelisanarm- Eachtimewepullanarm,wetrainthemodelbyonestep

- Whicharmshouldwepullfirst?

3

Simulation

• WNMT2018DE-ENdata(4Msentences)• RunKmodelstoconvergence.Checkifbanditlearningcanchoosecorrectly.

• Seq2SeqHyperparameters:– Varied=BPE:10k,30k,50k;Embeddingsize:100,300,500;RNNhiddensize:100,300,500;#layers:1,2;Dropout:0.0-0.4

– Fixed=Defaultoptimizer,learning-ratescheduler• Checkpointfrequency:10k,Batchsize:128– Eachcheckpoint=1unitofbudget

4

Epsilon-GreedyAlgorithm

• Foreachturnuntilbudgetrunsout:– Drawxfromrandom_uniform(0,1)– Ifx<epsilon(e.g.0.1)

• Pullrandomarm

– Else:• Pullbestarm:k’=argmax_k value[k]• Updatevalue[k’]=latestBLEU(oraveragesofar)

5

Epsilon-Greedytendstoexploreonlymodelsthataregoodinitially.OKherebutrisky.(budget=40)

6

UpperConfidenceBound(UCB)

• Idea:moreuncertaintyonarmslesspulled,sofavorthem.

• Foreachturnuntilbudgetrunsout:– Foreacharmk:

• Computebound[k]=sqrt(2log(totalcount)/count[k])– Pullbestarm:k’=argmax_k value[k]+bound[k]– Updatevalue[k’]=latestBLEU(oraveragesofar)– Incrementcount[k’]+=1;totalcount +=1

7

Boundistoolargeinpractice.UCBuniformlyexploresallarms.(Budget=40)

8

Hyperband/SuccessiveHalvingLiet.al.2016.Hyperband:ANovelBandit-BasedApproachtoHyperparameter Optimization

• Previously:choosing1armistoorisky,andvaluesaren’tfairlycomparableacrosssteps

• Idea:Choosehalfofpopulationateachturn

• L =list(Arms)• Foreachturnuntilbudgetrunsout:– PulleacharmkinL;updatevalue[k]=currentBLEU– S =[armssortedbyvalue]– L=tophalfofS

9

PromisingarmstrainsuccessivelylongerunderSuccessiveHalving(Budget=40)

10

16arms.SuccessiveHalvingwithBudget=96.

11

Considerations/Discussions

• Nextsteps:– IncludemultipleobjectivesviaParetoranks–Makethismorepractical.Implementsuccessivehalvingasinnerloopwithinevolutionarysearch

• Algorithmicquestions:– Fixedoptimizer&learningratescheduler– Newrunvsoldrun

12

Considerations/Discussions

• Implementationquestions:– ContinueonfinishedrunforSockeye:

• --params,--source-vocab,--target-vocab• Differentdatasets?E.g.smallerdatasets• Sockeye.prepared_data?

–Measurements:• Time:CPUdecoding?VsGPUdecoding• Accuracy:validationBLEUvstrainperplexity

13

14

SuggestionsfromMichael

• Tryvastlydifferentarchitecturesandrepeatthesimulation

• Trydifferentoptimizers– ADAMforlargedata- longruns,lookingattrainingperplexity,decreasinglearningrateslowlyby0.9

– EVEforsmalldata– NADAMdoesn’twork,butinterestingtotry

• CPUdecoder:lookatWNMT’18DockerforMKLversionthatismoreperformant

15

SuggestionsforRudolphe

• Initialcondition:maybeIneedtotraineacharmlongerinitiallybeforestartingtheK-armbandits

• ButwhatifIhavetoomanyK?

16

June2018discussion

17

TEDDE-EN– differentoptimizers{adadelta,adagrad,adam,eve,nadam,rmsprop,sgd}x

initiallearningrate={0.0002,0.001}

batch_size=4096schedule=plataeu-reducelearning_rate_reduce_factor=0.7loss="cross-entropy”checkpoint=750(~1epoch)

Totalresource=56

18

Sameaslastslide,butevaluateat10checkpointintervals(750x10updates)

Totalresource=280

Increasingresourceusageà safer19

Initiallearning rate ValidationPerplexity

ValidationBLEU

Adadelta1 0.0002 19.17 23.27

Adadelta2 0.001 17.24 25.02

Adam1b 0.0002 20.68 24.53

Adam1 0.0002 20.41 24.25

Adam2b 0.001 17.18 25.68

Adam2 0.001 19.14 25.24

Eve1 0.0002 14.83 27.39

Eve2 0.001 40.06 12.59

Nadam1 0.0002 20.89 24.24

Nadam2 0.001 15.43 26.97

RMSprop1 0.0002 19.08 24.55

RMSprop2 0.001 16.10 27.00

adagrad 0.001,0.0002 411, 195

sgd 4171,66220

WMTZH-EN– differencearchitecture

21

WMTRU-EN– differentarchiecture

22

SuggestionsfromMichael

• Differentbatchsize• Differentscheduler(sqrt)• Mixarchitectures(&differentencoder/decoderdepths&layersize)

• BLEU– howclosetothebestmodel,i.e.canIgetto0.2BLEUofbestmodelwith10%oftheresources?

23

July2018discussion

24

MoreexperimentstoverifyHyberband’s robustness

• Motivation:– PreviousHyperband resultswerepromising,butwanttotestonmorediverse(e.g.crisscrossing)learningcurves

• Thismonth:– Curriculumlearningexperiments

25

CurriculumLearning

• Hunch:– Startbytrainingeasysamples– Asmodelimproves,addinhardersamples– Maybemodelwillconvergefaster?OrbetterBLEU?

• SockeyeImplementation(atMTMA):– Easy/hardsamplesareassignedtodifferentshards– Schedulewhatshardisvisibletotraineratwhattime

26

CurriculumLearning- Visualization

TrainingTimei.e.updates

VeryEasy

Hard

Startwitheasyshard

VeryHard

Easy MidLevel

Graduallyaddhardershards

CurriculumUpdateFrequencye.g.every1000updates

Atthispoint,seealldataandgetrandombatches

Visible(i.e.available)shards27

CurriculumLearning– manyvariantstogetdifferentlearningcurves

• Differentschedules,e.g.

• Differentdefinitionsofeasy/hard:– Sentencelength– Vocabularyfrequency– Force-decode/1bestscoreofexistingmodel

• Differentcurriculumupdatefrequency28

Setup

• Data:De-EnTED• Preparation:– 100trainingrunswithdifferentcurriculumlearningsetting

– Randomlydraw16runseachtimeandobserveHyberband results

• Question:– CanHyperband correctlybetonnear-bestruns?

29

30

31

RankHistogram(100randomtrials)

8120/64[19,19,20,18,14,5,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]8240/128[26,23,20,14,10,2,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]8360/192[42,31,14,9,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]8480/256[43,23,18,10,5,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]16148/256[16,11,10,10,14,12,11,3,6,2,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]16296/512[41,20,14,8,1,8,0,5,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]163144/768[46,23,12,3,6,7,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]164192/1024[47,21,8,6,7,4,3,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]321112/1024[28,11,7,5,7,4,9,5,2,3,1,3,0,1,0,2,0,2,3,1,1,0,0,0,2,2,1,0,0,0]322224/2048[45,21,11,2,1,3,3,4,5,3,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]323336/3072[101,61,14,6,2,4,2,1,2,3,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]324448/4096[56,33,1,4,1,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]641256/4096[10,22,27,14,1,1,0,1,0,0,2,0,0,2,3,0,3,3,3,4,0,0,2,0,2,0,0,0,0,0]642512/8192[19,32,18,18,8,3,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0]

#run

Halving_freq Resourceusedvs gridsearch

In19/100trials,Hyberband choserank1(best)curveIn20/100trials,Hyberband choserank3curve

32

Summary

• FoundrelativelyrobustsettingsforHyberbandonNMTlearningcurves

• Next:tryondifferentNMTarchitecturesandincorporatespeed/accuracymulti-objective

• (Nextmeeting:SeptemberratherthanAugust?CurrentlydoingsummerworkshoponDomainAdaptationforNMT)

33

JHUHLTCOESCALE2018Workshop:ResilientMachineTranslation

forNewDomains

KevinDuh,PaulMcNamee,KathyBaker,PhilippKoehn,BrianThompson,ChrisCallison-Burch,

JanNiehues,MarineCarpuat,TimAnderson,JeremyGwinnup,MariannaMartindale,Jenn Drexler,

Calandra Moore,StevenBradtke,JamesWoo,Gaurav Kumar,HudaKhayrallah,PamelaShapiro,BeckyMarvin,JonathanWeese,Dusan Varis

FinalPresentation:Baltimore,August9(savethedate!)

Goal:ImproveDomainAdaptationofNMT

Test TrainingdataforNMT Ar-En De-En Fa-En Ko-En Ru-En

Zh-En

TEDTalks

GeneralDomain 29.6 34.6 22.2 11.6 23.4 15.9InDomain(TED) 27.4 32.3 21.3 14.4 22.9 16.2ContinuedTraining 35.4 39.9 27.9 17.2 28.6 20.4

Patent GeneralDomain n/a 36.0 n/a 2.7 23.4 12.6In-Domain(Patent) n/a 61.9 n/a 29.9 26.9 40.2Continued Training n/a 62.3 n/a 31.7 37.0 43.7

35BLEUscores:ContinuedTraininggivesconsistentgains(~0.5-5BLEU)

LargeGeneral-DomainBitext In-DomainBitext:e.g.patents

GENERALMODEL

ADAPTEDMODEL

1.Train3.ContinueTraining

2.Initialize

SuggestionsfromMichael

• Quantifyresourcessaved:– whatpercentageofresourcescanwesavevs gridsearchwhileachievingsimilarmodels

• Whatwerethebadexamplesinthehistogramcurve?

• PullRequestforCurriculumLearningcode

36

September2018discussion

37

Summarysofar

38

Withbanditlearning,wecansaveX%ofresourceswhileachievinglessthanYdegradationinBLEU.(Here,X=81%,Y=0)

Openquestion:- Resultsfordrastically

differentarchitectures

Otherdirections

39

• Methodsforspeedinguptraining(i.e.inner-loopofhyperparameter optimization)– Banditlearning– Datasub-selectionfortrainingspeedup

• Methodsforspeedingupmodelsorreducingresourceusageduringinferenceingeneral–Modelcompression

Datasubsetselection:Formulation

• TrainingdataT:Nsamples• CanweselectsubsetSofM<<Nsamples– Wheretrainingonsubsetgivessamehyperparametersearchrecommendationsastrainingonfullset?

• Formulation:1. TrainKmodelswithdifferenthyperparameters onT2. SimilarlytheKmodelstrainonsubsetS3. Comparetherankingof(1)and(2).Ifsame,then

datasubsetisagoodsurrogate

40

Datasubsetselection:Details

• Baseline1:TrainonT asusual,withuptosametrainingtimeasM*#epoch

• Baseline2:Flipthesubsetselectioncriteria• Subsetselectionmethod:– Cynicaldataselection– Vocabularybasedselection

• Evaluation:– Howtointerpretrankingdifferences?

41

Modelcompression

• Focusmoreoninferenceresourceconstraints• Existingideastoexplore:–ModelDistillation– Quantization– (Discussion)

• Comparespeed,memoryfootprint• Integratethisinlargerauto-tuningloop

42

Discussionnotes(withMichael)• Datasubsetselection:

– It’dbegoodtohavealltheplots– informativewhetherresultsaregoodorbad

– Forvocabselection:currentlysomethingsimilarisdoneinunittests.(Replacemostvocabwithunk)

• Modeldistillation:– Trainbigmodelandtranslatetrainingdata.Trainsmallmodelandthencontinuetrainingonbigmodel’soutputs.Thismaybesufficient(noneedforoutputdistributionasIoriginallyimagined)

• Quantization:– MichaelwillhelplookforpointersonquantizationworkinMxNet

43

44

• Next:exploringaneworthogonaldirectionforspeedinguphyperparameter search

45

DataSubsetSelectionforNMTHyperparameter Search

KevinDuh

Motivation

• Ittakestimetotrainmodelstoconvergenceonlargedatasets

• Question:Canwetrainmodelstoconvergenceonasmalldataset?– E.g.inapaperlongtimeago,Lecun suggestsfiddlinglearningrateonsmallsubsetfirst

–Whatsubsetleadstofastconvergence?– Doestherankingofhyperparameters onsmallsubsetcorrelatewiththatonthefulldataset?

47

Datasubsetselection:Formulation

• TrainingdataT:Nsamples• CanweselectsubsetSofM<<Nsamples– Wheretrainingonsubsetgivessamehyperparametersearchrecommendationsastrainingonfullset?

• Formulation:1. TrainKmodelswithdifferenthyperparameters onT2. SimilarlytheKmodelstrainonsubsetS3. Comparetherankingof(1)and(2).Ifsame,then

datasubsetisagoodsurrogate

48

PreliminaryExperiments• Big:Modeltrainedon28millionsentencepairsofgeneral-domainDE-EN– Approx 300k-500kupdatestoconverge

• Small:Modeltrainedonrandomlyselected10%ofdata– Approx 150k-300kupdates(30-60hours)toconverge

• Vocab:Modeltrainedonsentencescontainingonlythetop1/256vocabulary– Approx 100k-200kupdatestoconverge

à Vary#layers,size,etc.andseeifrankingisthesameforBigvsSmallandBigvsVocab

49

Datasubsetselectionbyvocabulary

50

LSTMvs Transformer,Layer=1,2,4

51

Changinglearningrates

52

ConnectionstoBanditLearning

• Bandit:Stopsomerunsbeforeconvergence• DataSelection:Shortertimetoconvergence• Thesearebothheuristicsonearlystoppingsomemodelsduringhyperparameter search

• Nextstep:– Collectmoreempiricalresults– Experimentwithotherdataselectionmethods

53

Discussionnotes(withMichael)

• Plotallresultsonsamefigure• Tryevensmallerdatasetsandseewhenrankingstartstobreak

• Experimentonatleastonemoredataset• Aswrap-up,findgeneralrecommendations:basedonsomedatasetcharacteristic,whatspeed-upmethodtouse?

54