The transformer from sugarme

Example in readme gives wrong output

The following statement in basic example of README.md:

encodings, err := tk.EncodeBatch(input, true)

could cause two different encodings as described in tokenizer/tokenizer.go:
"EncodeBatch encodes all sentences in concurrency".
In fact, the EncodeBatch function lets the goroutines to concurrently execute the following action:

encodings = append(encodings, *e)

Therefore, since there are two sentences in the input, the encodings could be of two different orders: {sentence1, sentence2} and {sentence2, sentence1}. It is unmatched to the two statements after:

index1 := output.MustGet(0).MustGet(4).MustArgmax(0, false, false).Int64Values()[0]
index2 := output.MustGet(1).MustGet(7).MustArgmax(0, false, false).Int64Values()[0]

As mentioned above, we could either revise function EncodeBatch to make sure it returns encodings in a correct order, or recorrect the two output statements above to choose the right subtensors.

Hope this helps. I like your work very much.

This repo looks defunct: but prove me wrong :)

This looks really great! But 6 months of silence -- meanwhile transformers.py has made huge headways -- suggests this has fallen into disuse and is no longer active. But I hope I'm wrong!

"go mod tidy" error from sermo repo

I was looking to update the repo to work with the "tensorflow" package that has been renamed to "ts" one of the dependency projects but ran into this:

Clone the repo.
go mod tidy

You get a git "invalid version" error because something is trying to pull in github.com/sugarme/[email protected], and the repo seems to be private or deleted.

pipelines

create task specific pipelines in a sub-package pipeline

And API transformer.Pipeline() wrapper at transformer package.

Update to gotch 0.9.0

Would be great to update transformer to work with https://github.com/sugarme/gotch/tree/v0.9.0

For example in /transformer/util/activation.go, the MustGelu method signature changed to accept a string and a bool:

func (g GeluActivation) Fwd(x *ts.Tensor) (retVal *ts.Tensor) {
	return x.MustGelu("???", false)
                           ^^^
}

Different Result on Same BERT Embedding Model

Transformer Code

	singleEncodeInput := tokenizer.NewSingleEncodeInput(tokenizer.NewInputSequence(text))

	encodingResult, err := s.tokenizer.Encode(singleEncodeInput, true)
	if err != nil {
		return
	}

	var attentionMask []float64
	for _, attentionMaskItem := range encodingResult.AttentionMask {
		attentionMask = append(attentionMask, float64(attentionMaskItem))
	}

	tokenIdsTensor := ts.TensorFrom(encodingResult.Ids).MustTo(s.compileMode, true)
	typeIdsTensor := ts.TensorFrom(encodingResult.TypeIds).MustTo(s.compileMode, true)

	positionalIdsTensor := ts.TensorFrom(encodingResult.Words).MustTo(s.compileMode, true)
	
	// If no Clamp, Error Because -1 out of range
	positionalIdsTensor, _ = positionalIdsTensor.Clamp(ts.IntScalar(0), ts.IntScalar(s.config.VocabSize), true)

	ts.NoGrad(func() {
		sbertResult, err := s.model.ForwardT(
			tokenIdsTensor,
			typeIdsTensor, 
			positionalIdsTensor,
			ts.None,
			false,
		)
		if err != nil {
			return
		}

		meanPoolResult, err := MeanPooling(sbertResult, ts.TensorFrom(attentionMask))
		if err != nil {
			return
		}

		result = meanPoolResult.Float64Values(true)
	})

With:

MeanPooling(
	modelOutput *ts.Tensor,
	attentionMask *ts.Tensor,
) (*ts.Tensor, error) {
	squeezeMask, err := attentionMask.Unsqueeze(-1, true)
	if err != nil {
		fmt.Println("Error in Squeeze Mask Expanded", err)
		return nil, err
	}

	expandedMask, err := squeezeMask.Expand([]int64{modelOutput.MustSize()[0], 1}, false, true)
	if err != nil {
		fmt.Println("Error in Expanded Mask Expanded", err)
		return nil, err
	}

	multiple, err := modelOutput.Multiply(expandedMask, false)
	if err != nil {
		fmt.Println("Error in Multiple Embedding Value & Mask Expanded", err)
		return nil, err
	}

	multipleSum, err := multiple.SumToSize([]int64{s.config.HiddenSize}, true)
	if err != nil {
		fmt.Println("Error in Sum Multiple Embedding Value & Mask Expanded", err)
		return nil, err
	}

	fmt.Println("Multiple Sum Size", multipleSum.MustSize())

	expandedMaskSum, err := expandedMask.Sum(gotch.BFloat16, true)
	if err != nil {
		fmt.Println("Error in Sum Expanded Mask Expanded", err)
		return nil, err
	}

	expandedMaskClamp, err := expandedMaskSum.ClampMin(ts.FloatScalar(1e-9), true)
	if err != nil {
		fmt.Println("Error in Clamp Expanded Mask", err)
		return nil, err
	}

	return multipleSum.Divide(expandedMaskClamp, true)
}

Using BERT Embedding using firqaaa/indo-sentence-bert-base model with Mean Pooling, Got:

 [-1.6227916538715363,0.7554875880479812,0.1589603044645628,0.8658471912145614,-0.4249356545507908,0.01967335669323802,0.9502722173929214,-0.3677066618576646,1.1301569476723672,-1.4967026352882384,0.16354716694913804,-1.0760953173041343,-0.624056831933558,0.13163216412067413,-0.5605657094158232,-0.1305385995656252,-0.19867468923330306,1.722248911857605,-0.1994105402380228,-1.4139509856700898,0.42254706418607385,-1.0478729814291001,1.5752075016498566,-1.1409893691539765,0.4027868114331795,1.0004204362630844,0.9397359506983776,0.3373792964965105,-0.3797923713922501,-0.4052686534821987,0.7873915031552314,-1.1068729043006897,-0.5163623943924904,0.2611077643930912,1.1646993651986122,0.41107474863529203,-0.06333556547760963,-1.0262116046622396,-0.5505763769149781,-0.29493677951395514,0.5741721652448177,0.6379409968852997,0.18359720571897925,0.2578465469181538,-0.04277798626571894,-0.006689520180225372,-0.07283653393387794,0.027506009116768836,-0.5368784964084625,-0.2336610034108162,-0.6083711832761765,-1.1165205121040345,0.2659545764327049,-0.420451824599877,-0.09777965396642685,0.37623532488942146,1.376661330461502,-0.34119268879294395,-0.8356417715549469,1.204750394821167,-0.7787564300000668,-0.5885449111461639,-0.23886494506150485,-0.12483588429167866,0.43210629746317863,-0.5659979455173015,0.6688313819584437,0.38319472670555116,0.3330675795674324,0.8130357705056668,-0.4499365001916885,-1.6743723392486571,1.3455155491828918,1.3436111986637116,-0.29789138287305833,-1.1723614156246185,0.28456454649567603,0.9028494514524936,-1.0644310090690852,0.4904009073972702,-0.7448074653744697,0.3888047568500042,1.0846442807465793,-0.4661381786223501,1.146113967895508,0.2453242763876915,0.6124971672892571,-0.12931594848632813,0.4396951522678137,-0.10419512782245874,-0.1389782418962568,0.7814542919397354,-1.082936555147171,0.543551667034626,-0.45684188716113566,-1.6327427625656128,0.547798165678978,-1.0640608921647072,-0.592055407166481,0.2637485801242292,0.04996199081651866,0.259585880022496,0.5630159471184015,0.5123400680720807,1.1783067256212234,1.4461433589458466,0.3455542854964733,-0.9629830822348595,0.19565976709127425,-1.0863568186759949,-0.34217013604938984,0.8319569528102875,1.202864345908165,0.28094092253595593,-0.9778533601667732,-0.0915665116161108,-1.7865074813365935,0.9091301769018173,-0.034772072732448575,-0.4708100453019142,-0.24228798504918814,-0.43598178327083587,0.5572963342070579,-0.9968375831842422,0.4635215237736702,0.2609421290457249,-0.46491607669740914,-1.4113442786037922,-0.051442237198352815,-0.6520886942744255,0.3925953581929207,0.5854724477976561,-0.15212079808115958,-0.4005308158695698,0.5666025180369616,0.248254712857306,0.48880391418933866,0.9002874851226806,-0.8982043355703354,-0.26018930720165373,-0.43198364223353564,-0.33130444027483463,0.4112240560352802,0.19897668808698654,-0.15394992828369142,-0.1385298477485776,-1.174750530719757,1.1421632036566733,0.5469675600528717,-1.0588995605707168,-1.5501525282859803,-0.7415343614295125,-0.1770402893424034,-0.14751780927181243,0.3815243750810623,0.4742387685924768,-0.5843388440087438,0.2055481977760792,0.13543433994054793,0.061221899837255477,0.6243008613586426,1.5520382758229971,0.8577583432197571,0.21417295262217523,0.11074728509411216,0.07564935982227325,0.7405689567327499,-0.8669735431671143,0.46681671440601347,-1.536320161819458,-0.5370194882154464,0.19270511120557784,0.6078463859856129,0.9395915418863297,-0.26305809170007705,-0.22174796797335147,-1.0416114673018455,-0.853209388256073,-0.9889775365591049,-0.253652174398303,-0.39191010892391204,0.3293208733201027,1.5862296529114246,0.03622507750988006,-0.5202541723847389,-0.6523760542273521,-0.999642089009285,0.7373349595814943,-0.20583635196089745,1.130672037601471,0.9826638631522655,0.33566457591950893,-0.36206286642700436,0.8018047153949738,0.1859155386686325,0.22497576847672462,-0.04723847098648548,0.4920762531459332,-0.8198982387781143,-0.22730761589482426,0.21381990164518355,-0.31953837871551516,-0.20060425549745559,-1.104482302069664,0.7249462202191352,-1.1688048660755157,0.9511110633611679,-0.8583337485790252,-0.8150541067123414,0.3349034905433655,0.26009077318012713,0.3106609046459198,0.10342213585972786,-0.029414141178131105,-0.7427665680646897,0.8560516193509102,0.7308406054973602,-0.279021218419075,-1.0104446589946747,-0.4133190631866455,-0.46885893144644797,-0.4712497230619192,-0.20888532549142838,-0.4310174971818924,0.18832004498690366,0.08069765288382769,0.6109874784946442,-0.04693659096956253,-0.8556462123990058,0.7957041263580322,-0.023471899703145026,-1.5803398966789246,-1.1334915831685066,-0.21581769958138466,0.7068514049053192,-0.5155457347631455,-0.43313210308551786,1.4783492684364319,-0.981499956548214,0.00045935362577438356,0.030957138910889626,-0.35024763755500316,-0.47378902435302733,-0.5338902229908854,0.8176291070878505,-0.580621188879013,0.020258036628365516,0.33431983217597006,-0.38455332070589066,-0.4846494659781456,0.7687359422445297,-0.27531019002199175,-0.401981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Using BERT Embedding using firqaaa/indo-sentence-bert-base model without Mean Pooling (First Index in Tensor), Got:

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Sentence Transformer Code

from sentence_transformers import SentenceTransformer
sentences = ["Nama saya Yusyfina Yuniarti"]

model = SentenceTransformer('firqaaa/indo-sentence-bert-base')
embeddings = model.encode(sentences)
print(embeddings[0])

Using Hugging Face Sentence Transformer firqaaa/indo-sentence-bert-base, Got:

[-0.728769541,1.03070807,0.597038627,-0.724488199,1.1109004,0.367057145,-1.69312644,-0.432370961,-0.479102075,0.447933823,0.294710457,-0.476568758,-0.745833099,-0.0163534097,1.58723664,-0.292736471,1.61940897,-0.322055757,0.176826686,-0.575170398,-0.0526563823,-0.182893798,-0.732043743,-0.193100244,0.190573841,-0.586943209,1.39887476,-1.78764725,-0.936073959,-0.154629216,-0.00232271547,0.64753139,0.0663546473,0.887663722,-0.416142374,0.397735804,1.49786794,0.234046772,-0.623843729,0.572009206,-1.08866334,-0.853966534,0.00190120342,-0.315858692,0.689347386,0.455875814,0.127252415,-1.36490893,0.711537302,0.500115097,0.487224877,0.268592447,0.211649343,0.856098652,-0.0447329655,-0.0101358993,0.394373477,-0.886888027,-0.758469701,0.512086987,0.490378439,2.43307161,0.111732796,0.345883131,-0.544274449,-1.08962536,-0.620646179,0.926523387,-1.48084176,0.694925427,0.864914536,-0.426542372,1.21106458,0.515560389,0.0673334524,0.109631538,0.392081559,0.137717515,-0.667318404,-0.196232617,0.308202267,0.996181488,0.961703956,-0.326957136,0.541214287,0.0227988958,-0.348437488,1.42413735,-0.554342389,1.69492698,1.6115309,-0.733397007,-1.36865056,-0.858273864,-0.441358805,0.301705241,0.166016787,0.339023799,-0.233055398,0.649249494,-1.3350929,1.61175442,-0.194594815,0.560382724,-1.47822452,-1.17750573,-2.18012691,-1.30076694,0.345810175,-0.174260587,-1.02486825,-0.0365230553,0.148810506,-0.967721343,0.980295002,-1.12986374,-0.983318686,0.0646299571,-0.316435724,0.95693177,-0.009766954,-0.603620708,0.469835222,-2.40198946,0.513327837,1.08039689,0.400699228,0.258626252,0.366823822,0.34700194,-0.920541763,-0.430476189,-0.212324068,-0.64631021,1.61734962,-0.426103413,-0.307771385,-0.730635047,0.231489375,0.381328523,0.0886718109,-0.795271218,-0.554852128,0.597893834,1.08427835,0.764360249,-0.105325758,-0.338070929,-0.960273147,-0.471959829,-0.685437024,0.164848149,-1.62792242,1.44042611,0.181020856,0.300756216,-0.332496613,0.989802361,-0.479425669,-0.683336616,0.0553333387,-0.0631729513,0.799441934,1.4526732,-0.340206295,0.405278593,0.597343385,-0.88882637,1.18792522,-0.336030811,-0.958358109,0.739529014,-0.314184368,1.01407504,1.78445506,-0.217505187,1.57367468,0.798707485,0.149183512,-1.67905998,-1.1579926,-0.304793924,-0.489402115,-0.773307621,-0.399694085,0.280870914,1.36378324,-0.267359853,-1.65033531,-0.926304221,0.251480311,-1.35584664,-0.278774828,-1.20915508,-0.0865978077,0.00474175811,1.28428626,-0.914889693,-0.558385968,-0.483170658,0.0760708377,-1.20614624,-0.14691928,-0.0757105798,-0.888332844,1.21772885,-0.530717373,-0.528036773,0.0122616561,-1.10750651,0.736021817,-0.753809214,0.407816797,0.206235558,0.886216044,-1.00062001,0.18625921,-0.315424681,0.870782554,-1.05559707,-0.454290539,0.682997286,0.0866720676,0.779103279,-0.441071987,1.44329166,0.2653763,-0.627355516,-0.23099713,-0.209176183,0.279097438,-0.983110607,-0.907089591,-0.796043515,0.438886732,-1.25649297,1.13402379,0.308278531,-1.57490885,-1.0148319,0.643121362,-0.941913486,1.0450598,-0.832120538,0.501715541,0.107521728,0.698807478,0.837365448,0.689181864,1.0568223,-0.157781273,-0.403208643,-1.19043851,2.2801609,-0.365974575,-0.246961862,-0.798121154,0.590959668,0.0538649186,-0.282077909,-0.214157552,-0.0218678676,1.68445718,0.219645217,-0.0937952548,1.53240812,-1.69949269,0.537909567,2.07488275,-0.145033315,0.675438881,-0.361287266,-2.10395336,0.920599937,0.305064619,0.813822091,-1.69480383,0.348859847,-1.16524172,1.42593038,0.288887173,-0.274802864,-0.792757213,-0.454061359,0.404667377,-1.39712834,-0.364296049,0.0700722411,0.109201215,0.224094316,1.27386761,0.136766627,-0.135663301,0.0253491346,-0.799788952,-0.963498712,0.564341366,-0.345468938,2.52282643,0.439551413,0.0166289993,0.00336701283,0.117773674,-0.207406953,-1.16465116,0.807724476,-1.44290638,-0.321124703,0.192775249,0.312011003,-0.521700621,-0.134442046,-0.645359874,-1.6678499,-0.0944285542,-0.405213833,0.435147107,-0.793227315,0.153089494,0.257286251,-0.771237016,0.352756083,-0.561610222,-0.112235069,0.585083961,-0.780044913,-0.108352736,-0.123265579,0.26248163,1.48682272,0.189995721,-0.722001731,-0.308026373,-1.86904275,1.24657941,1.20005584,0.416530758,-0.791242719,-0.152546808,0.813379467,0.0996577963,-1.46598887,-0.110749543,0.543525398,0.614660144,0.750840724,-1.31719518,1.33560681,-0.309238762,-0.386107028,0.29648149,-1.11639178,-0.0334254727,-0.16517362,0.129936546,0.394218177,0.813289285,0.289862633,0.0304445084,0.829479575,1.59007955,0.201292425,1.09429002,0.578027129,-0.393633157,-0.270335108,0.731883168,0.396843672,-0.617126346,0.63915813,-0.676132977,1.43360972,-0.0391074643,-0.0167858433,-2.21214747,-0.373658329,-1.52301121,1.03671086,-0.975005507,0.692907274,0.276638687,-0.70737797,-0.511367679,-0.663949847,0.42901215,0.196242899,-0.00546634803,-0.0782182291,1.03464198,1.18685651,-0.983290493,-0.148377091,0.584150791,-1.52060783,-0.0226204693,-0.804868817,0.204304367,0.738552094,0.724400997,-0.0688259378,-0.258259386,-0.487994492,-1.70594561,2.77724814,-0.146072835,-0.111535132,0.593815327,0.309488565,-1.27585483,0.852240205,0.100150742,0.908684134,-0.229397565,-0.725431442,-0.135620549,0.699554145,0.468046814,-1.11318195,1.16892755,1.43818605,-1.42920756,-2.62224388,-0.0989494771,-1.22972155,-1.39280677,0.796736598,-0.604684234,0.677146792,0.0349738263,0.763561964,0.163160428,-0.54277575,-0.522984624,-1.68672085,0.438635826,-1.03538144,0.58415395,0.366744787,0.813757122,0.795464993,0.536079407,-0.377952516,0.154311493,-0.16921486,-0.205458447,0.584939599,-1.28760731,0.878001571,-0.706819654,-2.01022291,1.64731336,-1.19172537,-0.0806191042,-0.365895182,-1.5297116,2.06994915,-1.33397555,0.448670059,0.176600054,0.944906116,0.812909782,-0.0819159448,0.154557198,-0.883100033,1.22251022,-0.0769464523,0.675515294,0.897033393,0.796602249,0.395277083,-1.35935009,-0.227515548,-0.113261677,-1.50256562,0.835243404,0.990077198,-0.706494689,0.397959739,0.817661107,-0.283436149,0.938300729,0.380985796,0.734152436,-0.210968345,-1.2447046,1.2427547,0.493545383,0.448120356,0.743328631,0.368068278,-0.996080697,-0.794371247,1.2836144,-0.706647754,-1.46724737,-0.679964542,0.749995768,0.611397147,-0.452865779,-0.431475073,0.0747181028,0.260969281,-1.28478551,-0.784657657,0.145807609,0.504044175,0.329966813,1.39786005,0.822176933,-0.08945252,-0.190196529,1.05751705,-0.999282241,0.571135104,-0.892419815,-0.388410747,0.028385317,-0.14772889,0.0772391781,-1.22697961,0.277946174,-0.272798598,-0.749939144,0.406444311,0.975308597,0.835246861,-0.634159565,-0.16951704,-1.30175054,1.52183867,-2.08554888,1.6525526,-0.686420321,1.45032358,1.05336189,0.271915704,1.34345853,1.25561631,1.12044895,-0.244602963,0.312030971,0.15331845,1.01322353,0.297351062,0.582654834,-1.59378338,-0.75789237,-1.7211988,-0.458590269,-1.24758196,-1.13531661,-1.50956011,0.187723517,-1.08038068,-0.905101597,-1.24955416,-0.935046017,0.494446516,-0.535005808,-0.80595541,-0.21375367,0.977862656,-0.347039342,-2.03845263,-0.832987428,0.8676036,-1.01497841,1.53159952,0.509725094,0.261962622,-1.9713074,-0.161661521,0.805979073,1.07685411,0.359058499,0.0219900068,1.16120934,1.07788217,0.803620458,0.277541935,0.214384109,1.04378235,-1.02108479,0.703242898,-0.142739743,0.34108758,-0.0411276594,-0.330990493,1.32234263,-0.225063995,1.26802695,0.294223756,-0.303400129,0.0654654652,-0.355899751,1.07629526,0.236245066,0.0482232682,2.01075006,-0.497893572,1.01250887,0.713065684,0.530504107,-0.87463063,1.50622451,0.0338454023,-0.143252462,-0.714521527,0.857954323,0.267211974,-0.0708858967,0.0451739207,0.752122164,1.26691508,0.00222370028,0.563353181,0.0933398381,-0.603803515,0.200790375,-0.0984424129,-1.72864854,-0.858503222,0.00269610435,0.146178544,0.643018603,0.163280159,0.839584053,-0.430350959,0.166197896,-0.671090961,0.256469369,0.340761721,-0.821236968,0.32586664,0.205598027,1.0675503,0.0379331261,0.58070457,-1.28695083,0.917746544,-0.493280411,-0.460144192,0.908219337,-0.245404094,-0.860964596,-0.593600512,-0.71861279,1.07989144,-0.776190281,-0.994650006,-0.206728458,-0.462715298,-0.835745633,0.0756522417,-1.07736754,-0.11177025,1.45153201,2.46418571,1.63063276,-0.447155952,0.986142755,0.749830127,0.978344142,-0.210574791,-0.710896313,0.216160819,0.778284073,1.13490891,1.03955841,0.678692162,0.368737131,1.02859855,1.41996849,-0.258090019,-0.30130893,0.787674606,-1.05440295,-1.01637745,-1.66441083,-1.69106221,0.761142015,1.02617896,-0.262973011,0.223167494,1.03020942,-0.310333759,-0.326069713,0.918969274,-1.1214999,1.45283413,-0.331593364,0.0737351403,0.713323951,-0.240523726,-1.69688416,0.0111242533,-0.323366553,-0.0687069744,-1.06364954,1.17658615,0.447214842,1.42611599,-0.555052876,-0.969000936,-0.718675017,0.953151822,0.227617472,0.105078563,1.05417657,-1.52192688,-0.539461255,-0.272577465,0.533005834,-1.39793515,0.89386493,1.11877894,-2.00432444,1.90087891,0.532482564,0.514702797,0.441513926,1.42499733,0.0199254807,0.55358994,-0.619493127,-1.94097638,-0.340827733,0.518716693,-0.260291457,-1.53159451,-0.0554607995,-0.215524286,-0.027808547,1.59226429,-0.399910927,-0.707757115,0.975118518,-1.88227272,-0.149101168,1.8514179,-0.832739234,0.255464286,0.430327833,-0.701454222,-0.307301342,-0.61709106,0.290703148,-0.788572788,-0.581383884,-0.936832607,0.0595518239,1.97561288,-0.653161407,1.09138918,0.65674603,-1.45197105,0.322718084,-1.00390351,0.643758595,-0.241118819,0.200553179,-0.864903748,-1.31950569,-0.271951973,-0.402432144,-1.0449146,0.0887210965,-1.21201491]

Edit

Different vector result, but same accuracy for Text Similarity

roberta model

Implement Roberta model

Segmentation Fault

Segmentation fault when encode text data (BERT Embedding) in Web Server Application using Go Fiber.

Scenario:

Encode Server Error (Intermittent)
Try Encode Again!

Temporary Solution:

Reboot & Restart Server

 ┌───────────────────────────────────────────────────┐
 │                   Fiber v2.48.0                   │
 │               http://127.0.0.1:8080               │
 │       (bound on host 0.0.0.0 and port 8080)       │
 │                                                   │
 │ Handlers ............ 60  Processes ........... 1 │
 │ Prefork ....... Disabled  PID ................. 1 │
 └───────────────────────────────────────────────────┘

1  200  -  GET      /
1  200  -  GET      /assets/index-bc26e55c.js
1  200  -  GET      /assets/index-d9ef7090.css
1  200  -  GET      /src/assets/js/script.min.js
1  200  -  GET      /favicon.ico
1  200  -  GET      /favicon.ico
SIGSEGV: segmentation violation
PC=0x7f8c33bf7524 m=0 sigcode=1
signal arrived during cgo execution

goroutine 10 [syscall, 1 minutes]:
runtime.cgocall(0xf804a0, 0xc000166a90)
        /usr/local/go/src/runtime/cgocall.go:157 +0x4b fp=0xc000166a68 sp=0xc000166a30 pc=0x4b440b
github.com/sugarme/gotch/libtch._Cfunc_atg_divide(0x7f8bd800d310, 0x7f8bd800b860, 0x7f8bd800b420)
        _cgo_gotypes.go:16083 +0x45 fp=0xc000166a90 sp=0xc000166a68 pc=0xa74cc5
github.com/sugarme/gotch/ts.(*Tensor).Divide.AtgDivide.func1(0x0?, 0x7f8bd800d310?, 0x0?)
        /app/go/pkg/mod/github.com/sugarme/[email protected]/libtch/c-generated.go:4545 +0x87 fp=0xc000166ad8 sp=0xc000166a90 pc=0xbef247
github.com/sugarme/gotch/libtch.AtgDivide(...)
        /app/go/pkg/mod/github.com/sugarme/[email protected]/libtch/c-generated.go:4545
github.com/sugarme/gotch/ts.(*Tensor).Divide(0xc000599110, 0xc000599170, 0x1)
        /app/go/pkg/mod/github.com/sugarme/[email protected]/ts/tensor-generated.go:16412 +0xbe fp=0xc000166b70 sp=0xc000166ad8 pc=0xbef05e

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