Commit 1bb38a02 authored by kulcsar's avatar kulcsar
Browse files

doc changes in main and train

parent 3017363c

Code/train.py

+20 −82
Original line number Diff line number Diff line
@@ -22,16 +22,8 @@ torch.cuda.empty_cache() 


#with torch.autocast("cuda"):



def train(model, name, imdb, seed,gradient_accumulation_steps,mixup, threshold, lambda_value, mixepoch, tmix, mixlayer, train_dataset, test_dataset, num_epochs, learning_rate, batch_size, test_batch_size):

	"""Write Train loop for model with certain train dataset"""

	#set_seed(seed)

	#if model_name[0] == "b":

	#	model=BertForWordClassification.from_pretrained(model_name).to("cuda")

	#elif model_name[0] == "r":

	#	model=RobertaForWordClassification.from_pretrained(model_name),to("cuda")

	print("batch size: ", batch_size)

	print("test batch size: ", test_batch_size)

	print("mix up: ", mixup)

def train(model, name, imdb, seed,mixup,lambda_value, mixepoch, tmix, mixlayer, train_dataset, test_dataset, num_epochs, learning_rate, batch_size, test_batch_size):

	"""Train loop for models. Iterates over epochs and batches and gives inputs to model. After training, call evaluation.py for evaluation of finetuned model."""

	model.train().to("cuda")

	train_sampler = RandomSampler(train_dataset)

	train_dataloader=DataLoader(train_dataset, sampler=train_sampler, batch_size=batch_size)
@@ -41,14 +33,7 @@ def train(model, name, imdb, seed,gradient_accumulation_steps,mixup, threshold, 
	lr_scheduler=get_scheduler(name="linear", optimizer=optimizer, num_warmup_steps=10, num_training_steps=num_training_steps)



	model.zero_grad()

	#progress_bar=tqdm(range(num_training_steps))

	print("imdb: ", imdb)

	print("tmix: ", tmix)

	print("mixlayer:", mixlayer)

	for epoch in range(num_epochs):

		#for param_tensor in model.state_dict():

		#	print(param_tensor, "\t", model.state_dict()[param_tensor])

		print("Epoche: ", epoch)

		index=0

		

		for batch in train_dataloader:
@@ -126,56 +111,29 @@ def train(model, name, imdb, seed,gradient_accumulation_steps,mixup, threshold, 
				start_positions=batch[2]

				end_positions=batch[3]

			outputs=model(**inputs)

			#print("outputs: ", outputs)

			#print("outputs 0: ", outputs[0])

			loss=outputs[0]

			#print("length of outputs; ", len(outputs))

			#for i in range(len(outputs)):

			print("Loss: ", loss)

			if mixup == True: #and epoch>=mixup_epoch-1:

				#loss.backward(retain_graph=True)

			loss.backward()

				#print("epoch: {0}, retained".format(epoch))

			else:

				loss.backward()

			#if (index+1)%gradient_accumulation_steps==0:

			optimizer.step()

			lr_scheduler.step()

			optimizer.zero_grad()

			model.zero_grad()

			#	#	print("outputs {0}: {1}".format(i, outputs[i].size()))

			if epoch==mixepoch: #also make choosing epoch for tmix available



			if epoch==mixepoch:

				print("mixepoch")

				if mixup == True:

					print("length of outputs: ", len(outputs))

					print("outputs: ", outputs)

					#calculate new last hidden states and predictions(logits)

					new_matrix_batch, new_labels_batch = mixup_function(outputs[2], labels, lambda_value, threshold)

               		#for matrix in new_matrix_batchi

					print("new matrix batch size: ", new_matrix_batch.size())



					new_matrix_batch.to("cuda")

					new_labels_batch.to("cuda")



					span_output=torch.randn(new_matrix_batch.shape[0], new_matrix_batch.shape[-1]).to("cuda")

					for i in range(new_matrix_batch.shape[0]):

						span_output[i]=new_matrix_batch[i][start_positions[i]:end_positions[i]].mean(dim=0)

					#print("span output size: ", span_output.size())

					#print("span output: ", span_output)

					logits=model.classifier(span_output.detach()) #target_value?

						

					#print("logits: ", logits)

					print("logits shape: ", list(logits.shape))

					# print("Newlabels: ", new_labels_batch)

					print("labels shape: ", list(new_labels_batch.shape))



					logits=model.classifier(span_output.detach())

					logits = logits.view(-1, 2).to("cuda")

					print("logits: ", logits)

					target = new_labels_batch.view(-1).to("cuda")

					print("Newlabels: ", new_labels_batch)

					loss_2 = cross_entropy(logits, target, lambda_value)

					#loss_2 = SoftCrossEntropyLoss(logits.view(-1, 2).to("cuda"), new_labels_batch.view(-1).to("cuda"))

					#loss_2 = torch.nn.functional.cross_entropy(preds, target.long())

					print("MixUp Loss: ", loss_2)

					

					#update entire model

					loss_2.backward()

					optimizer.step()
@@ -183,20 +141,13 @@ def train(model, name, imdb, seed,gradient_accumulation_steps,mixup, threshold, 
					optimizer.zero_grad()

					model.zero_grad()

	



				#print(outputs[2].size())



			#print(outputs[0].size())

			#progress_bar.update(1)

		#print("one epoch done")

	

	torch.save(model, "./saved_models/bert_baseline.pt")



	#print(model_name)

	#evaluate trained model

	evaluation_test = evaluation.evaluate_model(model, name,  test_dataset, learning_rate, test_batch_size, imdb)

	evaluation_train = evaluation.evaluate_model(model, name, train_dataset, learning_rate, test_batch_size, imdb)

 

	print("DEV: ", evaluation_test)

	print("TEST: ", evaluation_test)

	print("TRAIN: ", evaluation_train)



	return evaluation_test, evaluation_train
@@ -243,49 +194,36 @@ def cross_entropy(logits, target, l): 






def mixup_function(batch_of_matrices, batch_of_labels, l, t):

def mixup_function(batch_of_matrices, batch_of_labels, l):

	"""Function to perform mixup on a batch of matrices and labels with a given lambda

	"""

	runs = math.floor(batch_of_matrices.size()[0]/2)

	counter=0

	results=[]

	result_labels=[]

	for i in range(runs):

		print("doing interpolation with lambda: {0} and threshold: {1}...".format(l, t))

		#get matrices and labels out of batch

		matrix1=batch_of_matrices[counter]

		label1=batch_of_labels[counter]

		matrix2=batch_of_matrices[counter+1]

		label2=batch_of_labels[counter+1]

		new_matrix, new_label=interpolate(matrix1, label1, matrix2, label2, l, t)



		#do interpolation

		new_matrix=matrix1*l + (1-l)*matrix2

		new_label=l*label1 + (1-l)*label2

		

		if new_matrix != None:

			results.append(new_matrix)

			result_labels.append(new_label)

		counter+=2

	results=torch.stack(results)

	result_labels= torch.stack(result_labels) #torch.LongTensor(result_labels)

	#print("mixup done")

	return results, result_labels



def interpolate(matrix1, label1, matrix2, label2, l, threshold):

	new_matrix=(matrix1*l)+(matrix2 * (1-l))

	new_label=(label1*l)+(label2*(1-l))



	#if new_label > 0.5+threshold:

	#	new_label=1

	#elif new_label < 0.5-threshold:

	#	new_label=0

	#else:

	#	print("in undefinded zone")

	#	return None, None

	return new_matrix, new_label#torch.tensor([new_label])

	

def train_salami(model, seed, train_set, test_set, batch_size, test_batch_size, learning_rate, epochs):

	"""Train loop of the salami group"""

	results=[]

	#for num_run, seed in enumerate(random.sample(range(1, 100), num_runs)):

		#if model_name[0]=="b":

		#	model=BertForWordClassification.from_pretrained(model_name)

		#else:

		#	model=RobertaForWordClassification.from_pretrained(model_name)



		#set_seed(seed)

	training_args = TrainingArguments(

		output_dir="./results",  # output directory

		num_train_epochs=epochs,  # total # of training epochs

main.py

+5 −30
Original line number Diff line number Diff line
@@ -11,10 +11,6 @@ from typing import List 




def run(raw_args):

	#print("parsing")

	#args=_parse_args(raw_args)

	#print("parsed arguments")



	#load test and train dataset as well as tokenizers and models...



	#Datasets
@@ -58,9 +54,6 @@ def run(raw_args): 
		print("train dataset preprocessing ")        

		print(args.tcontext)

		train_dataset=preprocess.tokenizer_new(tokenizer, data_train, args.max_length, masked=args.masking, old_dataset=args.tcontext)

		

		#print("test dataset preprocesssing ")

		#print(args.vcontext)

		test_dataset=preprocess.tokenizer_new(tokenizer, data_test, args.max_length, masked=args.masking, old_dataset=False) 

	

	elif args.tokenizer=="li":
@@ -70,15 +63,16 @@ def run(raw_args): 
	else:

		print("non eligible tokenizer selected")



	#train...

	#train&evaluate...

	print("training..")

	if args.train_loop=="swp":

		evaluation_test, evaluation_train = train.train(model, args.architecture, args.imdb, args.random_seed, args.gradient_accumulation_steps, args.mix_up, args.threshold, args.lambda_value, args.mixepoch, args.tmix, args.mixlayer, train_dataset, test_dataset, args.epochs, args.learning_rate, args.batch_size, args.test_batch_size)

		evaluation_test, evaluation_train = train.train(model, args.architecture, args.imdb, args.random_seed, args.mix_up, args.lambda_value, args.mixepoch, args.tmix, args.mixlayer, train_dataset, test_dataset, args.epochs, args.learning_rate, args.batch_size, args.test_batch_size)

	elif args.train_loop=="salami":

		evaluation_test = train.train_salami(model,args.random_seed, train_dataset, test_dataset, args.batch_size, args.test_batch_size, args.learning_rate, args.epochs)

	else:

		print("no eligible train loop selected")

	#(evaluate... is done internally) but could maybe be implemented here to make average over multiple random seeds

	

	#save

	if isinstance(args.save_directory, str): 

		with open(args.save_directory, "x") as f:

			f.write(str(args))
@@ -139,24 +133,19 @@ if __name__ == "__main__": 
		"-tc",

		"--tcontext",

		action="store_true",

		#default=False,

		#type=bool,

		help="whether or not to preprocess train set with context")



	parser.add_argument(

		"-vc",

		"--vcontext",

		#default=False,

		#type=bool,

        action="store_true",

		help="whether or not to preprocess the test set with context")



	parser.add_argument(

		"--masking",

		#default=False,

		#type=bool,

		action="store_true",

		help="whether or not to mask the target word")

	

	parser.add_argument(

		"-max",

		"--max_length",
@@ -196,26 +185,12 @@ if __name__ == "__main__": 
		type=int,

		default=32)



	parser.add_argument(

		"-gras",

		"--gradient_accumulation_steps",

		help="gradient accumulation steps for training",

		type=int,

		default=1)



	parser.add_argument(

		"-mixup",

		"--mix_up",

		help="whether or not to apply mixup during training",

		action="store_true")



	parser.add_argument(

		"-threshold",

		"--threshold",

		help="specifies the value for mixup threshold",

		type=float,

		default=0.05)



	parser.add_argument(

		"-lambda",

		"--lambda_value",