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train_qa_model.py
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train_qa_model.py
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import argparse
from typing import Dict
import logging
import torch
from torch import optim
import pickle
import numpy as np
from qa_baselines import QA_baseline, QA_lm, QA_embedkgqa, QA_cronkgqa
from qa_tempoqr import QA_TempoQR
from qa_datasets import QA_Dataset, QA_Dataset_TempoQR, QA_Dataset_Baseline
from torch.utils.data import Dataset, DataLoader
import utils
from tqdm import tqdm
from utils import loadTkbcModel, loadTkbcModel_complex, print_info
from collections import defaultdict
from datetime import datetime
from collections import OrderedDict
parser = argparse.ArgumentParser(
description="Temporal KGQA"
)
parser.add_argument(
'--tkbc_model_file', default='tcomplex.ckpt', type=str,
help="Pretrained tkbc model checkpoint"
)
parser.add_argument(
'--tkg_file', default='full.txt', type=str,
help="TKG to use for hard-supervision"
)
parser.add_argument(
'--model', default='tempoqr', type=str,
help="Which model to use."
)
parser.add_argument(
'--supervision', default='soft', type=str,
help="Which supervision to use."
)
parser.add_argument(
'--load_from', default='', type=str,
help="Pretrained qa model checkpoint"
)
parser.add_argument(
'--save_to', default='', type=str,
help="Where to save checkpoint."
)
parser.add_argument(
'--max_epochs', default=20, type=int,
help="Number of epochs."
)
parser.add_argument(
'--eval_k', default=1, type=int,
help="Hits@k used for eval. Default 10."
)
parser.add_argument(
'--valid_freq', default=1, type=int,
help="Number of epochs between each valid."
)
parser.add_argument(
'--batch_size', default=150, type=int,
help="Batch size."
)
parser.add_argument(
'--valid_batch_size', default=50, type=int,
help="Valid batch size."
)
parser.add_argument(
'--frozen', default=1, type=int,
help="Whether entity/time embeddings are frozen or not. Default frozen."
)
parser.add_argument(
'--lm_frozen', default=1, type=int,
help="Whether language model params are frozen or not. Default frozen."
)
parser.add_argument(
'--lr', default=2e-4, type=float,
help="Learning rate"
)
parser.add_argument(
'--mode', default='train', type=str,
help="Whether train or eval."
)
parser.add_argument(
'--eval_split', default='valid', type=str,
help="Which split to validate on"
)
parser.add_argument(
'--dataset_name', default='wikidata_big', type=str,
help="Which dataset."
)
parser.add_argument(
'--lm', default='distill_bert', type=str,
help="Lm to use."
)
parser.add_argument(
'--fuse', default='add', type=str,
help="For fusing time embeddings."
)
parser.add_argument(
'--extra_entities', default=False, type=bool,
help="For some question types."
)
parser.add_argument(
'--corrupt_hard', default=0., type=float,
help="For some question types."
)
parser.add_argument(
'--test', default="test", type=str,
help="Test data."
)
args = parser.parse_args()
print_info(args)
def eval(qa_model, dataset, batch_size = 128, split='valid', k=10):
num_workers = 4
qa_model.eval()
eval_log = []
print_numbers_only = False
k_for_reporting = k # not change name in fn signature since named param used in places
k_list = [1,10]
#k_list = [1,2,5, 10]
max_k = max(k_list)
eval_log.append("Split %s" % (split))
print('Evaluating split', split)
data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=False,
num_workers=num_workers, collate_fn=dataset._collate_fn)
topk_answers = []
total_loss = 0
loader = tqdm(data_loader, total=len(data_loader), unit="batches")
for i_batch, a in enumerate(loader):
# if size of split is multiple of batch size, we need this
# todo: is there a more elegant way?
if i_batch * batch_size == len(dataset.data):
break
answers_khot = a[-1] # last one assumed to be target
scores = qa_model.forward(a)
for s in scores:
pred = dataset.getAnswersFromScores(s, k=max_k)
topk_answers.append(pred)
loss = qa_model.loss(scores, answers_khot.cuda())
total_loss += loss.item()
eval_log.append('Loss %f' % total_loss)
eval_log.append('Eval batch size %d' % batch_size)
# do eval for each k in k_list
# want multiple hit@k
eval_accuracy_for_reporting = 0
for k in k_list:
hits_at_k = 0
total = 0
question_types_count = defaultdict(list)
simple_complex_count = defaultdict(list)
entity_time_count = defaultdict(list)
for i, question in enumerate(dataset.data):
actual_answers = question['answers']
question_type = question['type']
if 'simple' in question_type:
simple_complex_type = 'simple'
else:
simple_complex_type = 'complex'
entity_time_type = question['answer_type']
# question_type = question['template']
predicted = topk_answers[i][:k]
if len(set(actual_answers).intersection(set(predicted))) > 0:
val_to_append = 1
hits_at_k += 1
else:
val_to_append = 0
question_types_count[question_type].append(val_to_append)
simple_complex_count[simple_complex_type].append(val_to_append)
entity_time_count[entity_time_type].append(val_to_append)
total += 1
eval_accuracy = hits_at_k/total
if k == k_for_reporting:
eval_accuracy_for_reporting = eval_accuracy
if not print_numbers_only:
eval_log.append('Hits at %d: %f' % (k, round(eval_accuracy, 3)))
else:
eval_log.append(str(round(eval_accuracy, 3)))
question_types_count = dict(sorted(question_types_count.items(), key=lambda x: x[0].lower()))
simple_complex_count = dict(sorted(simple_complex_count.items(), key=lambda x: x[0].lower()))
entity_time_count = dict(sorted(entity_time_count.items(), key=lambda x: x[0].lower()))
# for dictionary in [question_types_count]:
for dictionary in [question_types_count, simple_complex_count, entity_time_count]:
# for dictionary in [simple_complex_count, entity_time_count]:
for key, value in dictionary.items():
hits_at_k = sum(value)/len(value)
s = '{q_type} \t {hits_at_k} \t total questions: {num_questions}'.format(
q_type = key,
hits_at_k = round(hits_at_k, 3),
num_questions = len(value)
)
if print_numbers_only:
s = str(round(hits_at_k, 3))
eval_log.append(s)
eval_log.append('')
# print eval log as well as return it
for s in eval_log:
print(s)
return eval_accuracy_for_reporting, eval_log
def append_log_to_file(eval_log, epoch, filename):
f = open(filename, 'a+')
now = datetime.now()
dt_string = now.strftime("%d/%m/%Y %H:%M:%S")
f.write('Log time: %s\n' % dt_string)
f.write('Epoch %d\n' % epoch)
for line in eval_log:
f.write('%s\n' % line)
f.write('\n')
f.close()
def train(qa_model, dataset, valid_dataset, args,result_filename=None):
num_workers = 5
optimizer = torch.optim.Adam(qa_model.parameters(), lr=args.lr)
optimizer.zero_grad()
batch_size = args.batch_size
data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers,
collate_fn=dataset._collate_fn)
max_eval_score = 0
if args.save_to == '':
args.save_to = 'temp'
if result_filename is None:
result_filename = 'results/{dataset_name}/{model_file}.log'.format(
dataset_name = args.dataset_name,
model_file = args.save_to
)
checkpoint_file_name = 'models/{dataset_name}/qa_models/{model_file}.ckpt'.format(
dataset_name = args.dataset_name,
model_file = args.save_to
)
# if not loading from any previous file
# we want to make new log file
# also log the config ie. args to the file
if args.load_from == '':
print('Creating new log file')
f = open(result_filename, 'a+')
now = datetime.now()
dt_string = now.strftime("%d/%m/%Y %H:%M:%S")
f.write('Log time: %s\n' % dt_string)
f.write('Config: \n')
for key, value in vars(args).items():
key = str(key)
value = str(value)
f.write('%s:\t%s\n' % (key, value))
f.write('\n')
f.close()
max_eval_score= 0.
print('Starting training')
for epoch in range(args.max_epochs):
qa_model.train()
epoch_loss = 0
loader = tqdm(data_loader, total=len(data_loader), unit="batches")
running_loss = 0
for i_batch, a in enumerate(loader):
qa_model.zero_grad()
# so that don't need 'if condition' here
# scores = qa_model.forward(question_tokenized.cuda(),
# question_attention_mask.cuda(), entities_times_padded.cuda(),
# entities_times_padded_mask.cuda(), question_text)
answers_khot = a[-1] # last one assumed to be target
scores = qa_model.forward(a)
loss = qa_model.loss(scores, answers_khot.cuda())
loss.backward()
optimizer.step()
epoch_loss += loss.item()
running_loss += loss.item()
loader.set_postfix(Loss=running_loss/((i_batch+1)*batch_size), Epoch=epoch)
loader.set_description('{}/{}'.format(epoch, args.max_epochs))
loader.update()
print('Epoch loss = ', epoch_loss)
if (epoch + 1) % args.valid_freq == 0:
print('Starting eval')
eval_score, eval_log = eval(qa_model, valid_dataset, batch_size=args.valid_batch_size, split=args.eval_split, k = args.eval_k)
if eval_score > max_eval_score:
print('Valid score increased')
save_model(qa_model, checkpoint_file_name)
max_eval_score = eval_score
# log each time, not max
# can interpret max score from logs later
append_log_to_file(eval_log, epoch, result_filename)
def save_model(qa_model, filename):
print('Saving model to', filename)
torch.save(qa_model.state_dict(), filename)
print('Saved model to ', filename)
return
if args.model != 'embedkgqa': #TODO this is a hack
tkbc_model = loadTkbcModel('models/{dataset_name}/kg_embeddings/{tkbc_model_file}'.format(
dataset_name = args.dataset_name, tkbc_model_file=args.tkbc_model_file
))
else:
tkbc_model = loadTkbcModel_complex('models/{dataset_name}/kg_embeddings/{tkbc_model_file}'.format(
dataset_name = args.dataset_name, tkbc_model_file=args.tkbc_model_file
))
if args.mode == 'test_kge':
utils.checkIfTkbcEmbeddingsTrained(tkbc_model, args.dataset_name, args.eval_split)
exit(0)
train_split = 'train'
test = args.test
if args.model == 'bert' or args.model == 'roberta':
qa_model = QA_lm(tkbc_model, args)
dataset = QA_Dataset_Baseline(split=train_split, dataset_name=args.dataset_name)
#valid_dataset = QA_Dataset_baseline(split=args.eval_split, dataset_name=args.dataset_name)
test_dataset = QA_Dataset_Baseline(split=test, dataset_name=args.dataset_name)
elif args.model == 'embedkgqa':
qa_model = QA_embedkgqa(tkbc_model, args)
dataset = QA_Dataset_Baseline(split=train_split, dataset_name=args.dataset_name)
#valid_dataset = QA_Dataset_baseline(split=args.eval_split, dataset_name=args.dataset_name)
test_dataset = QA_Dataset_Baseline(split=test, dataset_name=args.dataset_name)
elif args.model == 'cronkgqa' and args.supervision != 'hard':
qa_model = QA_cronkgqa(tkbc_model, args)
dataset = QA_Dataset_Baseline(split=train_split, dataset_name=args.dataset_name)
#valid_dataset = QA_Dataset_baseline(split=args.eval_split, dataset_name=args.dataset_name)
test_dataset = QA_Dataset_Baseline(split=test, dataset_name=args.dataset_name)
elif args.model in ['tempoqr', 'entityqr', 'cronkgqa']: #supervised models
qa_model = QA_TempoQR(tkbc_model, args)
if args.mode == 'train':
dataset = QA_Dataset_TempoQR(split=train_split, dataset_name=args.dataset_name, args=args)
#valid_dataset = QA_Dataset_TempoQR(split=args.eval_split, dataset_name=args.dataset_name, args=args)
test_dataset = QA_Dataset_TempoQR(split=test, dataset_name=args.dataset_name, args=args)
else:
print('Model %s not implemented!' % args.model)
exit(0)
print('Model is', args.model)
if args.load_from != '':
filename = 'models/{dataset_name}/qa_models/{model_file}.ckpt'.format(
dataset_name=args.dataset_name,
model_file=args.load_from
)
print('Loading model from', filename)
qa_model.load_state_dict(torch.load(filename))
print('Loaded qa model from ', filename)
else:
print('Not loading from checkpoint. Starting fresh!')
qa_model = qa_model.cuda()
if args.mode == 'eval':
score, log = eval(qa_model, test_dataset, batch_size=args.valid_batch_size, split=args.eval_split, k = args.eval_k)
exit(0)
result_filename = 'results/{dataset_name}/{model_file}.log'.format(
dataset_name=args.dataset_name,
model_file=args.save_to
)
train(qa_model, dataset, test_dataset, args,result_filename=result_filename)
# score, log = eval(qa_model, test_dataset, batch_size=args.valid_batch_size, split="test", k=args.eval_k)
# log=["######## TEST EVALUATION FINAL (BEST) #########"]+log
# append_log_to_file(log,0,result_filename)
print('Training finished')