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main.py
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main.py
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import os
import time
import torch
import argparse
from model import SASRec
from utils import *
def str2bool(s):
if s not in {'false', 'true'}:
raise ValueError('Not a valid boolean string')
return s == 'true'
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', required=True)
parser.add_argument('--train_dir', required=True)
parser.add_argument('--batch_size', default=128, type=int)
parser.add_argument('--lr', default=0.001, type=float)
parser.add_argument('--maxlen', default=200, type=int)
parser.add_argument('--hidden_units', default=50, type=int)
parser.add_argument('--num_blocks', default=2, type=int)
parser.add_argument('--num_epochs', default=1000, type=int)
parser.add_argument('--num_heads', default=1, type=int)
parser.add_argument('--dropout_rate', default=0.2, type=float)
parser.add_argument('--l2_emb', default=0.0, type=float)
parser.add_argument('--device', default='cuda', type=str)
parser.add_argument('--inference_only', default=False, type=str2bool)
parser.add_argument('--state_dict_path', default=None, type=str)
args = parser.parse_args()
if not os.path.isdir(args.dataset + '_' + args.train_dir):
os.makedirs(args.dataset + '_' + args.train_dir)
with open(os.path.join(args.dataset + '_' + args.train_dir, 'args.txt'), 'w') as f:
f.write('\n'.join([str(k) + ',' + str(v) for k, v in sorted(vars(args).items(), key=lambda x: x[0])]))
f.close()
if __name__ == '__main__':
u2i_index, i2u_index = build_index(args.dataset)
# global dataset
dataset = data_partition(args.dataset)
[user_train, user_valid, user_test, usernum, itemnum] = dataset
# num_batch = len(user_train) // args.batch_size # tail? + ((len(user_train) % args.batch_size) != 0)
num_batch = (len(user_train) - 1) // args.batch_size + 1
cc = 0.0
for u in user_train:
cc += len(user_train[u])
print('average sequence length: %.2f' % (cc / len(user_train)))
f = open(os.path.join(args.dataset + '_' + args.train_dir, 'log.txt'), 'w')
f.write('epoch (val_ndcg, val_hr) (test_ndcg, test_hr)\n')
sampler = WarpSampler(user_train, usernum, itemnum, batch_size=args.batch_size, maxlen=args.maxlen, n_workers=3)
model = SASRec(usernum, itemnum, args).to(args.device) # no ReLU activation in original SASRec implementation?
for name, param in model.named_parameters():
try:
torch.nn.init.xavier_normal_(param.data)
except:
pass # just ignore those failed init layers
model.pos_emb.weight.data[0, :] = 0
model.item_emb.weight.data[0, :] = 0
# this fails embedding init 'Embedding' object has no attribute 'dim'
# model.apply(torch.nn.init.xavier_uniform_)
model.train() # enable model training
epoch_start_idx = 1
if args.state_dict_path is not None:
try:
model.load_state_dict(torch.load(args.state_dict_path, map_location=torch.device(args.device)))
tail = args.state_dict_path[args.state_dict_path.find('epoch=') + 6:]
epoch_start_idx = int(tail[:tail.find('.')]) + 1
except: # in case your pytorch version is not 1.6 etc., pls debug by pdb if load weights failed
print('failed loading state_dicts, pls check file path: ', end="")
print(args.state_dict_path)
print('pdb enabled for your quick check, pls type exit() if you do not need it')
import pdb; pdb.set_trace()
if args.inference_only:
model.eval()
t_test = evaluate(model, dataset, args)
print('test (NDCG@10: %.4f, HR@10: %.4f)' % (t_test[0], t_test[1]))
# ce_criterion = torch.nn.CrossEntropyLoss()
# https://github.com/NVIDIA/pix2pixHD/issues/9 how could an old bug appear again...
bce_criterion = torch.nn.BCEWithLogitsLoss() # torch.nn.BCELoss()
adam_optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, betas=(0.9, 0.98))
best_val_ndcg, best_val_hr = 0.0, 0.0
best_test_ndcg, best_test_hr = 0.0, 0.0
T = 0.0
t0 = time.time()
for epoch in range(epoch_start_idx, args.num_epochs + 1):
if args.inference_only: break # just to decrease identition
for step in range(num_batch): # tqdm(range(num_batch), total=num_batch, ncols=70, leave=False, unit='b'):
u, seq, pos, neg = sampler.next_batch() # tuples to ndarray
u, seq, pos, neg = np.array(u), np.array(seq), np.array(pos), np.array(neg)
pos_logits, neg_logits = model(u, seq, pos, neg)
pos_labels, neg_labels = torch.ones(pos_logits.shape, device=args.device), torch.zeros(neg_logits.shape, device=args.device)
# print("\neye ball check raw_logits:"); print(pos_logits); print(neg_logits) # check pos_logits > 0, neg_logits < 0
adam_optimizer.zero_grad()
indices = np.where(pos != 0)
loss = bce_criterion(pos_logits[indices], pos_labels[indices])
loss += bce_criterion(neg_logits[indices], neg_labels[indices])
for param in model.item_emb.parameters(): loss += args.l2_emb * torch.norm(param)
loss.backward()
adam_optimizer.step()
print("loss in epoch {} iteration {}: {}".format(epoch, step, loss.item())) # expected 0.4~0.6 after init few epochs
if epoch % 20 == 0:
model.eval()
t1 = time.time() - t0
T += t1
print('Evaluating', end='')
t_test = evaluate(model, dataset, args)
t_valid = evaluate_valid(model, dataset, args)
print('epoch:%d, time: %f(s), valid (NDCG@10: %.4f, HR@10: %.4f), test (NDCG@10: %.4f, HR@10: %.4f)'
% (epoch, T, t_valid[0], t_valid[1], t_test[0], t_test[1]))
if t_valid[0] > best_val_ndcg or t_valid[1] > best_val_hr or t_test[0] > best_test_ndcg or t_test[1] > best_test_hr:
best_val_ndcg = max(t_valid[0], best_val_ndcg)
best_val_hr = max(t_valid[1], best_val_hr)
best_test_ndcg = max(t_test[0], best_test_ndcg)
best_test_hr = max(t_test[1], best_test_hr)
folder = args.dataset + '_' + args.train_dir
fname = 'SASRec.epoch={}.lr={}.layer={}.head={}.hidden={}.maxlen={}.pth'
fname = fname.format(epoch, args.lr, args.num_blocks, args.num_heads, args.hidden_units, args.maxlen)
torch.save(model.state_dict(), os.path.join(folder, fname))
f.write(str(epoch) + ' ' + str(t_valid) + ' ' + str(t_test) + '\n')
f.flush()
t0 = time.time()
model.train()
if epoch == args.num_epochs:
folder = args.dataset + '_' + args.train_dir
fname = 'SASRec.epoch={}.lr={}.layer={}.head={}.hidden={}.maxlen={}.pth'
fname = fname.format(args.num_epochs, args.lr, args.num_blocks, args.num_heads, args.hidden_units, args.maxlen)
torch.save(model.state_dict(), os.path.join(folder, fname))
f.close()
sampler.close()
print("Done")