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main.py
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main.py
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# Some parts derived from: https://github.com/yuangh-x/2022-NIPS-Tenrec/blob/master/main.py
import os
import pathlib
import random
import sys
import time
from argparse import ArgumentParser
import torch
from torch.utils.tensorboard import SummaryWriter
from data import (BertTrainDataset, EvalDataset, TrainDataset, get_data_loader,
sequence_dataset, train_val_test_split)
from model_def.bert4rec import BERTModel
from model_def.gru4rec import GRU4Rec
from model_def.nextitnet import NextItNet
from model_def.sasrec import SASRec
from model_def.sas4infacc import SAS4infaccModel, SAS_PolicyNetGumbel
from model_def.skiprec import SkipRec, PolicyNetGumbel
from train import train_val_schedular, validator, inference_acc_schedular, paired_validator
def list_of_ints(arg):
return [int(i) for i in arg.split(',')]
def parse_args(description):
parser = ArgumentParser(description=description)
parser.add_argument('--device', default='cuda')
parser.add_argument('--save_path', type=str, default='./checkpoint/')
parser.add_argument("--data_path", type=str, required=True)
parser.add_argument('--seed', type=int, default=100) # seed default = 0
parser.add_argument("--num_users", type=int, default=0)
parser.add_argument("--num_products", type=int, default=0)
parser.add_argument("--max_len", type=int, default=20)
parser.add_argument("--min_seq_len", type=int, default=5)
parser.add_argument("--pad_token", type=int, default=0)
parser.add_argument("--train_batch_size", type=int, default=32)
parser.add_argument("--val_batch_size", type=int, default=32)
parser.add_argument("--test_batch_size", type=int, default=32)
parser.add_argument("--valid_rate", type=int, default=100)
parser.add_argument("--sample_prob", type=float, default=0.11)
parser.add_argument("--is_parallel", type=bool, default=False)
parser.add_argument("--model_name", type=str, required=True)
parser.add_argument("--embedding_size", type=int, default=64)
parser.add_argument('--hidden_size', type=int, default=64, help='Size of hidden vectors (model)')
parser.add_argument("--epochs", type=int, default=2)
parser.add_argument("--lr", type=float, default=0.0001)
parser.add_argument("--weight_decay", type=float, default=0.0)
parser.add_argument("--dropout", type=float, default=0.3)
parser.add_argument("--task_name", type=str, default='sequence')
parser.add_argument("--task", type=int, default=-1)
# bert4Rec
parser.add_argument("--mask_prob", type=float, default=0.3)
parser.add_argument("--num_heads", type=int, default=4)
# nextitnet
parser.add_argument("--block_num", type=int, default=8)
parser.add_argument("--dilations", type=list_of_ints, default=[1, 4])
parser.add_argument("--kernel_size", type=int, default=3)
# eval params
parser.add_argument('--k', type=int, default=20, help='The number of items to measure the hit@k metric (i.e. hit@10 to see if the correct item is within the top 10 scores)')
parser.add_argument('--metric_ks', nargs='+', type=int, default=[5, 20], help='ks for Metric@k')
parser.add_argument('--eval', type=bool, default=True)
# transfer learning
parser.add_argument('--is_pretrain', type=int, default=1, help='0: mean transfer, 1: mean pretrain, 2:mean train full model without transfer')
# inference acceleration
parser.add_argument('--temp', type=int, default=7)
args = parser.parse_args()
return args
def get_model(args):
if args.model_name == 'nextitnet':
model = NextItNet(args=args)
elif args.model_name == 'bert4rec':
model = BERTModel(args)
elif args.model_name == 'gru4rec':
model = GRU4Rec(args)
elif args.model_name == 'sasrec':
model = SASRec(args)
elif args.model_name == 'skiprec':
model = (SkipRec(args), PolicyNetGumbel(args))
elif args.model_name == 'sas4infacc':
model = (SAS4infaccModel(args), SAS_PolicyNetGumbel(args))
return model
def main(args):
if args.task_name not in ['sequence', 'inference_acc']:
raise ValueError("Invalid task option.")
if args.model_name == 'inference_acc':
args.test_batch_size = 1
rng = random.Random(args.seed)
writer = SummaryWriter()
print("Model Name: ", args.model_name)
model_dir = pathlib.Path(args.save_path)
model_dir.mkdir(parents=True, exist_ok=True)
data_path = args.data_path
sequences, product_count, user_count = sequence_dataset(
path=data_path, min_seq_len=args.min_seq_len, sample_prob=args.sample_prob)
print("Len of filtered sequences: ", len(sequences))
args.num_users = user_count
args.num_products = product_count
print("Num products: ", args.num_products)
train_data, val_data, test_data = train_val_test_split(sequences=sequences)
# sample the val dataset, since validation epoch takes a lot of time
cnt = 0; train_data_val_small = {}; val_data_val_small = {}
for key, _ in val_data.items():
train_data_val_small[key] = train_data[key]
val_data_val_small[key] = val_data[key]
cnt += 1
if cnt == int(len(train_data) / args.valid_rate):
break
if 'bert' in args.model_name:
train_dataset = BertTrainDataset(train_data, args.max_len, args.mask_prob, args.pad_token, args.num_products, rng)
else:
train_dataset = TrainDataset(train_data, args.max_len, args.pad_token)
val_dataset = EvalDataset(train_data_val_small, val_data_val_small, args.max_len, args.pad_token, args.num_products)
test_dataset = EvalDataset(train_data, test_data, args.max_len, args.pad_token, args.num_products)
train_loader = get_data_loader(
train_dataset, batch_size=args.train_batch_size, is_parallel=args.is_parallel, is_train=True
)
val_loader = get_data_loader(
val_dataset, batch_size=args.val_batch_size, is_parallel=args.is_parallel, is_train=False
)
test_loader = get_data_loader(
test_dataset, batch_size=args.test_batch_size, is_parallel=args.is_parallel, is_train=False
)
# Task specific model loading
if args.task_name == 'sequence':
model = get_model(args).to(args.device)
elif args.task_name == 'inference_acc':
backbonenet, policynet = get_model(args)
backbonenet = backbonenet.to(args.device)
policynet = policynet.to(args.device)
since = time.time()
if args.task_name == 'sequence':
_ = train_val_schedular(args.epochs, model, train_loader, val_loader, writer, args)
elif args.task_name == 'inference_acc':
_,_ = inference_acc_schedular(args.epochs, backbonenet, policynet, train_loader, val_loader, writer, args)
print("Total time to train: ", time.time() - since)
if args.eval:
if args.task_name == 'sequence':
best_model = torch.load(
os.path.join(
args.save_path,
'{}_{}_seed{}_is_pretrain_{}_best_model_lr{}_wd{}_block{}_hd{}_emb{}.pth'.format(
args.task_name, args.model_name, args.seed, args.is_pretrain, args.lr,
args.weight_decay, args.block_num, args.hidden_size, args.embedding_size
)
)
)
model.load_state_dict(best_model)
model = model.to(args.device)
since_val = time.time()
_ = validator(0, model=model, dataloader=test_loader, writer=writer, args=args, test=False)
elif args.task_name == 'inference_acc':
best_policy = torch.load(
os.path.join(
args.save_path,
'{}_{}_seed{}_lr{}_block{}_best_policynet.pth'.format(
args.task_name, args.model_name, args.seed, args.lr, args.block_num
)
)
)
best_backbone = torch.load(
os.path.join(
args.save_path,
'{}_{}_seed{}_lr{}_block{}_best_backbone.pth'.format(
args.task_name, args.model_name, args.seed, args.lr, args.block_num
)
)
)
policynet.load_state_dict(best_policy)
backbonenet.load_state_dict(best_backbone)
policynet = policynet.to(args.device)
backbonenet = backbonenet.to(args.device)
since_val = time.time()
metrics = paired_validator(0, backbonenet, policynet, test_loader, writer, args, test=True)
print(metrics)
print('[Inf Acc] inference_time:', backbonenet.all_time)
print("Total time to test: ", time.time() - since_val)
writer.close()
if __name__ == "__main__":
sys.exit(main(parse_args("Run training pipeline.")))