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run.py
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import os
import argparse
import random
import numpy as np
from datetime import datetime
import torch
import torch.distributed as dist
from exp.exp_forecast import Exp_Forecast
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Timer-XL')
# basic config
parser.add_argument('--task_name', type=str, required=True, default='forecast', help='task name, options:[forecast]')
parser.add_argument('--is_training', type=int, required=True, default=1, help='status')
parser.add_argument('--model_id', type=str, required=True, default='test', help='model id')
parser.add_argument('--model', type=str, required=True, default='timer_xl', help='model name, options: [timer_xl, timer, moirai, moment]')
parser.add_argument('--seed', type=int, default=2021, help='seed')
# data loader
parser.add_argument('--data', type=str, required=True, default='ETTh1', help='dataset type')
parser.add_argument('--root_path', type=str, default='./dataset/ETT-small/', help='root path of the data file')
parser.add_argument('--data_path', type=str, default='ETTh1.csv', help='data file')
parser.add_argument('--checkpoints', type=str, default='./checkpoints/', help='location of model checkpoints')
parser.add_argument('--test_flag', type=str, default='T', help='test domain')
# forecasting task
parser.add_argument('--seq_len', type=int, default=672, help='input sequence length')
parser.add_argument('--input_token_len', type=int, default=576, help='input token length')
parser.add_argument('--output_token_len', type=int, default=96, help='output token length')
parser.add_argument('--test_seq_len', type=int, default=672, help='test seq len')
parser.add_argument('--test_pred_len', type=int, default=96, help='test pred len')
parser.add_argument('--inverse', action='store_true', help='inverse output data', default=False)
# model define
parser.add_argument('--dropout', type=float, default=0.1, help='dropout')
parser.add_argument('--e_layers', type=int, default=1, help='encoder layers')
parser.add_argument('--d_model', type=int, default=512, help='d model')
parser.add_argument('--n_heads', type=int, default=8, help='n heads')
parser.add_argument('--d_ff', type=int, default=2048, help='d ff')
parser.add_argument('--activation', type=str, default='relu', help='activation')
parser.add_argument('--covariate', action='store_true', help='use cov', default=False)
parser.add_argument('--node_num', type=int, default=100, help='number of nodes')
parser.add_argument('--node_list', type=str, default='23,37,40', help='number of nodes for a tree')
parser.add_argument('--use_norm', action='store_true', help='use norm', default=False)
parser.add_argument('--nonautoregressive', action='store_true', help='nonautoregressive', default=False)
parser.add_argument('--test_dir', type=str, default='./test', help='test dir')
parser.add_argument('--test_file_name', type=str, default='checkpoint.pth', help='test file')
parser.add_argument('--output_attention', action='store_true', help='output attention', default=False)
parser.add_argument('--visualize', action='store_true', help='visualize', default=False)
parser.add_argument('--flash_attention', action='store_true', help='flash attention', default=False)
# adaptation
parser.add_argument('--adaptation', action='store_true', help='adaptation', default=False)
parser.add_argument('--pretrain_model_path', type=str, default='pretrain_model.pth', help='pretrain model path')
parser.add_argument('--subset_rand_ratio', type=float, default=1, help='few shot ratio')
# optimization
parser.add_argument('--num_workers', type=int, default=10, help='data loader num workers')
parser.add_argument('--itr', type=int, default=1, help='experiments times')
parser.add_argument('--train_epochs', type=int, default=10, help='train epochs')
parser.add_argument('--batch_size', type=int, default=32, help='batch size of train input data')
parser.add_argument('--patience', type=int, default=3, help='early stopping patience')
parser.add_argument('--learning_rate', type=float, default=0.0001, help='optimizer learning rate')
parser.add_argument('--des', type=str, default='test', help='exp description')
parser.add_argument('--loss', type=str, default='MSE', help='loss function')
parser.add_argument('--lradj', type=str, default='type1', help='adjust learning rate')
parser.add_argument('--cosine', action='store_true', help='use cosine annealing lr', default=False)
parser.add_argument('--tmax', type=int, default=10, help='tmax in cosine anealing lr')
parser.add_argument('--weight_decay', type=float, default=0)
parser.add_argument('--valid_last', action='store_true', help='valid last', default=False)
parser.add_argument('--last_token', action='store_true', help='last token', default=False)
# GPU
parser.add_argument('--gpu', type=int, default=0, help='gpu')
parser.add_argument('--ddp', action='store_true', help='Distributed Data Parallel', default=False)
parser.add_argument('--dp', action='store_true', help='Data Parallel', default=False)
parser.add_argument('--devices', type=str, default='0,1,2,3', help='device ids of multile gpus')
# LLM-based model
parser.add_argument('--gpt_layers', type=int, default=6)
parser.add_argument('--patch_size', type=int, default=16)
parser.add_argument('--kernel_size', type=int, default=25)
parser.add_argument('--stride', type=int, default=8)
args = parser.parse_args()
fix_seed = args.seed
random.seed(fix_seed)
torch.manual_seed(fix_seed)
np.random.seed(fix_seed)
args.node_list = [int(x) for x in args.node_list.split(',')]
if args.dp:
args.devices = args.devices.replace(' ', '')
device_ids = args.devices.split(',')
args.device_ids = [int(id_) for id_ in device_ids]
args.gpu = args.device_ids[0]
elif args.ddp:
ip = os.environ.get("MASTER_ADDR", "127.0.0.1")
port = os.environ.get("MASTER_PORT", "64209")
hosts = int(os.environ.get("WORLD_SIZE", "8"))
rank = int(os.environ.get("RANK", "0"))
local_rank = int(os.environ.get("LOCAL_RANK", "0"))
gpus = torch.cuda.device_count()
args.local_rank = local_rank
print(ip, port, hosts, rank, local_rank, gpus)
dist.init_process_group(backend="nccl", init_method=f"tcp://{ip}:{port}", world_size=hosts,
rank=rank)
torch.cuda.set_device(local_rank)
if args.task_name == 'forecast':
Exp = Exp_Forecast
else:
Exp = Exp_Forecast
if args.is_training:
for ii in range(args.itr):
# setting record of experiments
exp = Exp(args) # set experiments
setting = '{}_{}_{}_{}_sl{}_it{}_ot{}_lr{}_bt{}_wd{}_el{}_dm{}_dff{}_nh{}_cos{}_{}_{}'.format(
args.task_name,
args.model_id,
args.model,
args.data,
args.seq_len,
args.input_token_len,
args.output_token_len,
args.learning_rate,
args.batch_size,
args.weight_decay,
args.e_layers,
args.d_model,
args.d_ff,
args.n_heads,
args.cosine,
args.des, ii)
setting = datetime.now().strftime("%y-%m-%d_%H-%M-%S") + setting
print('>>>>>>>start training : {}>>>>>>>>>>>>>>>>>>>>>>>>>>'.format(setting))
exp.train(setting)
print('>>>>>>>testing : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting))
if not args.ddp and not args.dp:
exp.test(setting)
torch.cuda.empty_cache()
else:
ii = 0
setting = '{}_{}_{}_{}_sl{}_it{}_ot{}_lr{}_bt{}_wd{}_el{}_dm{}_dff{}_nh{}_cos{}_{}_{}'.format(
args.task_name,
args.model_id,
args.model,
args.data,
args.seq_len,
args.input_token_len,
args.output_token_len,
args.learning_rate,
args.batch_size,
args.weight_decay,
args.e_layers,
args.d_model,
args.d_ff,
args.n_heads,
args.cosine,
args.des, ii)
setting = datetime.now().strftime("%y-%m-%d_%H-%M-%S") + setting
exp = Exp(args) # set experiments
exp.test(setting, test=1)
torch.cuda.empty_cache()