import argparse import torch from accelerate import Accelerator, DeepSpeedPlugin from accelerate import DistributedDataParallelKwargs from torch import nn, optim from torch.optim import lr_scheduler from tqdm import tqdm from models import Autoformer, DLinear, TimeLLM from data_provider.data_factory import data_provider import time import random import numpy as np import os os.environ['CURL_CA_BUNDLE'] = '' os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:64" from utils.tools import del_files, EarlyStopping, adjust_learning_rate, vali, load_content parser = argparse.ArgumentParser(description='Time-LLM') fix_seed = 2021 random.seed(fix_seed) torch.manual_seed(fix_seed) np.random.seed(fix_seed) # basic config parser.add_argument('--task_name', type=str, required=True, default='long_term_forecast', help='task name, options:[long_term_forecast, short_term_forecast, imputation, classification, anomaly_detection]') 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_comment', type=str, required=True, default='none', help='prefix when saving test results') parser.add_argument('--model', type=str, required=True, default='Autoformer', help='model name, options: [Autoformer, DLinear]') parser.add_argument('--seed', type=int, default=2021, help='random seed') # data loader parser.add_argument('--data', type=str, required=True, default='ETTm1', help='dataset type') parser.add_argument('--root_path', type=str, default='./dataset', help='root path of the data file') parser.add_argument('--data_path', type=str, default='ETTh1.csv', help='data file') parser.add_argument('--features', type=str, default='M', help='forecasting task, options:[M, S, MS]; ' 'M:multivariate predict multivariate, S: univariate predict univariate, ' 'MS:multivariate predict univariate') parser.add_argument('--target', type=str, default='OT', help='target feature in S or MS task') parser.add_argument('--loader', type=str, default='modal', help='dataset type') parser.add_argument('--freq', type=str, default='h', help='freq for time features encoding, ' 'options:[s:secondly, t:minutely, h:hourly, d:daily, b:business days, w:weekly, m:monthly], ' 'you can also use more detailed freq like 15min or 3h') parser.add_argument('--checkpoints', type=str, default='./checkpoints/', help='location of model checkpoints') # forecasting task parser.add_argument('--seq_len', type=int, default=96, help='input sequence length') parser.add_argument('--label_len', type=int, default=48, help='start token length') parser.add_argument('--pred_len', type=int, default=96, help='prediction sequence length') parser.add_argument('--seasonal_patterns', type=str, default='Monthly', help='subset for M4') # model define parser.add_argument('--enc_in', type=int, default=7, help='encoder input size') parser.add_argument('--dec_in', type=int, default=7, help='decoder input size') parser.add_argument('--c_out', type=int, default=7, help='output size') parser.add_argument('--d_model', type=int, default=16, help='dimension of model') parser.add_argument('--n_heads', type=int, default=8, help='num of heads') parser.add_argument('--e_layers', type=int, default=2, help='num of encoder layers') parser.add_argument('--d_layers', type=int, default=1, help='num of decoder layers') parser.add_argument('--d_ff', type=int, default=32, help='dimension of fcn') parser.add_argument('--moving_avg', type=int, default=25, help='window size of moving average') parser.add_argument('--factor', type=int, default=1, help='attn factor') parser.add_argument('--dropout', type=float, default=0.1, help='dropout') parser.add_argument('--embed', type=str, default='timeF', help='time features encoding, options:[timeF, fixed, learned]') parser.add_argument('--activation', type=str, default='gelu', help='activation') parser.add_argument('--output_attention', action='store_true', help='whether to output attention in encoder') parser.add_argument('--patch_len', type=int, default=16, help='patch length') parser.add_argument('--stride', type=int, default=8, help='stride') parser.add_argument('--prompt_domain', type=int, default=0, help='') # 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('--align_epochs', type=int, default=10, help='alignment epochs') parser.add_argument('--batch_size', type=int, default=32, help='batch size of train input data') parser.add_argument('--eval_batch_size', type=int, default=8, help='batch size of model evaluation') parser.add_argument('--patience', type=int, default=10, 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('--pct_start', type=float, default=0.2, help='pct_start') parser.add_argument('--use_amp', action='store_true', help='use automatic mixed precision training', default=False) parser.add_argument('--llm_layers', type=int, default=6) parser.add_argument('--percent', type=int, default=100) args = parser.parse_args() ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True) deepspeed_plugin = DeepSpeedPlugin(hf_ds_config='./ds_config_zero2.json') accelerator = Accelerator(kwargs_handlers=[ddp_kwargs], deepspeed_plugin=deepspeed_plugin) for ii in range(args.itr): # setting record of experiments setting = '{}_{}_{}_{}_ft{}_sl{}_ll{}_pl{}_dm{}_nh{}_el{}_dl{}_df{}_fc{}_eb{}_{}_{}'.format( args.task_name, args.model_id, args.model, args.data, args.features, args.seq_len, args.label_len, args.pred_len, args.d_model, args.n_heads, args.e_layers, args.d_layers, args.d_ff, args.factor, args.embed, args.des, ii) train_data, train_loader = data_provider(args, 'train') vali_data, vali_loader = data_provider(args, 'val') test_data, test_loader = data_provider(args, 'test') if args.model == 'Autoformer': model = Autoformer.Model(args).float() elif args.model == 'DLinear': model = DLinear.Model(args).float() else: model = TimeLLM.Model(args).float() path = os.path.join(args.checkpoints, setting + '-' + args.model_comment) # unique checkpoint saving path args.content = load_content(args) if not os.path.exists(path) and accelerator.is_local_main_process: os.makedirs(path) time_now = time.time() train_steps = len(train_loader) early_stopping = EarlyStopping(accelerator=accelerator, patience=args.patience) trained_parameters = [] for p in model.parameters(): if p.requires_grad is True: trained_parameters.append(p) model_optim = optim.Adam(trained_parameters, lr=args.learning_rate) if args.lradj == 'COS': scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(model_optim, T_max=20, eta_min=1e-8) else: scheduler = lr_scheduler.OneCycleLR(optimizer=model_optim, steps_per_epoch=train_steps, pct_start=args.pct_start, epochs=args.train_epochs, max_lr=args.learning_rate) criterion = nn.MSELoss() mae_metric = nn.L1Loss() train_loader, vali_loader, test_loader, model, model_optim, scheduler = accelerator.prepare( train_loader, vali_loader, test_loader, model, model_optim, scheduler) if args.use_amp: scaler = torch.cuda.amp.GradScaler() for epoch in range(args.train_epochs): iter_count = 0 train_loss = [] model.train() epoch_time = time.time() for i, (batch_x, batch_y, batch_x_mark, batch_y_mark) in tqdm(enumerate(train_loader)): iter_count += 1 model_optim.zero_grad() batch_x = batch_x.float().to(accelerator.device) batch_y = batch_y.float().to(accelerator.device) batch_x_mark = batch_x_mark.float().to(accelerator.device) batch_y_mark = batch_y_mark.float().to(accelerator.device) # decoder input dec_inp = torch.zeros_like(batch_y[:, -args.pred_len:, :]).float().to( accelerator.device) dec_inp = torch.cat([batch_y[:, :args.label_len, :], dec_inp], dim=1).float().to( accelerator.device) # encoder - decoder if args.use_amp: with torch.cuda.amp.autocast(): if args.output_attention: outputs = model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0] else: outputs = model(batch_x, batch_x_mark, dec_inp, batch_y_mark) f_dim = -1 if args.features == 'MS' else 0 outputs = outputs[:, -args.pred_len:, f_dim:] batch_y = batch_y[:, -args.pred_len:, f_dim:].to(accelerator.device) loss = criterion(outputs, batch_y) train_loss.append(loss.item()) else: if args.output_attention: outputs = model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0] else: outputs = model(batch_x, batch_x_mark, dec_inp, batch_y_mark) f_dim = -1 if args.features == 'MS' else 0 outputs = outputs[:, -args.pred_len:, f_dim:] batch_y = batch_y[:, -args.pred_len:, f_dim:] loss = criterion(outputs, batch_y) train_loss.append(loss.item()) if (i + 1) % 100 == 0: accelerator.print( "\titers: {0}, epoch: {1} | loss: {2:.7f}".format(i + 1, epoch + 1, loss.item())) speed = (time.time() - time_now) / iter_count left_time = speed * ((args.train_epochs - epoch) * train_steps - i) accelerator.print('\tspeed: {:.4f}s/iter; left time: {:.4f}s'.format(speed, left_time)) iter_count = 0 time_now = time.time() if args.use_amp: scaler.scale(loss).backward() scaler.step(model_optim) scaler.update() else: accelerator.backward(loss) model_optim.step() if args.lradj == 'TST': adjust_learning_rate(accelerator, model_optim, scheduler, epoch + 1, args, printout=False) scheduler.step() accelerator.print("Epoch: {} cost time: {}".format(epoch + 1, time.time() - epoch_time)) train_loss = np.average(train_loss) vali_loss, vali_mae_loss = vali(args, accelerator, model, vali_data, vali_loader, criterion, mae_metric) test_loss, test_mae_loss = vali(args, accelerator, model, test_data, test_loader, criterion, mae_metric) accelerator.print( "Epoch: {0} | Train Loss: {1:.7f} Vali Loss: {2:.7f} Test Loss: {3:.7f} MAE Loss: {4:.7f}".format( epoch + 1, train_loss, vali_loss, test_loss, test_mae_loss)) early_stopping(vali_loss, model, path) if early_stopping.early_stop: accelerator.print("Early stopping") break if args.lradj != 'TST': if args.lradj == 'COS': scheduler.step() accelerator.print("lr = {:.10f}".format(model_optim.param_groups[0]['lr'])) else: if epoch == 0: args.learning_rate = model_optim.param_groups[0]['lr'] accelerator.print("lr = {:.10f}".format(model_optim.param_groups[0]['lr'])) adjust_learning_rate(accelerator, model_optim, scheduler, epoch + 1, args, printout=True) else: accelerator.print('Updating learning rate to {}'.format(scheduler.get_last_lr()[0])) accelerator.wait_for_everyone() if accelerator.is_local_main_process: path = './checkpoints' # unique checkpoint saving path del_files(path) # delete checkpoint files accelerator.print('success delete checkpoints')