mirror of
https://github.com/KimMeen/Time-LLM.git
synced 2024-11-21 03:13:47 +08:00
270 lines
13 KiB
Python
270 lines
13 KiB
Python
import argparse
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import torch
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from accelerate import Accelerator, DeepSpeedPlugin
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from accelerate import DistributedDataParallelKwargs
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from torch import nn, optim
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from torch.optim import lr_scheduler
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from tqdm import tqdm
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from models import Autoformer, DLinear, TimeLLM
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from data_provider.data_factory import data_provider
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import time
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import random
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import numpy as np
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import os
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os.environ['CURL_CA_BUNDLE'] = ''
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os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:64"
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from utils.tools import del_files, EarlyStopping, adjust_learning_rate, vali, load_content
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parser = argparse.ArgumentParser(description='Time-LLM')
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fix_seed = 2021
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random.seed(fix_seed)
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torch.manual_seed(fix_seed)
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np.random.seed(fix_seed)
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# basic config
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parser.add_argument('--task_name', type=str, required=True, default='long_term_forecast',
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help='task name, options:[long_term_forecast, short_term_forecast, imputation, classification, anomaly_detection]')
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parser.add_argument('--is_training', type=int, required=True, default=1, help='status')
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parser.add_argument('--model_id', type=str, required=True, default='test', help='model id')
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parser.add_argument('--model_comment', type=str, required=True, default='none', help='prefix when saving test results')
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parser.add_argument('--model', type=str, required=True, default='Autoformer',
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help='model name, options: [Autoformer, DLinear]')
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parser.add_argument('--seed', type=int, default=2021, help='random seed')
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# data loader
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parser.add_argument('--data', type=str, required=True, default='ETTm1', help='dataset type')
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parser.add_argument('--root_path', type=str, default='./dataset', help='root path of the data file')
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parser.add_argument('--data_path', type=str, default='ETTh1.csv', help='data file')
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parser.add_argument('--features', type=str, default='M',
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help='forecasting task, options:[M, S, MS]; '
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'M:multivariate predict multivariate, S: univariate predict univariate, '
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'MS:multivariate predict univariate')
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parser.add_argument('--target', type=str, default='OT', help='target feature in S or MS task')
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parser.add_argument('--loader', type=str, default='modal', help='dataset type')
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parser.add_argument('--freq', type=str, default='h',
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help='freq for time features encoding, '
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'options:[s:secondly, t:minutely, h:hourly, d:daily, b:business days, w:weekly, m:monthly], '
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'you can also use more detailed freq like 15min or 3h')
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parser.add_argument('--checkpoints', type=str, default='./checkpoints/', help='location of model checkpoints')
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# forecasting task
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parser.add_argument('--seq_len', type=int, default=96, help='input sequence length')
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parser.add_argument('--label_len', type=int, default=48, help='start token length')
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parser.add_argument('--pred_len', type=int, default=96, help='prediction sequence length')
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parser.add_argument('--seasonal_patterns', type=str, default='Monthly', help='subset for M4')
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# model define
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parser.add_argument('--enc_in', type=int, default=7, help='encoder input size')
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parser.add_argument('--dec_in', type=int, default=7, help='decoder input size')
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parser.add_argument('--c_out', type=int, default=7, help='output size')
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parser.add_argument('--d_model', type=int, default=16, help='dimension of model')
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parser.add_argument('--n_heads', type=int, default=8, help='num of heads')
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parser.add_argument('--e_layers', type=int, default=2, help='num of encoder layers')
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parser.add_argument('--d_layers', type=int, default=1, help='num of decoder layers')
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parser.add_argument('--d_ff', type=int, default=32, help='dimension of fcn')
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parser.add_argument('--moving_avg', type=int, default=25, help='window size of moving average')
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parser.add_argument('--factor', type=int, default=1, help='attn factor')
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parser.add_argument('--dropout', type=float, default=0.1, help='dropout')
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parser.add_argument('--embed', type=str, default='timeF',
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help='time features encoding, options:[timeF, fixed, learned]')
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parser.add_argument('--activation', type=str, default='gelu', help='activation')
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parser.add_argument('--output_attention', action='store_true', help='whether to output attention in encoder')
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parser.add_argument('--patch_len', type=int, default=16, help='patch length')
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parser.add_argument('--stride', type=int, default=8, help='stride')
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parser.add_argument('--prompt_domain', type=int, default=0, help='')
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parser.add_argument('--llm_model', type=str, default='LLAMA', help='LLM model') # LLAMA, GPT2, BERT
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parser.add_argument('--llm_dim', type=int, default='4096', help='LLM model dimension')# LLama7b:4096; GPT2-small:768; BERT-base:768
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# optimization
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parser.add_argument('--num_workers', type=int, default=10, help='data loader num workers')
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parser.add_argument('--itr', type=int, default=1, help='experiments times')
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parser.add_argument('--train_epochs', type=int, default=10, help='train epochs')
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parser.add_argument('--align_epochs', type=int, default=10, help='alignment epochs')
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parser.add_argument('--batch_size', type=int, default=32, help='batch size of train input data')
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parser.add_argument('--eval_batch_size', type=int, default=8, help='batch size of model evaluation')
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parser.add_argument('--patience', type=int, default=10, help='early stopping patience')
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parser.add_argument('--learning_rate', type=float, default=0.0001, help='optimizer learning rate')
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parser.add_argument('--des', type=str, default='test', help='exp description')
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parser.add_argument('--loss', type=str, default='MSE', help='loss function')
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parser.add_argument('--lradj', type=str, default='type1', help='adjust learning rate')
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parser.add_argument('--pct_start', type=float, default=0.2, help='pct_start')
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parser.add_argument('--use_amp', action='store_true', help='use automatic mixed precision training', default=False)
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parser.add_argument('--llm_layers', type=int, default=6)
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parser.add_argument('--percent', type=int, default=100)
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args = parser.parse_args()
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ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
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deepspeed_plugin = DeepSpeedPlugin(hf_ds_config='./ds_config_zero2.json')
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accelerator = Accelerator(kwargs_handlers=[ddp_kwargs], deepspeed_plugin=deepspeed_plugin)
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for ii in range(args.itr):
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# setting record of experiments
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setting = '{}_{}_{}_{}_ft{}_sl{}_ll{}_pl{}_dm{}_nh{}_el{}_dl{}_df{}_fc{}_eb{}_{}_{}'.format(
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args.task_name,
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args.model_id,
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args.model,
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args.data,
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args.features,
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args.seq_len,
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args.label_len,
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args.pred_len,
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args.d_model,
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args.n_heads,
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args.e_layers,
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args.d_layers,
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args.d_ff,
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args.factor,
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args.embed,
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args.des, ii)
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train_data, train_loader = data_provider(args, 'train')
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vali_data, vali_loader = data_provider(args, 'val')
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test_data, test_loader = data_provider(args, 'test')
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if args.model == 'Autoformer':
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model = Autoformer.Model(args).float()
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elif args.model == 'DLinear':
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model = DLinear.Model(args).float()
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else:
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model = TimeLLM.Model(args).float()
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path = os.path.join(args.checkpoints,
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setting + '-' + args.model_comment) # unique checkpoint saving path
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args.content = load_content(args)
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if not os.path.exists(path) and accelerator.is_local_main_process:
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os.makedirs(path)
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time_now = time.time()
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train_steps = len(train_loader)
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early_stopping = EarlyStopping(accelerator=accelerator, patience=args.patience)
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trained_parameters = []
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for p in model.parameters():
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if p.requires_grad is True:
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trained_parameters.append(p)
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model_optim = optim.Adam(trained_parameters, lr=args.learning_rate)
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if args.lradj == 'COS':
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scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(model_optim, T_max=20, eta_min=1e-8)
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else:
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scheduler = lr_scheduler.OneCycleLR(optimizer=model_optim,
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steps_per_epoch=train_steps,
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pct_start=args.pct_start,
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epochs=args.train_epochs,
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max_lr=args.learning_rate)
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criterion = nn.MSELoss()
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mae_metric = nn.L1Loss()
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train_loader, vali_loader, test_loader, model, model_optim, scheduler = accelerator.prepare(
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train_loader, vali_loader, test_loader, model, model_optim, scheduler)
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if args.use_amp:
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scaler = torch.cuda.amp.GradScaler()
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for epoch in range(args.train_epochs):
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iter_count = 0
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train_loss = []
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model.train()
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epoch_time = time.time()
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for i, (batch_x, batch_y, batch_x_mark, batch_y_mark) in tqdm(enumerate(train_loader)):
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iter_count += 1
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model_optim.zero_grad()
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batch_x = batch_x.float().to(accelerator.device)
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batch_y = batch_y.float().to(accelerator.device)
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batch_x_mark = batch_x_mark.float().to(accelerator.device)
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batch_y_mark = batch_y_mark.float().to(accelerator.device)
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# decoder input
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dec_inp = torch.zeros_like(batch_y[:, -args.pred_len:, :]).float().to(
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accelerator.device)
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dec_inp = torch.cat([batch_y[:, :args.label_len, :], dec_inp], dim=1).float().to(
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accelerator.device)
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# encoder - decoder
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if args.use_amp:
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with torch.cuda.amp.autocast():
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if args.output_attention:
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outputs = model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0]
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else:
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outputs = model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
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f_dim = -1 if args.features == 'MS' else 0
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outputs = outputs[:, -args.pred_len:, f_dim:]
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batch_y = batch_y[:, -args.pred_len:, f_dim:].to(accelerator.device)
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loss = criterion(outputs, batch_y)
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train_loss.append(loss.item())
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else:
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if args.output_attention:
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outputs = model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0]
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else:
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outputs = model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
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f_dim = -1 if args.features == 'MS' else 0
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outputs = outputs[:, -args.pred_len:, f_dim:]
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batch_y = batch_y[:, -args.pred_len:, f_dim:]
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loss = criterion(outputs, batch_y)
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train_loss.append(loss.item())
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if (i + 1) % 100 == 0:
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accelerator.print(
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"\titers: {0}, epoch: {1} | loss: {2:.7f}".format(i + 1, epoch + 1, loss.item()))
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speed = (time.time() - time_now) / iter_count
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left_time = speed * ((args.train_epochs - epoch) * train_steps - i)
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accelerator.print('\tspeed: {:.4f}s/iter; left time: {:.4f}s'.format(speed, left_time))
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iter_count = 0
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time_now = time.time()
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if args.use_amp:
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scaler.scale(loss).backward()
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scaler.step(model_optim)
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scaler.update()
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else:
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accelerator.backward(loss)
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model_optim.step()
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if args.lradj == 'TST':
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adjust_learning_rate(accelerator, model_optim, scheduler, epoch + 1, args, printout=False)
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scheduler.step()
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accelerator.print("Epoch: {} cost time: {}".format(epoch + 1, time.time() - epoch_time))
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train_loss = np.average(train_loss)
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vali_loss, vali_mae_loss = vali(args, accelerator, model, vali_data, vali_loader, criterion, mae_metric)
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test_loss, test_mae_loss = vali(args, accelerator, model, test_data, test_loader, criterion, mae_metric)
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accelerator.print(
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"Epoch: {0} | Train Loss: {1:.7f} Vali Loss: {2:.7f} Test Loss: {3:.7f} MAE Loss: {4:.7f}".format(
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epoch + 1, train_loss, vali_loss, test_loss, test_mae_loss))
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early_stopping(vali_loss, model, path)
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if early_stopping.early_stop:
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accelerator.print("Early stopping")
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break
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if args.lradj != 'TST':
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if args.lradj == 'COS':
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scheduler.step()
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accelerator.print("lr = {:.10f}".format(model_optim.param_groups[0]['lr']))
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else:
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if epoch == 0:
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args.learning_rate = model_optim.param_groups[0]['lr']
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accelerator.print("lr = {:.10f}".format(model_optim.param_groups[0]['lr']))
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adjust_learning_rate(accelerator, model_optim, scheduler, epoch + 1, args, printout=True)
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else:
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accelerator.print('Updating learning rate to {}'.format(scheduler.get_last_lr()[0]))
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accelerator.wait_for_everyone()
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if accelerator.is_local_main_process:
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path = './checkpoints' # unique checkpoint saving path
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del_files(path) # delete checkpoint files
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accelerator.print('success delete checkpoints') |