import sys from pathlib import Path # 获取项目根目录（train.py上级的上级） root_path = Path(__file__).parent.parent sys.path.append(str(root_path)) import torch import os import numpy as np import torch.distributed as dist import logging import time from model.fgn import FGN from onescience.datapipes.climate import ERA5Datapipe from onescience.utils.YParams import YParams try: from apex import optimizers _FUSED_ADAM = True except Exception: _FUSED_ADAM = False def main(): logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") logger = logging.getLogger() ## Model config init config_file_path = os.path.join(current_path, "conf/config.yaml") cfg = YParams(config_file_path, "model") ## Distributed config init cfg.world_size = 1 if "WORLD_SIZE" in os.environ: cfg.world_size = int(os.environ["WORLD_SIZE"]) world_rank = 0 local_rank = 0 if cfg.world_size > 1 and torch.cuda.is_available(): dist.init_process_group(backend="nccl", init_method="env://") local_rank = int(os.environ["LOCAL_RANK"]) world_rank = dist.get_rank() device = f"cuda:{local_rank}" if torch.cuda.is_available() else "cpu" ## DataLoader init cfg_data = YParams(config_file_path, "datapipe") cfg['N_in_channels'] = len(cfg_data.dataset.channels) cfg['N_out_channels'] = len(cfg_data.dataset.channels) datapipe = ERA5Datapipe( dataset_dir=cfg_data.dataset.data_dir, used_variables=cfg_data.dataset.channels, used_years=cfg_data.dataset.train_time, distributed=dist.is_initialized(), input_steps=cfg.input_steps, output_steps=cfg.output_steps, batch_size=cfg_data.dataloader.batch_size, num_workers=cfg_data.dataloader.num_workers, ) train_dataloader, train_sampler = datapipe.get_dataloader("train") datapipe = ERA5Datapipe( dataset_dir=cfg_data.dataset.data_dir, used_variables=cfg_data.dataset.channels, used_years=cfg_data.dataset.val_time, distributed=dist.is_initialized(), input_steps=cfg.input_steps, output_steps=cfg.output_steps, batch_size=cfg_data.dataloader.batch_size, num_workers=cfg_data.dataloader.num_workers, ) val_dataloader, val_sampler = datapipe.get_dataloader("valid") # Model init model = FGN( in_channels=cfg['N_in_channels'], out_channels=cfg['N_out_channels'], input_steps=cfg.input_steps, output_steps=cfg.output_steps, grid_shape=cfg.grid_shape, mesh_shape=cfg.mesh_shape, latent_dim=cfg.latent_dim, num_encoder_layers=cfg.num_encoder_layers, num_decoder_layers=cfg.num_decoder_layers, num_processor_blocks=cfg.num_processor_blocks, n_heads=cfg.n_heads, hidden_dim=cfg.hidden_dim, noise_dim=cfg.noise_dim, channel_weights=cfg.channel_weights, ).to(device) if _FUSED_ADAM: optimizer = optimizers.FusedAdam(model.parameters(), lr=cfg.lr) else: optimizer = torch.optim.Adam(model.parameters(), lr=cfg.lr) scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=0.2, patience=5, mode='min') ## Train process init os.makedirs(cfg.checkpoint_dir, exist_ok=True) train_loss_file = f"{cfg.checkpoint_dir}/trloss.npy" valid_loss_file = f"{cfg.checkpoint_dir}/valoss.npy" best_valid_loss = float("inf") best_loss_epoch = 0 train_losses = np.empty((0,), dtype=np.float32) valid_losses = np.empty((0,), dtype=np.float32) ## Get model params count if cfg.world_size == 1: total_params = sum(p.numel() for p in model.parameters()) print("\n\n") print("-" * 50) print(f"📂 now params is {total_params}, {total_params / 1e6:.2f}M, {total_params / 1e9:.2f}B") print("-" * 50, "\n") ## Load model weight if there exist well-trained model if os.path.exists(f"{cfg.checkpoint_dir}/model_bak.pth"): if world_rank == 0: print("\n\n") print("-" * 50) print(f"✅ There has a model weight, load and continue training...") print(f'If you want to train a new model, ensure there is no *.pth file in {cfg.checkpoint_dir}') print("-" * 50, "\n") ckpt = torch.load(f"{cfg.checkpoint_dir}/model_bak.pth", map_location=device, weights_only=False) model.load_state_dict(ckpt["model_state_dict"]) optimizer.load_state_dict(ckpt["optimizer_state_dict"]) scheduler.load_state_dict(ckpt["scheduler_state_dict"]) best_valid_loss = ckpt["best_valid_loss"] best_loss_epoch = ckpt["best_loss_epoch"] train_losses = np.load(train_loss_file) valid_losses = np.load(valid_loss_file) ## Distributed model if dist.is_initialized(): model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[local_rank], output_device=local_rank) world_rank == 0 and logger.info(f"start training ...") for epoch in range(cfg.max_epoch): if dist.is_initialized(): train_sampler.set_epoch(epoch) val_sampler.set_epoch(epoch) model.train() train_loss = 0 start_time = time.time() for j, data in enumerate(train_dataloader): invar = data[0].to(device, dtype=torch.float32) # [B, input_steps, C, H, W] outvar = data[1].to(device, dtype=torch.float32) # [B, output_steps, C, H, W] outvar_pred = model(invar, num_members=cfg.num_members) # [B, M, output_steps, C, H, W] loss = model.crps_loss(outvar_pred, outvar) # 论文式(4) 公平 CRPS optimizer.zero_grad() loss.backward() optimizer.step() train_loss += loss.item() if world_rank == 0: logger.info(f'Train: Epoch {epoch}-{j+1}/{len(train_dataloader)} ' f'[cost {int((time.time()-start_time) // 60):02}:{int((time.time()-start_time) % 60):02}] ' f'[{(time.time()-start_time)/(j+1): .02f}s/{cfg_data.dataloader.batch_size}batch] ' f'loss:{train_loss / (j+1): .04f}') train_loss /= len(train_dataloader) model.eval() valid_loss = 0 with torch.no_grad(): start_time = time.time() for j, data in enumerate(val_dataloader): invar = data[0].to(device, dtype=torch.float32) outvar = data[1].to(device, dtype=torch.float32) outvar_pred = model(invar, num_members=cfg.num_members) loss = model.crps_loss(outvar_pred, outvar) if dist.is_initialized(): loss_tensor = loss.detach().to(device) dist.all_reduce(loss_tensor) loss = loss_tensor.item() / cfg.world_size valid_loss += loss else: valid_loss += loss.item() if world_rank == 0: logger.info(f'Valid: Epoch {epoch}-{j+1}/{len(val_dataloader)} ' f'[cost {int((time.time()-start_time) // 60):02}:{int((time.time()-start_time) % 60):02}] ' f'loss:{valid_loss / (j+1): .04f}') valid_loss /= len(val_dataloader) is_save_ckp = False if valid_loss < best_valid_loss: best_valid_loss = valid_loss best_loss_epoch = epoch world_rank == 0 and save_checkpoint(model, optimizer, scheduler, best_valid_loss, best_loss_epoch, cfg.checkpoint_dir) is_save_ckp = True scheduler.step(valid_loss) if world_rank == 0: logger.info(f"Epoch [{epoch + 1}/{cfg.max_epoch}], " f"Train Loss: {train_loss:.4f}, " f"Valid Loss: {valid_loss:.4f}, " f"Best loss at Epoch: {best_loss_epoch + 1}" + (", saving checkpoint" if is_save_ckp else "") ) train_losses = np.append(train_losses, train_loss) valid_losses = np.append(valid_losses, valid_loss) np.save(train_loss_file, train_losses) np.save(valid_loss_file, valid_losses) if epoch - best_loss_epoch > cfg.patience: print(f"Loss has not decrease in {cfg.patience} epochs, stopping training...") exit() def save_checkpoint(model, optimizer, scheduler, best_valid_loss, best_loss_epoch, model_path): model_to_save = model.module if hasattr(model, "module") else model state = {"model_state_dict": model_to_save.state_dict(), "optimizer_state_dict": optimizer.state_dict(), "scheduler_state_dict": scheduler.state_dict(), "best_valid_loss": best_valid_loss, "best_loss_epoch": best_loss_epoch, } torch.save(state, f"{model_path}/model.pth") ### the weight file saving may interrupted due to DCU queue limit, get a backup to ensure there at least has one model os.system(f"mv {model_path}/model.pth {model_path}/model_bak.pth") if __name__ == "__main__": current_path = os.getcwd() sys.path.append(current_path) main()