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 tqdm import tqdm from torch.nn.parallel import DistributedDataParallel from model.fuxi import Fuxi from scripts.data_loader import ERA5Datapipe from onescience.utils.YParams import YParams from onescience.metrics.climate.loss import LatitudeWeightedLoss from onescience.memory.checkpoint import replace_function from apex import optimizers 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: dist.init_process_group(backend="nccl", init_method="env://") local_rank = int(os.environ["LOCAL_RANK"]) world_rank = dist.get_rank() if not os.path.exists(f"{cfg.checkpoint_dir}/model_medium_bak.pth"): if world_rank == 0: print(f'❌❌The Fuxi medium model must be trained before this model.') exit() ## DataLoader init cfg_data = YParams(config_file_path, "datapipe") datapipe = ERA5Datapipe( dataset_dir=cfg_data.dataset.data_dir, used_variables=cfg_data.dataset.channels, used_years=cfg_data.dataset.train_time, pattern='long', distributed=dist.is_initialized(), output_steps=2, input_steps=2, 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, pattern='long', distributed=dist.is_initialized(), output_steps=2, input_steps=2, 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 = Fuxi(img_size=cfg_data.dataset.img_size, patch_size=cfg.patch_size, in_chans=len(cfg_data.dataset.channels), out_chans=len(cfg_data.dataset.channels), embed_dim=cfg.embed_dim, num_groups=cfg.num_groups, num_heads=cfg.num_heads, window_size=cfg.window_size ).to(local_rank) optimizer = optimizers.FusedAdam(model.parameters(), lr=cfg.train_lr) scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=0.2, patience=5, mode="min") loss_obj = LatitudeWeightedLoss(loss_type="l1", normalize=True).to(local_rank) ## Train process init os.makedirs(cfg.checkpoint_dir, exist_ok=True) train_loss_file = f"{cfg.checkpoint_dir}/tr_long_loss.npy" valid_loss_file = f"{cfg.checkpoint_dir}/va_long_loss.npy" best_valid_loss = 1.0e6 best_loss_epoch = 0 train_losses = np.empty((0,), dtype=np.float32) valid_losses = np.empty((0,), dtype=np.float32) current_epoch = 0 ## 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 not os.path.exists(f"{cfg.checkpoint_dir}/model_medium_bak.pth"): print('⚠️ ⚠️ Please train to get medium model first...') exit() if os.path.exists(f"{cfg.checkpoint_dir}/model_long_bak.pth"): if world_rank == 0: print("\n\n") print("-" * 50) print(f"✅ There has a long-pattern model weight, load and continue training...") print(f'If you want to finetune a new model, ensure there is no model_short_bak.pth file in {cfg.checkpoint_dir}') print("-" * 50, "\n") ckpt = torch.load(f"{cfg.checkpoint_dir}/model_long_bak.pth", map_location=f'cuda:{local_rank}', 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"] current_epoch = ckpt["current_epoch"] train_losses = np.load(f"{cfg.checkpoint_dir}/tr_long_loss.npy") valid_losses = np.load(f"{cfg.checkpoint_dir}/va_long_loss.npy") else: if world_rank == 0: print("\n\n") print("-" * 50) print(f"✅ Load medium model and continue to finetune...") print("-" * 50, "\n") ckpt = torch.load(f"{cfg.checkpoint_dir}/model_medium_bak.pth", map_location=f'cuda:{local_rank}', 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"]) ## Distributed model if cfg.world_size > 1: model = DistributedDataParallel(model, device_ids=[local_rank], output_device=local_rank, find_unused_parameters=True) world_rank == 0 and logger.info(f"start training ...") for epoch in range(current_epoch, cfg.finetune_step): if epoch > cfg.step_change_freq: num_rollout_steps = epoch // cfg.step_change_freq + 2 if num_rollout_steps > 12: # Paper: 2~12 curriculum training schedule, then skip to 20. num_rollout_steps = cfg.long_num_steps - cfg.medium_num_steps if epoch % cfg.step_change_freq == 0 and world_rank == 0: logger.info(f"⚠️ ⚠️ Switching to {num_rollout_steps}-step rollout!") datapipe = ERA5Datapipe( dataset_dir=cfg_data.dataset.data_dir, used_variables=cfg_data.dataset.channels, used_years=cfg_data.dataset.train_time, pattern='long', distributed=dist.is_initialized(), output_steps=num_rollout_steps, input_steps=2, 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, pattern='long', distributed=dist.is_initialized(), output_steps=num_rollout_steps, input_steps=2, batch_size=cfg_data.dataloader.batch_size, num_workers=cfg_data.dataloader.num_workers ) val_dataloader, val_sampler = datapipe.get_dataloader("valid") 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(local_rank, dtype=torch.float32) # B, T, C, H, W invar = invar.permute(0, 2, 1, 3, 4) # B, C, T, H, W outvar = data[1].to(local_rank, dtype=torch.float32) for t in range(outvar.shape[1]): if t < outvar.shape[1] - 1: with torch.no_grad(): outvar_pred = model(invar) # B, 70, 2, 721, 1440 invar[:, :, 0] = invar[:, :, -1] invar[:, :, -1] = outvar_pred.detach() else: with replace_function(model, ["cube_embedding", "u_transformer"], cfg.world_size > 1): outvar_pred = model(invar) loss = loss_obj(outvar_pred, outvar[:, t]) 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(local_rank, dtype=torch.float32) # B, T, C, H, W invar = invar.permute(0, 2, 1, 3, 4) # B, C, T, H, W outvar = data[1].to(local_rank, dtype=torch.float32) for t in range(outvar.shape[1]): outvar_pred = model(invar) # B, 70, 2, 721, 1440 invar[:, :, 0] = invar[:, :, -1] invar[:, :, -1] = outvar_pred.detach() loss = loss_obj(outvar_pred, outvar[:, -1]) if cfg.world_size > 1: loss_tensor = loss.detach().to(local_rank) 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'[{(time.time()-start_time)/(j+1): .02f}s/{cfg_data.dataloader.batch_size}batch] ' 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, epoch) 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, epoch): 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, "current_epoch": epoch } torch.save(state, f"{model_path}/model_long.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_long.pth {model_path}/model_long_bak.pth") if __name__ == "__main__": current_path = os.getcwd() sys.path.append(current_path) main()