231 lines
9.1 KiB
Python
231 lines
9.1 KiB
Python
import os
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import time
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import argparse
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import tomllib
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from pathlib import Path
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import numpy as np
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from tqdm import tqdm
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import torch
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import torch.distributed as dist
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import torch.multiprocessing as mp
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from torch import nn
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from torch.utils.data import DataLoader
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from torch.utils.data.distributed import DistributedSampler
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from torch.nn.parallel import DistributedDataParallel as DDP
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from torch.utils.tensorboard import SummaryWriter
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from MOAFUtils import print_with_timestamp
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from MOAFDatasets import MOAFDataset
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from MOAFModels import MOAFNoFusion, MOAFWithFiLM, MOAFWithChannelCrossAttention, MOAFWithSE
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def train_epoch(model, train_loader, epoch, epochs, device, optimizer, loss_fn, rank):
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model.train()
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train_loss = 0.0
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data_iter = tqdm(train_loader, desc=f"Epoch {epoch+1:03d}/{epochs:03d} [Train]") if rank in {-1, 0} else train_loader
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for data in data_iter:
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images, labels = data["image"].to(device, non_blocking=True), data["label"].to(device, non_blocking=True)
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params = torch.stack((data["mag"], data["na"], data["rix"]), dim=1).to(device, non_blocking=True)
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optimizer.zero_grad()
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outputs = model(images, params)
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loss = loss_fn(outputs.squeeze(1), labels)
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loss.backward()
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optimizer.step()
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train_loss += loss.item()
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return train_loss
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def valid_epoch(model, val_loader, epoch, epochs, device, loss_fn, rank):
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model.eval()
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val_loss = 0.0
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data_iter = tqdm(val_loader, desc=f"Epoch {epoch+1:03d}/{epochs:03d} [Valid]") if rank in {-1, 0} else val_loader
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with torch.no_grad():
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for data in data_iter:
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images, labels = data["image"].to(device, non_blocking=True), data["label"].to(device, non_blocking=True)
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params = torch.stack((data["mag"], data["na"], data["rix"]), dim=1).to(device, non_blocking=True)
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outputs = model(images, params)
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loss = loss_fn(outputs.squeeze(1), labels)
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val_loss += loss.item()
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return val_loss
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def fit(rank, world_size, cfg):
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# 初始化分布式参数
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os.environ['MASTER_ADDR'] = '127.0.0.1'
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os.environ['MASTER_PORT'] = cfg.get("master_port", "29500")
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dist.init_process_group(backend='nccl', rank=rank, world_size=world_size)
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torch.cuda.set_device(rank)
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device = torch.device(f'cuda:{rank}')
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if rank == 0:
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print_with_timestamp(f"Distributed initialized. World size: {world_size}")
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# 确定超参数
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model_type = cfg["model_type"]
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output_type = cfg["output_type"]
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dataset_dir = cfg["dataset_dir"]
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batch_size = int(cfg["batch_size"])
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num_workers = int(cfg["num_workers"])
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lr = float(cfg["lr"])
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epochs = int(cfg["epochs"])
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warmup_epochs = int(cfg["warmup_epochs"])
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objective_params_list = cfg["train_objective_params_list"]
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checkpoint_load = cfg["checkpoint_load"]
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# 加载数据集
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train_set = MOAFDataset(dataset_dir, "train", objective_params_list, output_type)
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val_set = MOAFDataset(dataset_dir, "val", objective_params_list, output_type)
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# 分布式化数据集
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train_sampler = DistributedSampler(train_set, num_replicas=world_size, rank=rank, shuffle=True)
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val_sampler = DistributedSampler(val_set, num_replicas=world_size, rank=rank, shuffle=False)
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train_loader = DataLoader(
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train_set, batch_size=batch_size, num_workers=num_workers,
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shuffle=False, pin_memory=True, persistent_workers=True, sampler=train_sampler
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)
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val_loader = DataLoader(
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val_set, batch_size=batch_size, num_workers=num_workers,
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shuffle=False, pin_memory=True, persistent_workers=True, sampler=val_sampler
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)
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if rank == 0:
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print_with_timestamp("Dataset Loaded (Distributed)")
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# 模型选择
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if "film" in model_type:
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fusion_depth_list = [int(ch) for ch in model_type[4:]]
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model = MOAFWithFiLM(fusion_depth_list).to(device)
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elif "cca" in model_type:
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fusion_depth_list = [int(ch) for ch in model_type[3:]]
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model = MOAFWithChannelCrossAttention(fusion_depth_list).to(device)
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elif "se" in model_type:
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fusion_depth_list = [int(ch) for ch in model_type[2:]]
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model = MOAFWithSE(fusion_depth_list).to(device)
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else:
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model = MOAFNoFusion().to(device)
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# 形式化预训练参数加载
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if checkpoint_load:
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if Path(f"ckpts/{model_type}_{output_type}_best_model.pt").exists():
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checkpoint = torch.load(f"ckpts/{model_type}_{output_type}_best_model.pt", map_location=device, weights_only=False)
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model.load_state_dict(checkpoint["model_state_dict"])
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print_with_timestamp("Model Checkpoint Params Loaded")
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else:
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print_with_timestamp("Model Checkpoint Params Not Exist")
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# DDP 化模型
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model = DDP(model, device_ids=[rank], output_device=rank, find_unused_parameters=False)
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if rank == 0:
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print_with_timestamp("Model Wrapped with DDP")
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# 损失函数、优化器、学习率调度器
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loss_fn = nn.HuberLoss()
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optimizer = torch.optim.Adam(model.parameters(), lr=lr)
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scheduler = torch.optim.lr_scheduler.LambdaLR(
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optimizer,
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lr_lambda=lambda epoch: (epoch + 1) / warmup_epochs if epoch < warmup_epochs
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else 0.5 * (1 + np.cos(np.pi * (epoch - warmup_epochs) / (epochs - warmup_epochs)))
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)
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# Tensorboard 上显示模型结构
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if rank == 0:
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tb_writer = SummaryWriter(log_dir=f"runs/{model_type}_{output_type}")
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dummy_input1, dummy_input2 = torch.randn(5, 3, 384, 384).to(device), torch.randn(5, 3).to(device)
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tb_writer.add_graph(model.module, (dummy_input1, dummy_input2))
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else:
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tb_writer = None
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# 训练
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best_val_loss = float('inf')
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if rank == 0:
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print_with_timestamp("Start training (DDP)")
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for epoch in range(epochs):
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# 必须在每个 epoch 开头设置 epoch 给 sampler，这样 shuffle 能跨 epoch 工作
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train_sampler.set_epoch(epoch)
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val_sampler.set_epoch(epoch)
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start_time = time.time()
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avg_train_loss = train_epoch(model, train_loader, epoch, epochs, device, optimizer, loss_fn, rank) / len(train_loader)
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avg_val_loss = valid_epoch(model, val_loader, epoch, epochs, device, loss_fn, rank) / len(val_loader)
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current_lr = optimizer.param_groups[0]['lr']
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scheduler.step()
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epoch_time = time.time() - start_time
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# 只有 rank 0 写 tensorboard 和保存 checkpoint，避免重复 IO
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if rank == 0:
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tb_writer.add_scalar('Loss/train', avg_train_loss, epoch)
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tb_writer.add_scalar('Loss/val', avg_val_loss, epoch)
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tb_writer.add_scalar('LearningRate', current_lr, epoch)
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tb_writer.add_scalar('Time/epoch', epoch_time, epoch)
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print_with_timestamp(f"Epoch {epoch+1}/{epochs} | "
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f"Train Loss: {avg_train_loss:.6f} | "
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f"Val Loss: {avg_val_loss:.6f} | "
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f"LR: {current_lr:.2e} | "
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f"Time: {epoch_time:.2f}s")
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if avg_val_loss < best_val_loss:
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best_val_loss = avg_val_loss
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save_dict = {
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"epoch": epoch,
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# 保存 module.state_dict()（DDP 包裹时用 module）
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"model_state_dict": model.module.state_dict(),
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"optimizer_state_dict": optimizer.state_dict(),
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"scheduler_state_dict": scheduler.state_dict(),
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"train_loss": avg_train_loss,
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"val_loss": avg_val_loss
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}
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Path("ckpts").mkdir(exist_ok=True, parents=True)
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torch.save(save_dict, f"ckpts/{model_type}_{output_type}_best_model.pt")
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print_with_timestamp(f"New best model saved at epoch {epoch+1}")
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# 清除进程
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if tb_writer is not None:
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tb_writer.close()
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dist.destroy_process_group()
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if rank == 0:
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print_with_timestamp("Training completed and distributed cleaned up")
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def parse_args_and_cfg():
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parser = argparse.ArgumentParser(description="DDP train script that loads config from a TOML file.")
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parser.add_argument("config", help="Path to TOML config file (e.g., config.toml)")
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parser.add_argument("--nproc_per_node", type=int, default=None, help="number of processes (GPUs) to use on this node")
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args = parser.parse_args()
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with open(args.config, "rb") as f:
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cfg = tomllib.load(f)
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return args, cfg
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def main():
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args, cfg = parse_args_and_cfg()
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# 自动检测可用 GPU 数量，除非用户指定 nproc_per_node
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available_gpus = torch.cuda.device_count()
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if available_gpus == 0:
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raise RuntimeError("No CUDA devices available for DDP. Use a GPU machine.")
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nproc = args.nproc_per_node if args.nproc_per_node is not None else available_gpus
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if nproc > available_gpus:
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raise ValueError(f"Requested {nproc} processes but only {available_gpus} GPUs available")
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# 使用 spawn 启动 nproc 个子进程，每个进程运行 fit(rank, world_size, cfg)
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# spawn 会在每个子进程中调用 fit(rank, world_size, cfg) 并传入不同的 rank
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mp.spawn(fit, args=(nproc, cfg), nprocs=nproc, join=True)
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if __name__ == "__main__":
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main()
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