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129 lines
4.4 KiB
129 lines
4.4 KiB
import argparse
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from pathlib import Path
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import torch
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import torch.nn as nn
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import torchvision
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import torchvision.transforms as transforms
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from torch.optim.lr_scheduler import MultiStepLR
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import colossalai
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from colossalai.booster import Booster
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from colossalai.booster.plugin import TorchDDPPlugin
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from colossalai.cluster import DistCoordinator
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# ==============================
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# Parse Arguments
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# ==============================
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parser = argparse.ArgumentParser()
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parser.add_argument('-r', '--resume', type=int, default=-1, help="resume from the epoch's checkpoint")
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parser.add_argument('-c', '--checkpoint', type=str, default='./checkpoint', help="checkpoint directory")
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parser.add_argument('-i', '--interval', type=int, default=5, help="interval of saving checkpoint")
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parser.add_argument('-f', '--fp16', action='store_true', help="use fp16")
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args = parser.parse_args()
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# ==============================
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# Prepare Checkpoint Directory
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# ==============================
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Path(args.checkpoint).mkdir(parents=True, exist_ok=True)
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# ==============================
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# Prepare Hyperparameters
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# ==============================
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NUM_EPOCHS = 80
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LEARNING_RATE = 1e-3
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START_EPOCH = args.resume if args.resume >= 0 else 0
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# ==============================
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# Launch Distributed Environment
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# ==============================
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colossalai.launch_from_torch(config={})
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coordinator = DistCoordinator()
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# update the learning rate with linear scaling
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# old_gpu_num / old_lr = new_gpu_num / new_lr
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LEARNING_RATE *= coordinator.world_size
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# ==============================
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# Prepare Booster
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# ==============================
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plugin = TorchDDPPlugin()
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if args.fp16:
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booster = Booster(mixed_precision='fp16', plugin=plugin)
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else:
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booster = Booster(plugin=plugin)
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# ==============================
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# Prepare Train Dataset
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# ==============================
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transform = transforms.Compose(
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[transforms.Pad(4),
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transforms.RandomHorizontalFlip(),
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transforms.RandomCrop(32),
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transforms.ToTensor()])
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# CIFAR-10 dataset
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with coordinator.priority_execution():
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train_dataset = torchvision.datasets.CIFAR10(root='./data/', train=True, transform=transform, download=True)
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# ====================================
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# Prepare model, optimizer, criterion
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# ====================================
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# resent50
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model = torchvision.models.resnet18(num_classes=10).cuda()
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# Loss and optimizer
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criterion = nn.CrossEntropyLoss()
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optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)
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# lr scheduler
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lr_scheduler = MultiStepLR(optimizer, milestones=[20, 40, 60, 80], gamma=1 / 3)
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# prepare dataloader with torch ddp plugin
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train_dataloader = plugin.prepare_train_dataloader(train_dataset, batch_size=100, shuffle=True)
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# ==============================
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# Resume from checkpoint
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# ==============================
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if args.resume >= 0:
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booster.load_model(model, f'{args.checkpoint}/model_{args.resume}.pth')
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booster.load_optimizer(optimizer, f'{args.checkpoint}/optimizer_{args.resume}.pth')
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booster.load_lr_scheduler(lr_scheduler, f'{args.checkpoint}/lr_scheduler_{args.resume}.pth')
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# ==============================
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# Boost with ColossalAI
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# ==============================
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model, optimizer, criterion, train_dataloader, lr_scheduler = booster.boost(model, optimizer, criterion,
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train_dataloader, lr_scheduler)
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# ==============================
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# Train model
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# ==============================
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total_step = len(train_dataloader)
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for epoch in range(START_EPOCH, NUM_EPOCHS):
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for i, (images, labels) in enumerate(train_dataloader):
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images = images.cuda()
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labels = labels.cuda()
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# Forward pass
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outputs = model(images)
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loss = criterion(outputs, labels)
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# Backward and optimize
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optimizer.zero_grad()
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booster.backward(loss, optimizer)
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optimizer.step()
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if (i + 1) % 100 == 0:
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print("Epoch [{}/{}], Step [{}/{}] Loss: {:.4f}".format(epoch + 1, NUM_EPOCHS, i + 1, total_step,
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loss.item()))
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lr_scheduler.step()
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# save checkpoint every 5 epoch
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if (epoch + 1) % args.interval == 0:
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booster.save_model(model, f'{args.checkpoint}/model_{epoch + 1}.pth')
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booster.save_optimizer(optimizer, f'{args.checkpoint}/optimizer_{epoch + 1}.pth')
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booster.save_lr_scheduler(lr_scheduler, f'{args.checkpoint}/lr_scheduler_{epoch + 1}.pth')
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