import torch import argparse import torch.backends.cudnn as cudnn import torch.multiprocessing as mp import torch.nn.functional as F import torch.optim as optim import torch.utils.data.distributed from torch.utils.tensorboard import SummaryWriter from torchvision import datasets, transforms, models import horovod.torch as hvd import os import math from tqdm import tqdm # Training settings parser = argparse.ArgumentParser(description='PyTorch ImageNet Example', formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('--train-dir', default=os.path.expanduser('~/imagenet/train'), help='path to training data') parser.add_argument('--val-dir', default=os.path.expanduser('~/imagenet/validation'), help='path to validation data') parser.add_argument('--log-dir', default='./logs', help='tensorboard log directory') parser.add_argument('--checkpoint-format', default='./checkpoint-{epoch}.pth.tar', help='checkpoint file format') parser.add_argument('--fp16-allreduce', action='store_true', default=False, help='use fp16 compression during allreduce') parser.add_argument('--batches-per-allreduce', type=int, default=1, help='number of batches processed locally before ' 'executing allreduce across workers; it multiplies ' 'total batch size.') parser.add_argument('--use-adasum', action='store_true', default=False, help='use adasum algorithm to do reduction') parser.add_argument('--gradient-predivide-factor', type=float, default=1.0, help='apply gradient predivide factor in optimizer (default: 1.0)') # Default settings from https://arxiv.org/abs/1706.02677. parser.add_argument('--batch-size', type=int, default=32, help='input batch size for training') parser.add_argument('--val-batch-size', type=int, default=32, help='input batch size for validation') parser.add_argument('--epochs', type=int, default=90, help='number of epochs to train') parser.add_argument('--base-lr', type=float, default=0.0125, help='learning rate for a single GPU') parser.add_argument('--warmup-epochs', type=float, default=5, help='number of warmup epochs') parser.add_argument('--momentum', type=float, default=0.9, help='SGD momentum') parser.add_argument('--wd', type=float, default=0.00005, help='weight decay') parser.add_argument('--no-cuda', action='store_true', default=False, help='disables CUDA training') parser.add_argument('--seed', type=int, default=42, help='random seed') def train(epoch): model.train() train_sampler.set_epoch(epoch) train_loss = Metric('train_loss') train_accuracy = Metric('train_accuracy') with tqdm(total=len(train_loader), desc='Train Epoch #{}'.format(epoch + 1), disable=not verbose) as t: for batch_idx, (data, target) in enumerate(train_loader): adjust_learning_rate(epoch, batch_idx) if args.cuda: data, target = data.cuda(), target.cuda() optimizer.zero_grad() # Split data into sub-batches of size batch_size for i in range(0, len(data), args.batch_size): data_batch = data[i:i + args.batch_size] target_batch = target[i:i + args.batch_size] output = model(data_batch) train_accuracy.update(accuracy(output, target_batch)) loss = F.cross_entropy(output, target_batch) train_loss.update(loss) # Average gradients among sub-batches loss.div_(math.ceil(float(len(data)) / args.batch_size)) loss.backward() # Gradient is applied across all ranks optimizer.step() t.set_postfix({'loss': train_loss.avg.item(), 'accuracy': 100. * train_accuracy.avg.item()}) t.update(1) if log_writer: log_writer.add_scalar('train/loss', train_loss.avg, epoch) log_writer.add_scalar('train/accuracy', train_accuracy.avg, epoch) def validate(epoch): model.eval() val_loss = Metric('val_loss') val_accuracy = Metric('val_accuracy') with tqdm(total=len(val_loader), desc='Validate Epoch #{}'.format(epoch + 1), disable=not verbose) as t: with torch.no_grad(): for data, target in val_loader: if args.cuda: data, target = data.cuda(), target.cuda() output = model(data) val_loss.update(F.cross_entropy(output, target)) val_accuracy.update(accuracy(output, target)) t.set_postfix({'loss': val_loss.avg.item(), 'accuracy': 100. * val_accuracy.avg.item()}) t.update(1) if log_writer: log_writer.add_scalar('val/loss', val_loss.avg, epoch) log_writer.add_scalar('val/accuracy', val_accuracy.avg, epoch) # Horovod: using `lr = base_lr * hvd.size()` from the very beginning leads to worse final # accuracy. Scale the learning rate `lr = base_lr` ---> `lr = base_lr * hvd.size()` during # the first five epochs. See https://arxiv.org/abs/1706.02677 for details. # After the warmup reduce learning rate by 10 on the 30th, 60th and 80th epochs. def adjust_learning_rate(epoch, batch_idx): if epoch < args.warmup_epochs: epoch += float(batch_idx + 1) / len(train_loader) lr_adj = 1. / hvd.size() * (epoch * (hvd.size() - 1) / args.warmup_epochs + 1) elif epoch < 30: lr_adj = 1. elif epoch < 60: lr_adj = 1e-1 elif epoch < 80: lr_adj = 1e-2 else: lr_adj = 1e-3 for param_group in optimizer.param_groups: param_group['lr'] = args.base_lr * hvd.size() * args.batches_per_allreduce * lr_adj def accuracy(output, target): # get the index of the max log-probability pred = output.max(1, keepdim=True)[1] return pred.eq(target.view_as(pred)).cpu().float().mean() def save_checkpoint(epoch): if hvd.rank() == 0: filepath = args.checkpoint_format.format(epoch=epoch + 1) state = { 'model': model.state_dict(), 'optimizer': optimizer.state_dict(), } torch.save(state, filepath) # Horovod: average metrics from distributed training. class Metric(object): def __init__(self, name): self.name = name self.sum = torch.tensor(0.) self.n = torch.tensor(0.) def update(self, val): self.sum += hvd.allreduce(val.detach().cpu(), name=self.name) self.n += 1 @property def avg(self): return self.sum / self.n if __name__ == '__main__': args = parser.parse_args() args.cuda = not args.no_cuda and torch.cuda.is_available() allreduce_batch_size = args.batch_size * args.batches_per_allreduce hvd.init() torch.manual_seed(args.seed) if args.cuda: # Horovod: pin GPU to local rank. torch.cuda.set_device(hvd.local_rank()) torch.cuda.manual_seed(args.seed) cudnn.benchmark = True # If set > 0, will resume training from a given checkpoint. resume_from_epoch = 0 for try_epoch in range(args.epochs, 0, -1): if os.path.exists(args.checkpoint_format.format(epoch=try_epoch)): resume_from_epoch = try_epoch break # Horovod: broadcast resume_from_epoch from rank 0 (which will have # checkpoints) to other ranks. resume_from_epoch = hvd.broadcast(torch.tensor(resume_from_epoch), root_rank=0, name='resume_from_epoch').item() # Horovod: print logs on the first worker. verbose = 1 if hvd.rank() == 0 else 0 # Horovod: write TensorBoard logs on first worker. log_writer = SummaryWriter(args.log_dir) if hvd.rank() == 0 else None # Horovod: limit # of CPU threads to be used per worker. torch.set_num_threads(4) kwargs = {'num_workers': 4, 'pin_memory': True} if args.cuda else {} # When supported, use 'forkserver' to spawn dataloader workers instead of 'fork' to prevent # issues with Infiniband implementations that are not fork-safe if (kwargs.get('num_workers', 0) > 0 and hasattr(mp, '_supports_context') and mp._supports_context and 'forkserver' in mp.get_all_start_methods()): kwargs['multiprocessing_context'] = 'forkserver' train_dataset = \ datasets.ImageFolder(args.train_dir, transform=transforms.Compose([ transforms.RandomResizedCrop(224), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ])) # Horovod: use DistributedSampler to partition data among workers. Manually specify # `num_replicas=hvd.size()` and `rank=hvd.rank()`. train_sampler = torch.utils.data.distributed.DistributedSampler( train_dataset, num_replicas=hvd.size(), rank=hvd.rank()) train_loader = torch.utils.data.DataLoader( train_dataset, batch_size=allreduce_batch_size, sampler=train_sampler, **kwargs) val_dataset = \ datasets.ImageFolder(args.val_dir, transform=transforms.Compose([ transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ])) val_sampler = torch.utils.data.distributed.DistributedSampler( val_dataset, num_replicas=hvd.size(), rank=hvd.rank()) val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=args.val_batch_size, sampler=val_sampler, **kwargs) # Set up standard ResNet-50 model. model = models.resnet50() # By default, Adasum doesn't need scaling up learning rate. # For sum/average with gradient Accumulation: scale learning rate by batches_per_allreduce lr_scaler = args.batches_per_allreduce * hvd.size() if not args.use_adasum else 1 if args.cuda: # Move model to GPU. model.cuda() # If using GPU Adasum allreduce, scale learning rate by local_size. if args.use_adasum and hvd.nccl_built(): lr_scaler = args.batches_per_allreduce * hvd.local_size() # Horovod: scale learning rate by the number of GPUs. optimizer = optim.SGD(model.parameters(), lr=(args.base_lr * lr_scaler), momentum=args.momentum, weight_decay=args.wd) # Horovod: (optional) compression algorithm. compression = hvd.Compression.fp16 if args.fp16_allreduce else hvd.Compression.none # Horovod: wrap optimizer with DistributedOptimizer. optimizer = hvd.DistributedOptimizer( optimizer, named_parameters=model.named_parameters(), compression=compression, backward_passes_per_step=args.batches_per_allreduce, op=hvd.Adasum if args.use_adasum else hvd.Average, gradient_predivide_factor=args.gradient_predivide_factor) # Restore from a previous checkpoint, if initial_epoch is specified. # Horovod: restore on the first worker which will broadcast weights to other workers. if resume_from_epoch > 0 and hvd.rank() == 0: filepath = args.checkpoint_format.format(epoch=resume_from_epoch) checkpoint = torch.load(filepath) model.load_state_dict(checkpoint['model']) optimizer.load_state_dict(checkpoint['optimizer']) # Horovod: broadcast parameters & optimizer state. hvd.broadcast_parameters(model.state_dict(), root_rank=0) hvd.broadcast_optimizer_state(optimizer, root_rank=0) for epoch in range(resume_from_epoch, args.epochs): train(epoch) validate(epoch) save_checkpoint(epoch)