pytorch使用horovod多gpu训练的实现

import torch
import horovod.torch as hvd

# Initialize Horovod 初始化horovod
hvd.init()

# Pin GPU to be used to process local rank (one GPU per process) 分配到每个gpu上
torch.cuda.set_device(hvd.local_rank())

# Define dataset... 定义dataset
train_dataset = ...

# Partition dataset among workers using DistributedSampler 对dataset的采样器进行调整，使用torch.utils.data.distributed.DistributedSampler
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=..., sampler=train_sampler)

# Build model...
model = ...
model.cuda()

optimizer = optim.SGD(model.parameters())

# Add Horovod Distributed Optimizer 使用Horovod的分布式优化器函数包裹在原先optimizer上
optimizer = hvd.DistributedOptimizer(optimizer, named_parameters=model.named_parameters())

# Broadcast parameters from rank 0 to all other processes. 参数广播到每个gpu上
hvd.broadcast_parameters(model.state_dict(), root_rank=0)

for epoch in range(100):
  for batch_idx, (data, target) in enumerate(train_loader):
    optimizer.zero_grad()
    output = model(data)
    loss = F.nll_loss(output, target)
    loss.backward()
    optimizer.step()
    if batch_idx % args.log_interval == 0:
      print('Train Epoch: {} [{}/{}]\tLoss: {}'.format(
        epoch, batch_idx * len(data), len(train_sampler), loss.item()))

  from __future__ import print_function
  
  import torch
  import argparse
  import torch.backends.cudnn as cudnn
  import torch.nn.functional as F
  import torch.optim as optim
  import torch.utils.data.distributed
  from torchvision import datasets, transforms, models
 import horovod.torch as hvd
 import os
 import math
 from tqdm import tqdm
 from distutils.version import LooseVersion
 
 # 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('--fp-allreduce', action='store_true', default=False,
           help='use fp compression during allreduce')
 parser.add_argument('--batches-per-allreduce', type=int, default=,
           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')

 # 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,
 44           help='learning rate for a single GPU')
 45 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')
 
 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.
 try:
   if LooseVersion(torch.__version__) >= LooseVersion('1.2.0'):
     from torch.utils.tensorboard import SummaryWriter
   else:
     from tensorboardX import SummaryWriter
   log_writer = SummaryWriter(args.log_dir) if hvd.rank() == 0 else None
 except ImportError:
   log_writer = 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 {}
 train_dataset =    datasets.ImageFolder(args.train_dir,
             transform=transforms.Compose([
               transforms.RandomResizedCrop(224),
               transforms.RandomHorizontalFlip(),
               transforms.ToTensor(),
               transforms.Normalize(mean=[., ., .],
                          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)
 
 # 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=)
 hvd.broadcast_optimizer_state(optimizer, root_rank=)
 
 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 + ),
        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
 
 
 for epoch in range(resume_from_epoch, args.epochs):
   train(epoch)
   validate(epoch)
   save_checkpoint(epoch)