4.apex_distributed.py

import argparse
import os
import random
import shutil
import time
import warnings

import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.multiprocessing as mp
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision.models as models

from apex import amp
from apex.parallel import DistributedDataParallel

model_names = sorted(name for name in models.__dict__
                     if name.islower() and not name.startswith("__") and callable(models.__dict__[name]))

parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
parser.add_argument('--data', metavar='DIR', default='/raid/xianchaow/pytorch-distributed/data/cifar10', help='path to dataset')
parser.add_argument('-a',
                    '--arch',
                    metavar='ARCH',
                    default='resnet18',
                    choices=model_names,
                    help='model architecture: ' + ' | '.join(model_names) + ' (default: resnet18)')
parser.add_argument('-j',
                    '--workers',
                    default=4,
                    type=int,
                    metavar='N',
                    help='number of data loading workers (default: 4)')
parser.add_argument('--epochs', default=10, type=int, metavar='N', help='number of total epochs to run')
parser.add_argument('--start-epoch', default=0, type=int, metavar='N', help='manual epoch number (useful on restarts)')
parser.add_argument('-b',
                    '--batch-size',
                    default=3200,
                    type=int,
                    metavar='N',
                    help='mini-batch size (default: 6400), this is the total '
                    'batch size of all GPUs on the current node when '
                    'using Data Parallel or Distributed Data Parallel')
parser.add_argument('--lr',
                    '--learning-rate',
                    default=0.1,
                    type=float,
                    metavar='LR',
                    help='initial learning rate',
                    dest='lr')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M', help='momentum')
parser.add_argument('--local_rank', default=-1, type=int, help='node rank for distributed training')
parser.add_argument('--wd',
                    '--weight-decay',
                    default=1e-4,
                    type=float,
                    metavar='W',
                    help='weight decay (default: 1e-4)',
                    dest='weight_decay')
parser.add_argument('-p', '--print-freq', default=10, type=int, metavar='N', help='print frequency (default: 10)')
parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true', help='evaluate model on validation set')
parser.add_argument('--pretrained', dest='pretrained', action='store_true', help='use pre-trained model')
parser.add_argument('--seed', default=None, type=int, help='seed for initializing training. ')


def reduce_mean(tensor, nprocs):
    rt = tensor.clone()
    dist.all_reduce(rt, op=dist.ReduceOp.SUM)
    rt /= nprocs
    return rt


class data_prefetcher():
    def __init__(self, loader):
        self.loader = iter(loader)
        self.stream = torch.cuda.Stream()
        # mean=[0.4915, 0.4823, 0.4468], std=[0.2470, 0.2435, 0.2616])
        self.mean = torch.tensor([0.4915 * 255, 0.4823 * 255, 0.4468 * 255]).cuda().view(1, 3, 1, 1)
        self.std = torch.tensor([0.2470 * 255, 0.2435 * 255, 0.2616 * 255]).cuda().view(1, 3, 1, 1)
        # With Amp, it isn't necessary to manually convert data to half.
        # if args.fp16:
        #     self.mean = self.mean.half()
        #     self.std = self.std.half()
        self.preload()

    def preload(self):
        try:
            self.next_input, self.next_target = next(self.loader)
        except StopIteration:
            self.next_input = None
            self.next_target = None
            return
        # if record_stream() doesn't work, another option is to make sure device inputs are created
        # on the main stream.
        # self.next_input_gpu = torch.empty_like(self.next_input, device='cuda')
        # self.next_target_gpu = torch.empty_like(self.next_target, device='cuda')
        # Need to make sure the memory allocated for next_* is not still in use by the main stream
        # at the time we start copying to next_*:
        # self.stream.wait_stream(torch.cuda.current_stream())
        with torch.cuda.stream(self.stream):
            self.next_input = self.next_input.cuda(non_blocking=True)
            self.next_target = self.next_target.cuda(non_blocking=True)
            # more code for the alternative if record_stream() doesn't work:
            # copy_ will record the use of the pinned source tensor in this side stream.
            # self.next_input_gpu.copy_(self.next_input, non_blocking=True)
            # self.next_target_gpu.copy_(self.next_target, non_blocking=True)
            # self.next_input = self.next_input_gpu
            # self.next_target = self.next_target_gpu

            # With Amp, it isn't necessary to manually convert data to half.
            # if args.fp16:
            #     self.next_input = self.next_input.half()
            # else:
            self.next_input = self.next_input.float()
            self.next_input = self.next_input.sub_(self.mean).div_(self.std)

    def next(self):
        torch.cuda.current_stream().wait_stream(self.stream)
        input = self.next_input
        target = self.next_target
        if input is not None:
            input.record_stream(torch.cuda.current_stream())
        if target is not None:
            target.record_stream(torch.cuda.current_stream())
        self.preload()
        return input, target


def main():
    args = parser.parse_args()
    args.nprocs = torch.cuda.device_count()

    if args.seed is not None:
        random.seed(args.seed)
        torch.manual_seed(args.seed)
        cudnn.deterministic = True
        warnings.warn('You have chosen to seed training. '
                      'This will turn on the CUDNN deterministic setting, '
                      'which can slow down your training considerably! '
                      'You may see unexpected behavior when restarting '
                      'from checkpoints.')

    main_worker(args.local_rank, args.nprocs, args)


def main_worker(local_rank, nprocs, args):
    best_acc1 = .0

    dist.init_process_group(backend='nccl')
    # create model
    if args.pretrained:
        print("=> using pre-trained model '{}'".format(args.arch))
        model = models.__dict__[args.arch](pretrained=True)
    else:
        print("=> creating model '{}'".format(args.arch))
        model = models.__dict__[args.arch]()

    torch.cuda.set_device(local_rank)
    model.cuda()
    # When using a single GPU per process and per
    # DistributedDataParallel, we need to divide the batch size
    # ourselves based on the total number of GPUs we have
    args.batch_size = int(args.batch_size / nprocs)

    # define loss function (criterion) and optimizer
    criterion = nn.CrossEntropyLoss().cuda()

    optimizer = torch.optim.SGD(model.parameters(), args.lr, momentum=args.momentum, weight_decay=args.weight_decay)

    model, optimizer = amp.initialize(model, optimizer)
    model = DistributedDataParallel(model)

    cudnn.benchmark = True

    # Data loading code
    traindir = os.path.join(args.data, 'train')
    valdir = os.path.join(args.data, 'val')
    normalize = transforms.Normalize(mean=[0.4915, 0.4823, 0.4468], std=[0.2470, 0.2435, 0.2616])

    #train_dataset = datasets.ImageFolder(
    #    traindir,
    #    transforms.Compose([
    #        transforms.RandomResizedCrop(224),
    #        transforms.RandomHorizontalFlip(),
    #        transforms.ToTensor(),
    #        normalize,
    #    ]))
    
    train_dataset = datasets.CIFAR10(traindir, train=True, download=True, transform=transforms.Compose([transforms.ToTensor(), normalize]))
    train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)

    train_loader = torch.utils.data.DataLoader(train_dataset,
                                               batch_size=args.batch_size,
                                               shuffle=(train_sampler is None),
                                               num_workers=2,
                                               pin_memory=True,
                                               sampler=train_sampler)

    val_dataset = datasets.CIFAR10(valdir, train=False, download=True, transform=transforms.Compose([transforms.ToTensor(), normalize]))
    val_sampler = torch.utils.data.distributed.DistributedSampler(val_dataset)
    val_loader = torch.utils.data.DataLoader(val_dataset,batch_size=args.batch_size,
                                             shuffle=False,
                                             num_workers=2,
                                             pin_memory=True,
                                             sampler=val_sampler)

    if args.evaluate:
        validate(val_loader, model, criterion, local_rank, args)
        return

    for epoch in range(args.start_epoch, args.epochs):
        train_sampler.set_epoch(epoch)
        val_sampler.set_epoch(epoch)

        adjust_learning_rate(optimizer, epoch, args)

        # train for one epoch
        train(train_loader, model, criterion, optimizer, epoch, local_rank, args)

        # evaluate on validation set
        acc1 = validate(val_loader, model, criterion, local_rank, args)

        # remember best acc@1 and save checkpoint
        is_best = acc1 > best_acc1
        best_acc1 = max(acc1, best_acc1)

        if args.local_rank == 0:
            save_checkpoint(
                {
                    'epoch': epoch + 1,
                    'arch': args.arch,
                    'state_dict': model.module.state_dict(),
                    'best_acc1': best_acc1,
                }, is_best)


def train(train_loader, model, criterion, optimizer, epoch, local_rank, args):
    batch_time = AverageMeter('Time', ':6.3f')
    data_time = AverageMeter('Data', ':6.3f')
    losses = AverageMeter('Loss', ':.4e')
    top1 = AverageMeter('Acc@1', ':6.2f')
    top5 = AverageMeter('Acc@5', ':6.2f')
    progress = ProgressMeter(len(train_loader), [batch_time, data_time, losses, top1, top5],
                             prefix="Epoch: [{}]".format(epoch))

    # switch to train mode
    model.train()

    end = time.time()
    prefetcher = data_prefetcher(train_loader)
    images, target = prefetcher.next()
    i = 0
    while images is not None:
        # measure data loading time
        data_time.update(time.time() - end)

        # compute output
        output = model(images)
        loss = criterion(output, target)

        # measure accuracy and record loss
        acc1, acc5 = accuracy(output, target, local_rank, topk=(1, 5))

        torch.distributed.barrier()

        reduced_loss = reduce_mean(loss, args.nprocs)
        reduced_acc1 = reduce_mean(acc1, args.nprocs)
        reduced_acc5 = reduce_mean(acc5, args.nprocs)

        losses.update(reduced_loss.item(), images.size(0))
        top1.update(reduced_acc1.item(), images.size(0))
        top5.update(reduced_acc5.item(), images.size(0))

        # compute gradient and do SGD step
        optimizer.zero_grad()
        with amp.scale_loss(loss, optimizer) as scaled_loss:
            scaled_loss.backward()
        optimizer.step()

        # measure elapsed time
        batch_time.update(time.time() - end)
        end = time.time()

        if i % args.print_freq == 0:
            progress.display(i)

        i += 1

        images, target = prefetcher.next()


def validate(val_loader, model, criterion, local_rank, args):
    batch_time = AverageMeter('Time', ':6.3f')
    losses = AverageMeter('Loss', ':.4e')
    top1 = AverageMeter('Acc@1', ':6.2f')
    top5 = AverageMeter('Acc@5', ':6.2f')
    progress = ProgressMeter(len(val_loader), [batch_time, losses, top1, top5], prefix='Test: ')

    # switch to evaluate mode
    model.eval()

    with torch.no_grad():
        end = time.time()
        prefetcher = data_prefetcher(val_loader)
        images, target = prefetcher.next()
        i = 0
        while images is not None:

            # compute output
            output = model(images)
            loss = criterion(output, target)

            # measure accuracy and record loss
            acc1, acc5 = accuracy(output, target, local_rank, topk=(1, 5))

            torch.distributed.barrier()

            reduced_loss = reduce_mean(loss, args.nprocs)
            reduced_acc1 = reduce_mean(acc1, args.nprocs)
            reduced_acc5 = reduce_mean(acc5, args.nprocs)

            losses.update(reduced_loss.item(), images.size(0))
            top1.update(reduced_acc1.item(), images.size(0))
            top5.update(reduced_acc5.item(), images.size(0))

            # measure elapsed time
            batch_time.update(time.time() - end)
            end = time.time()

            if i % args.print_freq == 0:
                progress.display(i)

            i += 1

            images, target = prefetcher.next()

        # TODO: this should also be done with the ProgressMeter
        print(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'.format(top1=top1, top5=top5))

    return top1.avg

def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
    filename = state['arch'] + '.' + filename
    torch.save(state, filename)
    if is_best:
        filename2 = state['arch'] + '.model_best.pth.tar'
        shutil.copyfile(filename, filename2)
        
def save_checkpoint1(state, is_best, filename='checkpoint.pth.tar'):
    torch.save(state, filename)
    if is_best:
        shutil.copyfile(filename, 'model_best.pth.tar')


class AverageMeter(object):
    """Computes and stores the average and current value"""
    def __init__(self, name, fmt=':f'):
        self.name = name
        self.fmt = fmt
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count

    def __str__(self):
        fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
        return fmtstr.format(**self.__dict__)


class ProgressMeter(object):
    def __init__(self, num_batches, meters, prefix=""):
        self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
        self.meters = meters
        self.prefix = prefix

    def display(self, batch):
        entries = [self.prefix + self.batch_fmtstr.format(batch)]
        entries += [str(meter) for meter in self.meters]
        print('\t'.join(entries))

    def _get_batch_fmtstr(self, num_batches):
        num_digits = len(str(num_batches // 1))
        fmt = '{:' + str(num_digits) + 'd}'
        return '[' + fmt + '/' + fmt.format(num_batches) + ']'


def adjust_learning_rate(optimizer, epoch, args):
    """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
    lr = args.lr * (0.1**(epoch // 30))
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr

def accuracy(output, target, local_rank, topk=(1, )):
    """Computes the accuracy over the k top predictions for the specified values of k"""
    with torch.no_grad():
        #maxk = max(topk)
        #batch_size = target.size(0)

        #_, pred = output.topk(maxk, 1, True, True)
        #pred = pred.t()
        #correct = pred.eq(target.view(1, -1).expand_as(pred))

        #res = []
        #for k in topk:
        #    correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
        #    res.append(correct_k.mul_(100.0 / batch_size))
        predy = torch.max(output, 1)[1].data.squeeze()
        acc = (predy == target).sum().item()/float(target.size(0))
        acc = torch.tensor(acc).cuda(local_rank)
        res = []
        res.append(acc)
        res.append(acc)
        return res
        
def accuracy2(output, target, topk=(1, )):
    """Computes the accuracy over the k top predictions for the specified values of k"""
    with torch.no_grad():
        maxk = max(topk)
        batch_size = target.size(0)

        _, pred = output.topk(maxk, 1, True, True)
        pred = pred.t()
        correct = pred.eq(target.view(1, -1).expand_as(pred))

        res = []
        for k in topk:
            correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
            res.append(correct_k.mul_(100.0 / batch_size))
        return res

if __name__ == '__main__':
    main()