imagenet_ddp_mixprec.py

import argparse
import os
import shutil
import time

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.parallel import DistributedDataParallel as DDP
from apex import amp

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',
                    help='path to dataset')
parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet50',
                    choices=model_names,
                    help='model architecture: ' +
                        ' | '.join(model_names) +
                        ' (default: resnet50)')
parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
                    help='number of data loading workers (default: 4). '
                         'these are different from the processes that '
                         'run the programe. they are just for data loading')
parser.add_argument('--epochs', default=90, 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=896, type=int,
                    metavar='N',
                    help='mini-batch size (default: 896), this is the total '
                         'batch size of all GPUs on the current node when '
                         'using Data Parallel or Distributed Data Parallel'
                         'has to be a multiple of 8 to make use of Tensor'
                         'Cores. for GPU < 16 GB, max batch size is 224')
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('--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('--resume', default='', type=str, metavar='PATH',
                    help='path to latest checkpoint (default: none)')
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('--world-size', default=-1, type=int,
                    help='number of nodes for distributed training')
parser.add_argument('--rank', default=-1, type=int,
                    help='node rank for distributed training')
parser.add_argument('--dist-url', default='tcp://224.66.41.62:23456', type=str,
                    help='url used to set up distributed training. This should be'
                         'the IP address and open port number of the master node')
parser.add_argument('--dist-backend', default='nccl', type=str,
                    help='distributed backend')
parser.add_argument('--opt-level', default='O2', type=str)
parser.add_argument('--desired-acc', default=75.0, type=float,
                    help='Training will stop after desired-acc is reached.')

best_acc1 = 0


def main():
    args = parser.parse_args()

    args.ngpus_per_node = torch.cuda.device_count()

    # on each node we have: ngpus_per_node processes and ngpus_per_node gpus
    # that is, 1 process for each gpu on each node.
    # world_size is the total number of processes to run
    args.world_size = args.ngpus_per_node * args.world_size

    # Use torch.multiprocessing.spawn to launch distributed processes: the
    # main_worker process function
    mp.spawn(main_worker, nprocs=args.ngpus_per_node, args=(args))


def main_worker(gpu, args):
    """
    :param gpu: this is the process index, mp.spawn will assign this for you, goes from 0 to ngpus - 1 for the curr node
    :param args:
    :return:
    """
    global best_acc1
    print("Use GPU: {} for training".format(gpu))

    # For multiprocessing distributed training, rank needs to be the
    # global rank among all the processes across all nodes
    # This is “blocking,” meaning that no process will continue until all processes have joined.
    args.rank = args.rank * args.ngpus_per_node + gpu
    dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
                            world_size=args.world_size, rank=args.rank)

    # 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]()

    # For multiprocessing distributed, DistributedDataParallel constructor
    # should always set the single device scope, otherwise,
    # DistributedDataParallel will use all available devices.
    torch.cuda.set_device(gpu)
    model.cuda(gpu)

    # When using a single GPU per process and per
    # DistributedDataParallel, we need to divide the per node batch size
    # ourselves based on the total number of GPUs we have
    args.batch_size = int(args.batch_size / args.ngpus_per_node)  # calculate local batch size for each GPU
    args.workers = int((args.workers + args.ngpus_per_node - 1) / args.ngpus_per_node)

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

    # Scale learning rate based on global batch size
    args.lr = args.lr * float(args.batch_size * args.ngpus_per_node * args.world_size) / 256.

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

    ##############################################################
    # the model must already be on the correct GPU before calling amp.initialize.
    # The opt_level goes from O0 through O3, which uses different degrees of mixed-precision
    # We no longer have to specify the GPUs because Apex only allows one GPU per process.
    model, optimizer = amp.initialize(model, optimizer, opt_level=args.opt_level)
    model = DDP(model)
    ##############################################################

    # optionally resume from a checkpoint
    if args.resume:
        if os.path.isfile(args.resume):
            print("=> loading checkpoint '{}'".format(args.resume))
            # Map model to be loaded to specified single gpu.
            loc = 'cuda:{}'.format(gpu)
            checkpoint = torch.load(args.resume, map_location=loc)
            args.start_epoch = checkpoint['epoch']
            best_acc1 = checkpoint['best_acc1']
            # best_acc1 may be from a checkpoint from a different GPU
            best_acc1 = best_acc1.to(gpu)
            model.load_state_dict(checkpoint['state_dict'])
            optimizer.load_state_dict(checkpoint['optimizer'])
            print("=> loaded checkpoint '{}' (epoch {})"
                  .format(args.resume, checkpoint['epoch']))
        else:
            print("=> no checkpoint found at '{}'".format(args.resume))

    # cudnn will look for the optimal set of algorithms for that
    # particular configuration. this will have faster runtime if
    # your input sizes does not change at each iteration
    cudnn.benchmark = True

    # Data loading code
    traindir = os.path.join(args.data, 'train')
    valdir = os.path.join(args.data, 'val')
    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                     std=[0.229, 0.224, 0.225])

    train_dataset = datasets.ImageFolder(
        traindir,
        transforms.Compose([
            transforms.RandomResizedCrop(224),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
            normalize,
        ]))

    # makes sure that each process gets a different slice of the training data
    # during distributed training
    train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)

    # notice we turn off shuffling and use distributed data sampler
    train_loader = torch.utils.data.DataLoader(
        train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None),
        num_workers=args.workers, pin_memory=True, sampler=train_sampler)

    val_dataset = datasets.ImageFolder(
        valdir,
        transforms.Compose([
            transforms.Resize(256),
            transforms.CenterCrop(224),
            transforms.ToTensor(),
            normalize,
        ]))

    val_loader = torch.utils.data.DataLoader(
        val_dataset,
        batch_size=args.batch_size, shuffle=False,
        num_workers=args.workers, pin_memory=True)

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

    end = time.time()
    for epoch in range(args.start_epoch, args.epochs):
        train_sampler.set_epoch(epoch)

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

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

        if args.rank % args.ngpus_per_node == 0:
            # remember best acc@1 and save checkpoint
            is_best = acc1 > best_acc1
            best_acc1 = max(acc1, best_acc1)
            save_checkpoint({
                'epoch': epoch + 1,
                'arch': args.arch,
                'state_dict': model.state_dict(),
                'best_acc1': best_acc1,
                'optimizer': optimizer.state_dict(),
            }, is_best)


def train(train_loader, model, criterion, optimizer, epoch, gpu, args):
    batch_time = AverageMeter()
    losses = AverageMeter()
    top1 = AverageMeter()
    top5 = AverageMeter()

    # switch to train mode
    model.train()

    end = time.time()
    for i, (images, target) in enumerate(train_loader):

        images = images.cuda(gpu, non_blocking=True)
        target = target.cuda(gpu, non_blocking=True)

        adjust_learning_rate(optimizer, epoch, i, len(train_loader), args)

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

        # measure accuracy and record loss
        acc1, acc5 = accuracy(output, target, topk=(1, 5))
        losses.update(loss.item(), images.size(0))
        top1.update(acc1[0], images.size(0))
        top5.update(acc5[0], images.size(0))

        # compute gradient and do SGD step
        optimizer.zero_grad()
        ##############################################################
        # Mixed-precision training requires that the loss is scaled in order
        # to prevent the gradients from underflowing.
        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:
            print('Epoch: [{0}][{1}/{2}]\t'
                  'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                  'Speed {3:.3f} ({4:.3f})\t'
                  'Loss {loss.val:.10f} ({loss.avg:.4f})\t'
                  'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
                  'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
                  epoch, i, len(train_loader),
                  args.world_size * args.batch_size * args.ngpus_per_node / batch_time.val,
                  args.world_size * args.batch_size * args.ngpus_per_node / batch_time.avg,
                  batch_time=batch_time,
                  loss=losses, top1=top1, top5=top5))


def validate(val_loader, model, criterion, gpu, args):
    batch_time = AverageMeter()
    losses = AverageMeter()
    top1 = AverageMeter()
    top5 = AverageMeter()

    # switch to evaluate mode
    model.eval()

    with torch.no_grad():
        end = time.time()
        for i, (images, target) in enumerate(val_loader):
            images = images.cuda(gpu, non_blocking=True)
            target = target.cuda(gpu, non_blocking=True)

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

            # measure accuracy and record loss
            acc1, acc5 = accuracy(output, target, topk=(1, 5))
            losses.update(loss.item(), images.size(0))
            top1.update(acc1[0], images.size(0))
            top5.update(acc5[0], images.size(0))

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

            if i % args.print_freq == 0:
                print('Test: [{0}/{1}]\t'
                      'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                      'Speed {2:.3f} ({3:.3f})\t'
                      'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
                      'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
                      'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
                      i, len(val_loader),
                      args.world_size * args.batch_size * args.ngpus_per_node / batch_time.val,
                      args.world_size * args.batch_size * args.ngpus_per_node / batch_time.avg,
                      batch_time=batch_time, loss=losses,
                      top1=top1, top5=top5))

        # 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'):
    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):
        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 adjust_learning_rate(optimizer, epoch, step, len_epoch, args):
    """LR schedule that should yield 76% converged accuracy with batch size 256"""
    factor = epoch // 30

    if epoch >= 80:
        factor = factor + 1

    lr = args.lr*(0.1**factor)

    """Warmup"""
    if epoch < 5:
        lr = lr*float(1 + step + epoch*len_epoch)/(5.*len_epoch)

    # if(args.local_rank == 0):
    #     print("epoch = {}, step = {}, lr = {}".format(epoch, step, lr))

    for param_group in optimizer.param_groups:
        param_group['lr'] = lr


def accuracy(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()