train.py

"""
Modified from https://github.com/utsaslab/MONeT/blob/master/examples/imagenet.py
"""

import argparse
import json
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
import numpy as np

from scaled_resnet import scaled_resnet, scaled_wide_resnet

import actnn
from actnn import config, QScheme, QModule
from actnn import get_memory_usage, compute_tensor_bytes, exp_recorder
MB = 1024**2
GB = 1024**3

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',
                    help='model architecture')
parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
                    help='number of data loading workers (default: 4)')
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=64, type=int,
                    metavar='N',
                    help='mini-batch size (default: 64), 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('--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=2, 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')
parser.add_argument('--dist-backend', default='nccl', type=str,
                    help='distributed backend')
parser.add_argument('--seed', default=None, type=int,
                    help='seed for initializing training. ')
parser.add_argument('--gpu', default=None, type=int,
                    help='GPU id to use.')
parser.add_argument('--multiprocessing-distributed', action='store_true',
                    help='Use multi-processing distributed training to launch '
                         'N processes per node, which has N GPUs. This is the '
                         'fastest way to use PyTorch for either single node or '
                         'multi node data parallel training')
parser.add_argument('--alg', type=str, default="exact", help="Memory saving algorithm")
parser.add_argument('--input-size', type=int)
parser.add_argument('--get-macs', action='store_true')

best_acc1 = 0

def set_optimization_level(args):
    if args.alg == 'exact':
        pass
    elif args.alg.startswith('actnn-'):
        actnn.set_optimization_level(args.alg[6:])
    elif args.alg == 'swap':
        actnn.set_optimization_level('swap')
    else:
        raise ValueError("Invalid algorithm: " + args.alg)

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

    set_optimization_level(args)
    QScheme.num_samples = 1300000   # the size of training set

    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.')

    if args.gpu is not None:
        warnings.warn('You have chosen a specific GPU. This will completely '
                      'disable data parallelism.')

    if args.dist_url == "env://" and args.world_size == -1:
        args.world_size = int(os.environ["WORLD_SIZE"])

    args.distributed = args.world_size > 1 or args.multiprocessing_distributed

    ngpus_per_node = torch.cuda.device_count()

    if args.multiprocessing_distributed:
        # Since we have ngpus_per_node processes per node, the total world_size
        # needs to be adjusted accordingly
        args.world_size = ngpus_per_node * args.world_size
        # Use torch.multiprocessing.spawn to launch distributed processes: the
        # main_worker process function
        mp.spawn(main_worker, nprocs=ngpus_per_node, args=(ngpus_per_node, args))
    else:
        # Simply call main_worker function
        main_worker(args.gpu, ngpus_per_node, args)


def main_worker(gpu, ngpus_per_node, args):
    global best_acc1
    args.gpu = gpu

    if args.gpu is not None:
        print("Use GPU: {} for training".format(args.gpu))

    if args.distributed:
        if args.dist_url == "env://" and args.rank == -1:
            args.rank = int(os.environ["RANK"])
        if args.multiprocessing_distributed:
            # For multiprocessing distributed training, rank needs to be the
            # global rank among all the processes
            args.rank = args.rank * 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))
        if args.arch.startswith('scaled_wide_resnet'):
            model = scaled_wide_resnet(args.arch)
        elif args.arch.startswith('scaled_resnet'):
            model = scaled_resnet(args.arch)
        else:
            if args.arch in ['inception_v3']:
                kwargs = {"aux_logits": False}
            else:
                kwargs = {}
            model = models.__dict__[args.arch](**kwargs)

    if not torch.cuda.is_available():
        print('using CPU, this will be slow')
    elif args.distributed:
        # For multiprocessing distributed, DistributedDataParallel constructor
        # should always set the single device scope, otherwise,
        # DistributedDataParallel will use all available devices.
        if args.gpu is not None:
            torch.cuda.set_device(args.gpu)
            model.cuda(args.gpu)
            # 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 / ngpus_per_node)
            args.workers = int((args.workers + ngpus_per_node - 1) / ngpus_per_node)
            model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
        else:
            model.cuda()
            # DistributedDataParallel will divide and allocate batch_size to all
            # available GPUs if device_ids are not set
            model = torch.nn.parallel.DistributedDataParallel(model)
    elif args.gpu is not None:
        torch.cuda.set_device(args.gpu)
        model = model.cuda(args.gpu)
    else:
        # DataParallel will divide and allocate batch_size to all available GPUs
        if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
            model.features = torch.nn.DataParallel(model.features)
            model.cuda()
        else:
            model = torch.nn.DataParallel(model).cuda()

    if args.alg in ['swap'] or args.alg.startswith('actnn'):
        print("=> convert model")
        model = QModule(model)
        model.cuda()

    if config.debug_memory_model:
        print("========== Model Only ===========")
        usage = get_memory_usage(True)
        exp_recorder.record("network", args.arch)
        exp_recorder.record("algorithm", args.alg)
        exp_recorder.record("model_only", usage / GB, 2)


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

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

    # optionally resume from a checkpoint
    if args.resume:
        if os.path.isfile(args.resume):
            print("=> loading checkpoint '{}'".format(args.resume))
            if args.gpu is None:
                checkpoint = torch.load(args.resume)
            else:
                # Map model to be loaded to specified single gpu.
                loc = 'cuda:{}'.format(args.gpu)
                checkpoint = torch.load(args.resume, map_location=loc)
            args.start_epoch = checkpoint['epoch']
            best_acc1 = checkpoint['best_acc1']
            if args.gpu is not None:
                # best_acc1 may be from a checkpoint from a different GPU
                best_acc1 = best_acc1.to(args.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.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])

    if args.input_size is None:
        if args.arch in ['inception_v3']:
            input_size = 299
        else:
            input_size = 224
    else:
        input_size = args.input_size

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

    if args.distributed:
        train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
    else:
        train_sampler = None

    train_loader = actnn.dataloader.DataLoader(
        train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None),
        num_workers=args.workers, pin_memory=True, sampler=train_sampler)

    val_loader = torch.utils.data.DataLoader(
        datasets.ImageFolder(valdir, transforms.Compose([
            transforms.Resize(input_size),
            transforms.CenterCrop(input_size),
            transforms.ToTensor(),
            normalize,
        ])),
        batch_size=args.batch_size, shuffle=False,
        num_workers=args.workers, pin_memory=True)

    if args.get_macs:
        from thop import profile
        from thop.vision.basic_hooks import count_convNd, count_bn, count_linear
        from actnn.layers import QConv2d, QBatchNorm2d, QLinear
        if isinstance(model, QModule):
            QScheme.batch = [1]
            model = model.model
        model = model.module
        input = torch.randn(1, 3, input_size, input_size).cuda()
        macs, params = profile(model, inputs=(input, ),
            custom_ops={QConv2d: count_convNd, QBatchNorm2d: count_bn, QLinear: count_linear})
        print(f"Macs: {macs}\t Params: {params}")
        out_file = "get_macs.json"
        with open(out_file, 'w') as fout:
            fout.write(json.dumps([macs, params]))
        print(f"save results to {out_file}")
        exit()

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

    for epoch in range(args.start_epoch, args.epochs):
        if args.distributed:
            train_sampler.set_epoch(epoch)
        adjust_learning_rate(optimizer, epoch, args)

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

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

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

        if not args.multiprocessing_distributed or (args.multiprocessing_distributed
                and args.rank % ngpus_per_node == 0):
            save_checkpoint({
                'epoch': epoch + 1,
                'arch': args.arch,
                'state_dict': model.state_dict(),
                'best_acc1': best_acc1,
                'optimizer' : optimizer.state_dict(),
            }, is_best)


train_step_ct = 0
train_max_batch = 0
train_ips_list = []


def train(train_loader, model, criterion, optimizer, epoch, args):
    batch_time = AverageMeter('Time', ':.3f')
    data_time = AverageMeter('Data', ':.3f')
    losses = AverageMeter('Loss', ':.3f')
    top1 = AverageMeter('Acc@1', ':.2f')
    top5 = AverageMeter('Acc@5', ':.2f')
    ips = AverageMeter('IPS', ':.1f')
    progress = ProgressMeter(
        len(train_loader),
        [batch_time, data_time, losses, top1, top5, ips],
        prefix="Epoch: [{}]".format(epoch))

    # switch to train mode
    model.train()
    end = time.time()
    for i, (indices, (images, target)) in enumerate(train_loader):
        QScheme.batch = indices      # NOTE: only needed for use_gradient

        # measure data loading time
        data_time.update(time.time() - end)

        if config.debug_memory_model:
            print("========== Init Data Loader ===========")
            init_mem = get_memory_usage(True)
            exp_recorder.record("data_loader", init_mem / GB - exp_recorder.val_dict['model_only'], 2)

        if args.gpu is not None:
            images = images.cuda(args.gpu, non_blocking=True)
        if torch.cuda.is_available():
            target = target.cuda(args.gpu, non_blocking=True)

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

        if config.debug_memory_model:
            print("========== Before Backward ===========")
            before_backward = get_memory_usage(True)
            act_mem = get_memory_usage() - init_mem - compute_tensor_bytes([loss, output])
            res = "Batch size: %d\tTotal Mem: %.2f MB\tAct Mem: %.2f MB" % (
                    len(output), before_backward / MB, act_mem / MB)
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            del loss
            print("========== After Backward ===========")
            after_backward = get_memory_usage(True)
            total_mem = before_backward + (after_backward - init_mem)
            res = "Batch size: %d\tTotal Mem: %.2f MB\tAct Mem: %.2f MB" % (
                    len(output), total_mem / MB, act_mem / MB)
            print(res)
            exp_recorder.record("batch_size", len(output))
            exp_recorder.record("total", total_mem / GB, 2)
            exp_recorder.record("activation", act_mem / GB, 2)
            exp_recorder.dump('mem_results.json')
            exit()

        # 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()
        loss.backward()
        with torch.no_grad():
            optimizer.step()

        # measure elapsed time
        bs = len(images)
        batch_total_time = time.time() - end
        train_ips = bs / batch_total_time
        batch_time.update(batch_total_time)
        ips.update(train_ips)
        end = time.time()

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

        if config.debug_speed:
            global train_step_ct, train_ips_list, train_max_batch
            train_ips_list.append(train_ips)
            train_max_batch = max(train_max_batch, bs)
            if train_step_ct >= 4:
                train_ips = np.median(train_ips_list)
                res = "BatchSize: %d\tIPS: %.2f\t,Cost: %.2f ms" % (
                    bs, train_ips, 1000.0 / train_ips)
                print(res, flush=True)
                exp_recorder.record("network", args.arch)
                exp_recorder.record("algorithm", args.alg)
                exp_recorder.record("batch_size", train_max_batch)
                exp_recorder.record("ips", train_ips, 2)
                exp_recorder.record("tstamp", time.time(), 2)
                exp_recorder.dump('speed_results.json')
                exit(0)
            train_step_ct += 1


def validate(val_loader, model, criterion, 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()
        for i, (images, target) in enumerate(val_loader):
            if args.gpu is not None:
                images = images.cuda(args.gpu, non_blocking=True)
            if torch.cuda.is_available():
                target = target.cuda(args.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:
                progress.display(i)

        # 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, 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 + '})'
        fmtstr = '{name} {val' + 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, topk=(1,)):
    """Computes the accuracy over the k top predictions for the specified values of k"""
    if config.debug_speed:
        return [[-1]] * len(topk)

    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].reshape(-1).float().sum(0, keepdim=True)
            res.append(correct_k.mul_(100.0 / batch_size))
        return res


if __name__ == '__main__':
    # Run a matmul to initialize cublas first
    a = torch.ones((1, 1)).cuda()
    a = (a @ a).cpu()
    del a
    torch.cuda.empty_cache()

    main()