main.py

import argparse
import os
import shutil
import time
import random

import numpy as np
import torch
from torch.autograd import Variable
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.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from image_classification.quantize import config
from image_classification.preconditioner import init

try:
    from apex.parallel import DistributedDataParallel as DDP
    from apex.fp16_utils import *
    from apex import amp
except ImportError:
    raise ImportError("Please install apex from https://www.github.com/nvidia/apex to run this example.")

import image_classification.resnet as models
import image_classification.logger as log

from image_classification.smoothing import LabelSmoothing
from image_classification.mixup import NLLMultiLabelSmooth, MixUpWrapper
from image_classification.dataloaders import *
from image_classification.training import *
from image_classification.utils import *


def add_parser_arguments(parser):
    model_names = models.resnet_versions.keys()
    model_configs = models.resnet_configs.keys()

    parser.add_argument('data', metavar='DIR',
                        help='path to dataset')
    parser.add_argument('--dataset', type=str, default='imagenet')

    parser.add_argument('--data-backend', metavar='BACKEND', default='pytorch',
                        choices=DATA_BACKEND_CHOICES)

    parser.add_argument('--arch', '-a', metavar='ARCH', default='resnet50',
                        choices=model_names,
                        help='model architecture: ' +
                        ' | '.join(model_names) +
                        ' (default: resnet50)')

    parser.add_argument('--model-config', '-c', metavar='CONF', default='fanin',
                        choices=model_configs,
                        help='model configs: ' +
                        ' | '.join(model_configs) + '(default: classic)')

    parser.add_argument('-j', '--workers', default=5, type=int, metavar='N',
                        help='number of data loading workers (default: 5)')
    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: 256) per gpu')

    parser.add_argument('--optimizer-batch-size', default=-1, type=int,
                        metavar='N', help='size of a total batch size, for simulating bigger batches')

    parser.add_argument('--lr', '--learning-rate', default=0.512, type=float,
                        metavar='LR', help='initial learning rate')
    parser.add_argument('--lr-schedule', default='cosine', type=str, metavar='SCHEDULE', choices=['step','linear','cosine'])

    parser.add_argument('--warmup', default=4, type=int,
                        metavar='E', help='number of warmup epochs')

    parser.add_argument('--label-smoothing', default=0.1, type=float,
                        metavar='S', help='label smoothing')
    parser.add_argument('--mixup', default=0.0, type=float,
                        metavar='ALPHA', help='mixup alpha')

    parser.add_argument('--momentum', default=0.875, type=float, metavar='M',
                        help='momentum')
    parser.add_argument('--weight-decay', '--wd', default=3.0517578125e-05, type=float,
                        metavar='W', help='weight decay (default: 1e-4)')
    parser.add_argument('--bn-weight-decay', action='store_true',
                        help='use weight_decay on batch normalization learnable parameters, default: false)')
    parser.add_argument('--nesterov', action='store_true',
                        help='use nesterov momentum, default: false)')

    parser.add_argument('--print-freq', '-p', default=100, 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('--pretrained-weights', default='', type=str, metavar='PATH',
                        help='load weights from here')

    parser.add_argument('--fp16', action='store_true',
                        help='Run model fp16 mode.')
    parser.add_argument('--static-loss-scale', type=float, default=1,
                        help='Static loss scale, positive power of 2 values can improve fp16 convergence.')
    parser.add_argument('--dynamic-loss-scale', action='store_true',
                        help='Use dynamic loss scaling.  If supplied, this argument supersedes ' +
                        '--static-loss-scale.')
    parser.add_argument('--prof', type=int, default=-1,
                        help='Run only N iterations')
    parser.add_argument('--amp', action='store_true',
                        help='Run model AMP (automatic mixed precision) mode.')

    parser.add_argument("--local_rank", default=0, type=int)

    parser.add_argument('--seed', default=None, type=int,
                        help='random seed used for np and pytorch')

    parser.add_argument('--gather-checkpoints', action='store_true',
                        help='Gather checkpoints throughout the training')

    parser.add_argument('--raport-file', default='raport.json', type=str,
                        help='file in which to store JSON experiment raport')

    parser.add_argument('--final-weights', default='model.pth.tar', type=str,
                        help='file in which to store final model weights')

    parser.add_argument('--evaluate', action='store_true', help='evaluate checkpoint/model')
    parser.add_argument('--training-only', action='store_true', help='do not evaluate')

    parser.add_argument('--no-checkpoints', action='store_false', dest='save_checkpoints')

    parser.add_argument('--workspace', type=str, default='./')

    def str2bool(v):
        if isinstance(v, bool):
            return v
        if v.lower() in ('yes', 'true', 't', 'y', '1'):
            return True
        elif v.lower() in ('no', 'false', 'f', 'n', '0'):
            return False
        else:
            raise argparse.ArgumentTypeError('Boolean value expected.')

    parser.add_argument('--qa', type=str2bool, default=True, help='quantize activation')
    parser.add_argument('--qw', type=str2bool, default=True, help='quantize weights')
    parser.add_argument('--qg', type=str2bool, default=True, help='quantize gradients')
    parser.add_argument('--biased', type=str2bool, default=False, help='biased quantization')
    parser.add_argument('--abits', type=int, default=8, help='activation number of bits')
    parser.add_argument('--wbits', type=int, default=8, help='weight number of bits')
    parser.add_argument('--biasbits', type=int, default=16, help='bias number of bits')
    parser.add_argument('--bbits', type=int, default=8, help='backward number of bits')
    parser.add_argument('--bwbits', type=int, default=8, help='backward weight number of bits')
    parser.add_argument('--persample', type=str2bool, default=False, help='per-sample quantization of gradients')
    parser.add_argument('--hadamard', type=str2bool, default=False, help='apply Hadamard transformation on gradients')
    parser.add_argument('--biprecision', type=str2bool, default=True, help='Gradient bifurcation')


def main(args):
    config.quantize_activation = args.qa
    config.quantize_weights = args.qw
    config.quantize_gradient = args.qg
    config.activation_num_bits = args.abits
    config.weight_num_bits = args.wbits
    config.bias_num_bits = args.biasbits
    config.backward_num_bits = args.bbits
    config.bweight_num_bits = args.bwbits
    config.backward_persample = args.persample
    config.hadamard = args.hadamard
    config.biased = args.biased
    config.biprecision = args.biprecision
    init(args.batch_size)

    exp_start_time = time.time()
    global best_prec1
    best_prec1 = 0

    args.distributed = False
    if 'WORLD_SIZE' in os.environ:
        args.distributed = int(os.environ['WORLD_SIZE']) > 1

    args.gpu = 0
    args.world_size = 1

    if args.distributed:
        args.gpu = args.local_rank % torch.cuda.device_count()
        torch.cuda.set_device(args.gpu)
        dist.init_process_group(backend='nccl', init_method='env://')
        args.world_size = torch.distributed.get_world_size()

    if args.amp and args.fp16:
        print("Please use only one of the --fp16/--amp flags")
        exit(1)

    if args.seed is not None:
        print("Using seed = {}".format(args.seed))
        torch.manual_seed(args.seed + args.local_rank)
        torch.cuda.manual_seed(args.seed + args.local_rank)
        np.random.seed(seed=args.seed + args.local_rank)
        random.seed(args.seed + args.local_rank)

        def _worker_init_fn(id):
            np.random.seed(seed=args.seed + args.local_rank + id)
            random.seed(args.seed + args.local_rank + id)
    else:
        def _worker_init_fn(id):
            pass

    if args.fp16:
        assert torch.backends.cudnn.enabled, "fp16 mode requires cudnn backend to be enabled."

    if args.static_loss_scale != 1.0:
        if not args.fp16:
            print("Warning:  if --fp16 is not used, static_loss_scale will be ignored.")

    if args.optimizer_batch_size < 0:
        batch_size_multiplier = 1
    else:
        tbs = args.world_size * args.batch_size
        if args.optimizer_batch_size % tbs != 0:
            print("Warning: simulated batch size {} is not divisible by actual batch size {}".format(args.optimizer_batch_size, tbs))
        batch_size_multiplier = int(args.optimizer_batch_size/ tbs)
        print("BSM: {}".format(batch_size_multiplier))

    pretrained_weights = None
    if args.pretrained_weights:
        if os.path.isfile(args.pretrained_weights):
            print("=> loading pretrained weights from '{}'".format(args.pretrained_weights))
            pretrained_weights = torch.load(args.pretrained_weights)
        else:
            print("=> no pretrained weights found at '{}'".format(args.resume))

    # optionally resume from a checkpoint
    if args.resume:
        if os.path.isfile(args.resume):
            print("=> loading checkpoint '{}'".format(args.resume))
            checkpoint = torch.load(args.resume, map_location = lambda storage, loc: storage.cuda(args.gpu))
            args.start_epoch = checkpoint['epoch']
            best_prec1 = checkpoint['best_prec1']
            model_state = checkpoint['state_dict']
            optimizer_state = checkpoint['optimizer']
            print("=> loaded checkpoint '{}' (epoch {})"
                  .format(args.resume, checkpoint['epoch']))
        else:
            print("=> no checkpoint found at '{}'".format(args.resume))
            model_state = None
            optimizer_state = None
    else:
        model_state = None
        optimizer_state = None


    loss = nn.CrossEntropyLoss
    if args.mixup > 0.0:
        loss = lambda: NLLMultiLabelSmooth(args.label_smoothing)
    elif args.label_smoothing > 0.0:
        loss = lambda: LabelSmoothing(args.label_smoothing)

    model_and_loss = ModelAndLoss(
            (args.arch, args.model_config),
            loss,
            pretrained_weights=pretrained_weights,
            cuda = True, fp16 = args.fp16)

    # Create data loaders and optimizers as needed
    if args.dataset == 'cifar10':
        get_train_loader = get_pytorch_train_loader_cifar10
        get_val_loader = get_pytorch_val_loader_cifar10
        get_debug_loader = get_pytorch_debug_loader_cifar10
    elif args.data_backend == 'pytorch':
        get_train_loader = get_pytorch_train_loader
        get_val_loader = get_pytorch_val_loader
        get_debug_loader = get_pytorch_val_loader
    elif args.data_backend == 'dali-gpu':
        get_train_loader = get_dali_train_loader(dali_cpu=False)
        get_val_loader = get_dali_val_loader()
    elif args.data_backend == 'dali-cpu':
        get_train_loader = get_dali_train_loader(dali_cpu=True)
        get_val_loader = get_dali_val_loader()

    train_loader, train_loader_len = get_train_loader(args.data, args.batch_size, 1000, args.mixup > 0.0, workers=args.workers, fp16=args.fp16)
    if args.mixup != 0.0:
        train_loader = MixUpWrapper(args.mixup, 1000, train_loader)

    val_loader, val_loader_len = get_val_loader(args.data, args.batch_size, 1000, False, workers=args.workers, fp16=args.fp16)
    debug_loader, debug_loader_len = get_debug_loader(args.data, args.batch_size, 1000, False, workers=args.workers, fp16=args.fp16)

    if not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0:
        logger = log.Logger(
                args.print_freq,
                [
                    log.JsonBackend(os.path.join(args.workspace, args.raport_file), log_level=1),
                    log.StdOut1LBackend(train_loader_len, val_loader_len, args.epochs, log_level=0),
                ])

        for k, v in args.__dict__.items():
            logger.log_run_tag(k, v)
    else:
        logger = None

    optimizer = get_optimizer(list(model_and_loss.model.named_parameters()),
            args.fp16,
            args.lr, args.momentum, args.weight_decay,
            nesterov = args.nesterov,
            bn_weight_decay = args.bn_weight_decay,
            # state=optimizer_state,
            static_loss_scale = args.static_loss_scale,
            dynamic_loss_scale = args.dynamic_loss_scale)

    if args.lr_schedule == 'step':
        lr_policy = lr_step_policy(args.lr, [30,60,80], 0.1, args.warmup, logger=logger)
    elif args.lr_schedule == 'cosine':
        lr_policy = lr_cosine_policy(args.lr, args.warmup, args.epochs, logger=logger)
    elif args.lr_schedule == 'linear':
        lr_policy = lr_linear_policy(args.lr, args.warmup, args.epochs, logger=logger)

    if args.amp:
        model_and_loss, optimizer = amp.initialize(
                model_and_loss, optimizer, 
                opt_level="O2", 
                loss_scale="dynamic" if args.dynamic_loss_scale else args.static_loss_scale)

    if args.distributed:
        model_and_loss.distributed()

    model_and_loss.load_model_state(model_state)

    print('Start epoch {}'.format(args.start_epoch))
    train_loop(
        model_and_loss, optimizer,
        lr_policy,
        train_loader, val_loader, debug_loader, args.epochs,
        args.fp16, logger, should_backup_checkpoint(args), use_amp=args.amp,
        batch_size_multiplier = batch_size_multiplier,
        start_epoch = args.start_epoch, best_prec1 = best_prec1, prof=args.prof,
        skip_training = args.evaluate, skip_validation = args.training_only,
        save_checkpoints=args.save_checkpoints and not args.evaluate, checkpoint_dir=args.workspace)
    exp_duration = time.time() - exp_start_time
    if not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0:
        logger.end()
    print("Experiment ended")


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')

    add_parser_arguments(parser)
    args = parser.parse_args()
    cudnn.benchmark = True

    main(args)