fed_hfl.py

"""(S)HeteroFL"""
import os, argparse, time
import numpy as np
import wandb
from tqdm import tqdm
import torch
from torch import nn, optim
from torch.nn.modules.batchnorm import _NormBase
# federated
from federated.learning import train_slimmable, test, refresh_bn
# utils
from utils.utils import set_seed, AverageMeter, CosineAnnealingLR, \
    MultiStepLR, LocalMaskCrossEntropyLoss, str2bool
from utils.config import CHECKPOINT_ROOT

# NOTE import desired federation
from federated.core import SHeteFederation as Federation


def render_run_name(args, exp_folder):
    """Return a unique run_name from given args."""
    if args.model == 'default':
        args.model = {'Digits': 'digit', 'Cifar10': 'preresnet18', 'DomainNet': 'alex'}[args.data]
    run_name = f'{args.model}'
    run_name += Federation.render_run_name(args)
    # log non-default args
    if args.seed != 1: run_name += f'__seed_{args.seed}'
    # opt
    if args.lr_sch != 'none': run_name += f'__lrs_{args.lr_sch}'
    if args.opt != 'sgd': run_name += f'__opt_{args.opt}'
    if args.batch != 32: run_name += f'__batch_{args.batch}'
    if args.wk_iters != 1: run_name += f'__wk_iters_{args.wk_iters}'
    # slimmable
    if args.no_track_stat: run_name += f"__nts"
    # split-mix
    if not args.rescale_init: run_name += '__nri'
    if not args.rescale_layer: run_name += '__nrl'
    if args.loss_temp != 'none': run_name += f'__lt{args.loss_temp}'

    args.save_path = os.path.join(CHECKPOINT_ROOT, exp_folder)
    if not os.path.exists(args.save_path):
        os.makedirs(args.save_path)
    SAVE_FILE = os.path.join(args.save_path, run_name)
    return run_name, SAVE_FILE


def get_model_fh(data, model):
    if data == 'Digits':
        if model in ['digit']:
            from nets.slimmable_models import SlimmableDigitModel
            # TODO remove. Function the same as ens_digit
            ModelClass = SlimmableDigitModel
        else:
            raise ValueError(f"Invalid model: {model}")
    elif data in ['DomainNet']:
        if model in ['alex']:
            from nets.slimmable_models import SlimmableAlexNet
            ModelClass = SlimmableAlexNet
        else:
            raise ValueError(f"Invalid model: {model}")
    elif data == 'Cifar10':
        if model in ['preresnet18']:  # From heteroFL
            from nets.HeteFL.slimmable_preresne import resnet18
            ModelClass = resnet18
        else:
            raise ValueError(f"Invalid model: {model}")
    else:
        raise ValueError(f"Unknown dataset: {data}")
    return ModelClass


if __name__ == '__main__':
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

    parser = argparse.ArgumentParser()
    # basic problem setting
    parser.add_argument('--seed', type=int, default=1, help='random seed')
    parser.add_argument('--data', type=str, default='Digits', help='data name')
    parser.add_argument('--model', type=str.lower, default='default', help='model name')
    parser.add_argument('--no_track_stat', action='store_true', help='disable BN tracking')
    parser.add_argument('--test_refresh_bn', action='store_true', help='refresh BN before test')
    # control
    parser.add_argument('--no_log', action='store_true', help='disable wandb log')
    parser.add_argument('--test', action='store_true', help='test the pretrained model')
    parser.add_argument('--resume', action='store_true', help='resume training from checkpoint')
    parser.add_argument('--verbose', type=int, default=0, help='verbose level: 0 or 1')
    # federated
    Federation.add_argument(parser)
    # optimization
    parser.add_argument('--lr', type=float, default=1e-2, help='learning rate')
    parser.add_argument('--lr_sch', type=str, default='none', help='learning rate schedule')
    parser.add_argument('--opt', type=str.lower, default='sgd', help='optimizer')
    parser.add_argument('--iters', type=int, default=300, help='#iterations for communication')
    parser.add_argument('--wk_iters', type=int, default=1, help='#epochs in local train')
    # slimmable test
    parser.add_argument('--test_slim_ratio', type=float, default=1.,
                        help='slim_ratio of model at testing.')
    # split-mix
    parser.add_argument('--rescale_init', type=str2bool, default=True, help='rescale init after'
                                                                            ' slim')
    parser.add_argument('--rescale_layer', type=str2bool, default=True, help='rescale layer outputs'
                                                                             ' after slim')
    parser.add_argument('--loss_temp', type=str, default='none', choices=['none', 'auto'],
                        help='temper cross-entropy loss. auto: set temp as the width scale.')
    args = parser.parse_args()

    set_seed(args.seed)

    # set experiment files, wandb
    exp_folder = f'HFL_{args.data}'
    run_name, SAVE_FILE = render_run_name(args, exp_folder)
    wandb.init(group=run_name[:120], project=exp_folder, mode='offline' if args.no_log else 'online',
               config={**vars(args), 'save_file': SAVE_FILE})


    # /////////////////////////////////
    # ///// Fed Dataset and Model /////
    # /////////////////////////////////
    fed = Federation(args.data, args)
    # Data
    train_loaders, val_loaders, test_loaders = fed.get_data()
    mean_batch_iters = int(np.mean([len(tl) for tl in train_loaders]))
    print(f"  mean_batch_iters: {mean_batch_iters}")

    # Model
    ModelClass = get_model_fh(args.data, args.model)
    running_model = ModelClass(
        track_running_stats=not args.no_track_stat or (args.test and args.test_refresh_bn),
        num_classes=fed.num_classes, slimmabe_ratios=fed.train_slim_ratios,
    ).to(device)

    # Loss
    if args.pu_nclass > 0:  # niid
        loss_fun = LocalMaskCrossEntropyLoss(fed.num_classes)
    else:
        loss_fun = nn.CrossEntropyLoss()

    # Use running model to init a fed aggregator
    fed.make_aggregator(running_model)


    # /////////////////
    # //// Resume /////
    # /////////////////
    # log the best for each model on all datasets
    best_epoch = 0
    best_acc = [0. for j in range(fed.client_num)]
    train_elapsed = [[] for _ in range(fed.client_num)]
    start_epoch = 0
    if args.resume or args.test:
        if os.path.exists(SAVE_FILE):
            print(f'Loading chkpt from {SAVE_FILE}')
            checkpoint = torch.load(SAVE_FILE)
            best_epoch, best_acc = checkpoint['best_epoch'], checkpoint['best_acc']
            train_elapsed = checkpoint['train_elapsed']
            start_epoch = int(checkpoint['a_iter']) + 1
            fed.model_accum.load_state_dict(checkpoint['server_model'])

            print('Resume training from epoch {} with best acc:'.format(start_epoch))
            for client_idx, acc in enumerate(best_acc):
                print(' Best user-{:<10s}| Epoch:{} | Val Acc: {:.4f}'.format(
                    fed.clients[client_idx], best_epoch, acc))
        else:
            if args.test:
                raise FileNotFoundError(f"Not found checkpoint at {SAVE_FILE}")
            else:
                print(f"Not found checkpoint at {SAVE_FILE}\n **Continue without resume.**")


    # ///////////////
    # //// Test /////
    # ///////////////
    if args.test:
        wandb.summary[f'best_epoch'] = best_epoch

        # Set up model with specified width
        print(f"  Test with a single slim_ratio {args.test_slim_ratio}")
        running_model.switch_slim_mode(args.test_slim_ratio)
        test_model = running_model

        # Test on clients
        test_acc_mt = AverageMeter()
        for test_idx, test_loader in enumerate(test_loaders):
            if args.test_refresh_bn:
                fed.download(running_model, test_idx, strict=False)
                def set_rescale_layer_and_bn(m):
                    # if isinstance(m, ScalableModule):
                    #     m.rescale_layer = False
                    if isinstance(m, _NormBase):
                        m.reset_running_stats()
                        m.momentum = None
                running_model.apply(set_rescale_layer_and_bn)
                for ep in tqdm(range(20), desc='refresh bn', leave=False):
                    refresh_bn(running_model, train_loaders[test_idx], device)
            else:
                fed.download(running_model, test_idx)

            _, test_acc = test(test_model, test_loader, loss_fun, device)
            print(' {:<11s}| Test  Acc: {:.4f}'.format(fed.clients[test_idx], test_acc))
            
            wandb.summary[f'{fed.clients[test_idx]} test acc'] = test_acc
            test_acc_mt.append(test_acc)

        # Profile model FLOPs, sizes (#param)
        from nets.profile_func import profile_model
        flops, params = profile_model(test_model, device=device)
        wandb.summary['GFLOPs'] = flops / 1e9
        wandb.summary['model size (MB)'] = params / 1e6
        print('GFLOPS: %.4f, model size: %.4fMB' % (flops / 1e9, params / 1e6))

        print(f"\n Average Test Acc: {test_acc_mt.avg}")
        wandb.summary[f'avg test acc'] = test_acc_mt.avg
        wandb.finish()

        exit(0)


    # ////////////////
    # //// Train /////
    # ////////////////
    # LR scheduler
    if args.lr_sch == 'cos':
        lr_sch = CosineAnnealingLR(args.iters, eta_max=args.lr, last_epoch=start_epoch)
    elif args.lr_sch == 'multi_step':
        lr_sch = MultiStepLR(args.lr, milestones=[150, 250], gamma=0.1, last_epoch=start_epoch)
    else:
        assert args.lr_sch == 'none', f'Invalid lr_sch: {args.lr_sch}'
        lr_sch = None
    for a_iter in range(start_epoch, args.iters):
        # set global lr
        global_lr = args.lr if lr_sch is None else lr_sch.step()
        wandb.log({'global lr': global_lr}, commit=False)

        # ----------- Train Client ---------------
        train_loss_mt, train_acc_mt = AverageMeter(), AverageMeter()
        print("============ Train epoch {} ============".format(a_iter))
        for client_idx in fed.client_sampler.iter():
            # (Alg 2) Sample base models defined by shift index.
            slim_ratios, slim_shifts = fed.sample_bases(client_idx)

            start_time = time.process_time()

            # Download global model to local
            fed.download(running_model, client_idx)

            # (Alg 3) Local Train
            if args.opt == 'sgd':
                optimizer = optim.SGD(params=running_model.parameters(), lr=global_lr,
                                      momentum=0.9, weight_decay=5e-4)
            elif args.opt == 'adam':
                optimizer = optim.Adam(params=running_model.parameters(), lr=global_lr)
            else:
                raise ValueError(f"Invalid optimizer: {args.opt}")
            train_loss, train_acc = train_slimmable(
                running_model, train_loaders[client_idx], optimizer, loss_fun, device,
                max_iter=mean_batch_iters * args.wk_iters if args.partition_mode != 'uni'
                            else len(train_loaders[client_idx]) * args.wk_iters,
                slim_ratios=slim_ratios, slim_shifts=slim_shifts, progress=args.verbose > 0,
                loss_temp=args.loss_temp
            )

            # Upload
            fed.upload(running_model, client_idx,
                       max_slim_ratio=max(slim_ratios), slim_bias_idx=slim_shifts)

            # Log
            client_name = fed.clients[client_idx]
            elapsed = time.process_time() - start_time
            wandb.log({f'{client_name}_train_elapsed': elapsed}, commit=False)
            train_elapsed[client_idx].append(elapsed)

            train_loss_mt.append(train_loss), train_acc_mt.append(train_acc)
            print(f' User-{client_name:<10s} Train | Loss: {train_loss:.4f} |'
                  f' Acc: {train_acc:.4f} | Elapsed: {elapsed:.2f} s')
            wandb.log({
                f"{client_name} train_loss": train_loss,
                f"{client_name} train_acc": train_acc,
            }, commit=False)

        # Use accumulated model to update server model
        fed.aggregate()


        # ----------- Validation ---------------
        val_acc_list = [None for j in range(fed.client_num)]
        val_loss_mt = AverageMeter()
        slim_val_sacc_mt = {slim_ratio: AverageMeter() for slim_ratio in fed.val_slim_ratios}
        for client_idx in range(fed.client_num):
            fed.download(running_model, client_idx)

            for i_slim_ratio, slim_ratio in enumerate(fed.val_slim_ratios):
                # Load and set slim ratio
                running_model.switch_slim_mode(slim_ratio)  # full net should load the full net

                # Test
                val_loss, val_acc = test(running_model, val_loaders[client_idx], loss_fun, device)

                # Log
                val_loss_mt.append(val_loss)
                val_acc_list[client_idx] = val_acc
                if args.verbose > 0:
                    print(' {:<19s} slim {:.2f}| Val  Loss: {:.4f} | Val  Acc: {:.4f}'.format(
                        'User-'+fed.clients[client_idx] if i_slim_ratio == 0 else ' ', slim_ratio,
                        val_loss, val_acc))
                wandb.log({
                    f"{fed.clients[client_idx]} sm{slim_ratio:.2f} val_s-acc": val_acc,
                }, commit=False)
                if slim_ratio == fed.user_max_slim_ratios[client_idx]:
                    wandb.log({
                        f"{fed.clients[client_idx]} val_s-acc": val_acc,
                    }, commit=False)
                slim_val_sacc_mt[slim_ratio].append(val_acc)
        # Log averaged
        print(f' [Overall] Train Loss {train_loss_mt.avg:.4f} '
              f'| Val Acc {np.mean(val_acc_list)*100:.2f}%')
        wandb.log({
            f"train_loss": train_loss_mt.avg,
            f"train_acc": train_acc_mt.avg,
            f"val_loss": val_loss_mt.avg,
            f"val_acc": np.mean(val_acc_list),
        }, commit=False)
        wandb.log({
            f"slim{k:.2f} val_sacc": mt.avg if len(mt) > 0 else None
            for k, mt in slim_val_sacc_mt.items()
        }, commit=False)


        # ----------- Save checkpoint -----------
        if np.mean(val_acc_list) > np.mean(best_acc):
            best_epoch = a_iter
            for client_idx in range(fed.client_num):
                best_acc[client_idx] = val_acc_list[client_idx]
                if args.verbose > 0:
                    print(' Best site-{:<10s}| Epoch:{} | Val Acc: {:.4f}'.format(
                          fed.clients[client_idx], best_epoch, best_acc[client_idx]))
            print(' [Best Val] Acc {:.4f}'.format(np.mean(val_acc_list)))

            # Save
            print(f' Saving the local and server checkpoint to {SAVE_FILE}')
            save_dict = {
                'server_model': fed.model_accum.state_dict(),
                'best_epoch': best_epoch,
                'best_acc': best_acc,
                'a_iter': a_iter,
                'all_domains': fed.all_domains,
                'train_elapsed': train_elapsed,
            }
            torch.save(save_dict, SAVE_FILE)
        wandb.log({
            f"best_val_acc": np.mean(best_acc),
        }, commit=True)