memorizing_associations_task_relu.py

import argparse
import json
import os
import random
import socket
import sys
import time
import warnings
from datetime import datetime

import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.multiprocessing as mp
import torch.nn.functional
import torch.nn.parallel
import torch.utils.data
import torch.utils.data.distributed
from torch.utils.tensorboard import SummaryWriter

import utils.checkpoint
import utils.meters
import utils.metrics
from data.memorizing_associations_dataset import MemorizingAssociationsDataset
from functions.plasticity_functions import InvertedOjaWithSoftUpperBound
from models.network_models import MemorizingAssociations, MemorizingAssociationsReLU

parser = argparse.ArgumentParser(description='Memorizing associations task training with ReLU activations')
parser.add_argument('--sequence_length', default=10, type=int, metavar='SEQUENCE_LENGTH',
                    help='The number of vector-label pairs (default: 10)')
parser.add_argument('--num_classes', default=10, type=int, metavar='NUM_CLASSES',
                    help='The number of classes (default: 10)')
parser.add_argument('--feature_size', default=10, type=int, metavar='FEATURE_SIZE',
                    help='Size of the input features (default: 10)')
parser.add_argument('--inf_data', default=1, type=int, metavar='INF_DATA',
                    help='If 1, then generate a new dataset for every iteration of BPTT (default: 1)')

parser.add_argument('--workers', default=4, type=int, metavar='N',
                    help='Number of data loading workers (default: 4)')
parser.add_argument('--prefetch_factor', default=2, type=int, metavar='N',
                    help='Prefetch prefetch_factor * workers examples (default: 2)')

parser.add_argument('--embedding_size', default=80, type=int, metavar='N',
                    help='Embedding size (default: 80)')
parser.add_argument('--memory_size', default=100, type=int, metavar='N',
                    help='Size of the memory matrix (default: 100)')
parser.add_argument('--w_max', default=1.0, type=float, metavar='N',
                    help='Soft maximum of Hebbian weights (default: 1.0)')
parser.add_argument('--gamma_pos', default=0.1, type=float, metavar='N',
                    help='Write factor of Hebbian rule (default: 0.1)')
parser.add_argument('--gamma_neg', default=0.1, type=float, metavar='N',
                    help='Forget factor of Hebbian rule (default: 0.1)')

parser.add_argument('--epochs', default=250, type=int, metavar='N',
                    help='Number of total epochs to run (default: 250)')
parser.add_argument('--batch_size', default=512, type=int, metavar='N',
                    help='Mini-batch size (default: 512), this is the total '
                         'batch size of all GPUs on the current node when '
                         'using Data Parallel or Distributed Data Parallel')
parser.add_argument('--learning_rate', default=0.001, type=float, metavar='N',
                    help='Initial learning rate (default: 0.001)')
parser.add_argument('--learning_rate_decay', default=0.85, type=float, metavar='N',
                    help='Learning rate decay (default: 0.85)')
parser.add_argument('--decay_learning_rate_every', default=20, type=int, metavar='N',
                    help='Decay the learning rate every N epochs (default: 20)')
parser.add_argument('--max_grad_norm', default=40.0, type=float, metavar='N',
                    help='Gradients with an L2 norm larger than max_grad_norm will be clipped '
                         'to have an L2 norm of max_grad_norm. If non-positive, then the gradient will '
                         'not be clipped. (default: 40.0)')

parser.add_argument('--start_epoch', default=0, type=int, metavar='N',
                    help='Manual epoch number (useful on restarts, default: 0)')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
                    help='Path to latest checkpoint (default: none)')
parser.add_argument('--evaluate', action='store_true',
                    help='Evaluate the model on the test set')

parser.add_argument('--logging', action='store_true',
                    help='Write tensorboard logs')
parser.add_argument('--print_freq', default=1, type=int, metavar='N',
                    help='Print frequency (default: 1)')
parser.add_argument('--world_size', default=-1, type=int, metavar='N',
                    help='Number of nodes for distributed training (default: -1)')
parser.add_argument('--rank', default=-1, type=int, metavar='N',
                    help='Node rank for distributed training (default: -1)')
parser.add_argument('--dist_url', default='tcp://127.0.0.1:23456', type=str, metavar='DIST_URL',
                    help='URL used to set up distributed training (default: tcp://127.0.0.1:23456)')
parser.add_argument('--dist_backend', default='nccl', choices=['nccl', 'mpi', 'gloo'], type=str, metavar='DIST_BACKEND',
                    help='Distributed backend to use (default: nccl)')
parser.add_argument('--seed', default=None, type=int, metavar='N',
                    help='Seed for initializing training (default: none)')
parser.add_argument('--dataset_seed', default=42, type=int, metavar='N',
                    help='Seed for creating the dataset (default: 42)')
parser.add_argument('--gpu', default=None, type=int, metavar='N',
                    help='GPU id to use (default: none)')
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')

best_acc = 0
log_dir = ''
writer = None
time_stamp = datetime.now().strftime('%b%d_%H-%M-%S')

with open('version.txt') as f:
    version = f.readline()


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

    args.inf_data = True if args.inf_data else False
    args.max_grad_norm = args.max_grad_norm if args.max_grad_norm > 0 else None

    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

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


def main_worker(gpu, num_gpus_per_node, args):
    global best_acc
    global log_dir
    global writer
    global time_stamp
    global version
    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 * num_gpus_per_node + gpu
        dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
                                world_size=args.world_size, rank=args.rank)

    # Data loading code
    train_set = MemorizingAssociationsDataset(sequence_length=args.sequence_length, num_classes=args.num_classes,
                                              feature_size=args.feature_size, inf_data=args.inf_data, dataset_size=9000,
                                              seed=args.dataset_seed)

    val_set = MemorizingAssociationsDataset(sequence_length=args.sequence_length, num_classes=args.num_classes,
                                            feature_size=args.feature_size, inf_data=False, dataset_size=1000,
                                            seed=args.dataset_seed+args.workers)

    test_set = MemorizingAssociationsDataset(sequence_length=args.sequence_length, num_classes=args.num_classes,
                                             feature_size=args.feature_size, inf_data=False, dataset_size=2000,
                                             seed=args.dataset_seed+args.workers+1)

    # Create model
    print("=> creating model '{model_name}'".format(model_name=MemorizingAssociations.__name__))
    model = MemorizingAssociationsReLU(
        input_size=args.feature_size,
        output_size=args.num_classes,
        num_embeddings=args.num_classes,
        embedding_size=args.embedding_size,
        memory_size=args.memory_size,
        plasticity_rule=InvertedOjaWithSoftUpperBound(w_max=args.w_max,
                                                      gamma_pos=args.gamma_pos,
                                                      gamma_neg=args.gamma_neg))

    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 / num_gpus_per_node)
            args.workers = int((args.workers + num_gpus_per_node - 1) / num_gpus_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
        model = torch.nn.DataParallel(model).cuda()

    # Define loss function (criterion) and optimizer
    criterion = torch.nn.CrossEntropyLoss().cuda(args.gpu)
    optimizer = torch.optim.Adam(model.parameters(), args.learning_rate)

    # 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 = utils.checkpoint.load_checkpoint(args.resume, 'cpu')
            else:
                # Map model to be loaded to specified single gpu.
                checkpoint = utils.checkpoint.load_checkpoint(args.resume, 'cuda:{}'.format(args.gpu))
            args.start_epoch = checkpoint['epoch']
            best_acc = checkpoint['best_acc']
            log_dir = checkpoint['log_dir']
            time_stamp = checkpoint['time_stamp']

            # Checkpoint parameters have to match current parameters. If not, abort.
            ignore_keys = ['workers', 'prefetch_factor', 'epochs', 'start_epoch', 'resume',
                           'evaluate', 'logging', 'print_freq', 'world_size', 'rank', 'dist_url', 'dist_backend',
                           'seed', 'gpu', 'multiprocessing_distributed', 'distributed']
            if args.evaluate:
                ignore_keys.append('batch_size')
                ignore_keys.append('sequence_length')

            for key, val in vars(checkpoint['params']).items():
                if key not in ignore_keys:
                    if vars(args)[key] != val:
                        print("=> You tried to restart training of a model that was trained with different parameters "
                              "as you requested now. Aborting...")
                        sys.exit()

            if args.gpu is not None:
                # best_acc may be from a checkpoint from a different GPU
                best_acc = best_acc.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

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

    train_loader = torch.utils.data.DataLoader(
        train_set, batch_size=args.batch_size, shuffle=(train_sampler is None),
        num_workers=args.workers, persistent_workers=True, prefetch_factor=args.prefetch_factor,
        sampler=train_sampler, worker_init_fn=train_set.set_worker_seed)

    val_loader = torch.utils.data.DataLoader(
        val_set, batch_size=args.batch_size, shuffle=False, num_workers=args.workers, persistent_workers=True,
        prefetch_factor=args.prefetch_factor)

    test_loader = torch.utils.data.DataLoader(
        test_set, batch_size=args.batch_size, shuffle=False, num_workers=args.workers, persistent_workers=True,
        prefetch_factor=args.prefetch_factor)

    if args.evaluate:
        validate(test_loader, model, criterion, args, prefix='Test: ')
        return

    if not args.multiprocessing_distributed or (args.multiprocessing_distributed and
                                                args.rank % num_gpus_per_node == 0):
        if log_dir and args.logging:
            # Use the directory that is stored in checkpoint if we resume training
            writer = SummaryWriter(log_dir=log_dir)
        elif args.logging:
            log_dir = os.path.join(
                'results', 'runs', time_stamp +
                '_' + socket.gethostname() +
                f'_version-{version}-memorizing_associations_task_relu-{args.sequence_length}-{args.num_classes}')
            writer = SummaryWriter(log_dir=log_dir)

            def pretty_json(hp):
                json_hp = json.dumps(hp, indent=2, sort_keys=False)
                return "".join('\t' + line for line in json_hp.splitlines(True))

            writer.add_text('Info/params', pretty_json(vars(args)))

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

        # Train for one epoch
        train_loss, train_acc = train(train_loader, model, criterion, optimizer, epoch, args)

        # Evaluate on validation set
        val_loss, val_acc = validate(val_loader, model, criterion, args)

        # Remember best acc@1 and save checkpoint
        is_best = val_acc > best_acc
        best_acc = max(val_acc, best_acc)

        if not args.multiprocessing_distributed or (args.multiprocessing_distributed and
                                                    args.rank % num_gpus_per_node == 0):
            if args.logging:
                writer.add_scalar('Loss/train', train_loss, epoch)
                writer.add_scalar('Acc/train', train_acc, epoch)
                writer.add_scalar('Loss/val', val_loss, epoch)
                writer.add_scalar('Acc/val', val_acc, epoch)
                writer.add_scalar('Misc/lr', current_lr, epoch)
                if epoch + 1 == args.epochs:
                    writer.flush()

            utils.checkpoint.save_checkpoint({
                'epoch': epoch + 1,
                'state_dict': model.state_dict(),
                'best_acc': best_acc,
                'optimizer': optimizer.state_dict(),
                'log_dir': writer.get_logdir() if args.logging else '',
                'time_stamp': time_stamp,
                'params': args
            }, is_best, filename=os.path.join(
                'results', 'checkpoints', time_stamp +
                '_' + socket.gethostname() +
                f'_version-{version}-memorizing_associations_task_relu-{args.sequence_length}-{args.num_classes}'))


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

    # Switch to train mode
    model.train()

    end = time.time()
    for i, (sample, sequence_length) in enumerate(train_loader):
        # Measure data loading time
        data_time.update(time.time() - end)

        features, labels, query, answer = sample['features'], sample['labels'], sample['query'], sample['answer']

        if args.gpu is not None:
            features = features.cuda(args.gpu, non_blocking=True)
            labels = labels.cuda(args.gpu, non_blocking=True)
            query = query.cuda(args.gpu, non_blocking=True)
        if torch.cuda.is_available():
            answer = answer.cuda(args.gpu, non_blocking=True)

        # Compute output
        output, encoding_outputs, writing_outputs, reading_outputs = model(features, labels, query)
        loss = criterion(output, answer)

        # Measure accuracy and record loss
        acc = utils.metrics.accuracy(output, answer, top_k=(1,))

        losses.update(loss.item(), features.size(0))
        top1.update(acc[0], features.size(0))

        # Compute gradient
        optimizer.zero_grad(set_to_none=True)
        loss.backward()

        if args.max_grad_norm is not None:
            # Clip L2 norm of gradient to max_grad_norm
            torch.nn.utils.clip_grad_norm_(parameters=model.parameters(), max_norm=args.max_grad_norm)

        # Do SGD step
        optimizer.step()

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

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

    return losses.avg, top1.avg


def validate(data_loader, model, criterion, args, prefix="Val: "):
    batch_time = utils.meters.AverageMeter('Time', ':6.3f')
    losses = utils.meters.AverageMeter('Loss', ':.4e')
    top1 = utils.meters.AverageMeter('Acc', ':6.2f')
    progress = utils.meters.ProgressMeter(
        len(data_loader),
        [batch_time, losses, top1],
        prefix=prefix)

    # Switch to evaluate mode
    model.eval()

    with torch.no_grad():
        end = time.time()
        for i, (sample, sequence_length) in enumerate(data_loader):
            features, labels, query, answer = sample['features'], sample['labels'], sample['query'], sample['answer']

            if args.gpu is not None:
                features = features.cuda(args.gpu, non_blocking=True)
                labels = labels.cuda(args.gpu, non_blocking=True)
                query = query.cuda(args.gpu, non_blocking=True)
            if torch.cuda.is_available():
                answer = answer.cuda(args.gpu, non_blocking=True)

            # Compute output
            output, *_ = model(features, labels, query)
            loss = criterion(output, answer)

            # Measure accuracy and record loss
            acc = utils.metrics.accuracy(output, answer, top_k=(1,))
            losses.update(loss.item(), features.size(0))
            top1.update(acc[0], features.size(0))

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

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

        print(' * Acc {top1.avg:.3f}'.format(top1=top1))

    return losses.avg, top1.avg


def adjust_learning_rate(optimizer, epoch, args):
    """Sets the learning rate to the initial LR decayed by X% every N epochs"""
    lr = args.learning_rate * (args.learning_rate_decay ** (epoch // args.decay_learning_rate_every))
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr

    return lr


if __name__ == '__main__':
    main()