Function main

examples/tutorial/large_batch_optimizer/train.py:38–100

()

def main():
    # initialize distributed setting
    parser = colossalai.legacy.get_default_parser()
    parser.add_argument(
        "--optimizer", choices=["lars", "lamb"], help="Choose your large-batch optimizer", required=True
    )
    args = parser.parse_args()

    # launch from torch
    colossalai.legacy.launch_from_torch(config=args.config)

    # get logger
    logger = get_dist_logger()
    logger.info("initialized distributed environment", ranks=[0])

    # create synthetic dataloaders
    train_dataloader = DummyDataloader(length=10, batch_size=gpc.config.BATCH_SIZE)
    test_dataloader = DummyDataloader(length=5, batch_size=gpc.config.BATCH_SIZE)

    # build model
    model = resnet18(num_classes=gpc.config.NUM_CLASSES)

    # create loss function
    criterion = nn.CrossEntropyLoss()

    # create optimizer
    if args.optimizer == "lars":
        optim_cls = Lars
    elif args.optimizer == "lamb":
        optim_cls = Lamb
    optimizer = optim_cls(model.parameters(), lr=gpc.config.LEARNING_RATE, weight_decay=gpc.config.WEIGHT_DECAY)

    # create lr scheduler
    lr_scheduler = CosineAnnealingWarmupLR(
        optimizer=optimizer, total_steps=gpc.config.NUM_EPOCHS, warmup_steps=gpc.config.WARMUP_EPOCHS
    )

    # initialize
    engine, train_dataloader, test_dataloader, _ = colossalai.legacy.initialize(
        model=model,
        optimizer=optimizer,
        criterion=criterion,
        train_dataloader=train_dataloader,
        test_dataloader=test_dataloader,
    )

    logger.info("Engine is built", ranks=[0])

    for epoch in range(gpc.config.NUM_EPOCHS):
        # training
        engine.train()
        data_iter = iter(train_dataloader)

        if gpc.get_global_rank() == 0:
            description = "Epoch {} / {}".format(epoch, gpc.config.NUM_EPOCHS)
            progress = tqdm(range(len(train_dataloader)), desc=description)
        else:
            progress = range(len(train_dataloader))