Open-Sora/scripts/train.py

from copy import deepcopy

import colossalai
import torch
import torch.distributed as dist
import wandb
from colossalai.booster import Booster
from colossalai.booster.plugin import LowLevelZeroPlugin
from colossalai.cluster import DistCoordinator
from colossalai.nn.optimizer import HybridAdam
from colossalai.utils import get_current_device
from tqdm import tqdm

from opensora.acceleration.checkpoint import set_grad_checkpoint
from opensora.acceleration.parallel_states import (
    get_data_parallel_group,
    set_data_parallel_group,
    set_sequence_parallel_group,
)
from opensora.acceleration.plugin import ZeroSeqParallelPlugin
from opensora.datasets import prepare_dataloader
from opensora.datasets.datasets_variable import prepare_dataloader_with_batchsampler
from opensora.registry import DATASETS, MODELS, SCHEDULERS, build_module
from opensora.utils.ckpt_utils import create_logger, load, model_sharding, record_model_param_shape, save
from opensora.utils.config_utils import (
    create_experiment_workspace,
    create_tensorboard_writer,
    parse_configs,
    save_training_config,
)
from opensora.utils.misc import all_reduce_mean, format_numel_str, get_model_numel, requires_grad, to_torch_dtype
from opensora.utils.train_utils import MaskGenerator, update_ema


def main():
    # ======================================================
    # 1. args & cfg
    # ======================================================
    cfg = parse_configs(training=True)
    print(cfg)
    exp_name, exp_dir = create_experiment_workspace(cfg)
    save_training_config(cfg._cfg_dict, exp_dir)

    # ======================================================
    # 2. runtime variables & colossalai launch
    # ======================================================
    assert torch.cuda.is_available(), "Training currently requires at least one GPU."
    assert cfg.dtype in ["fp16", "bf16"], f"Unknown mixed precision {cfg.dtype}"

    # 2.1. colossalai init distributed training
    colossalai.launch_from_torch({})
    coordinator = DistCoordinator()
    device = get_current_device()
    dtype = to_torch_dtype(cfg.dtype)

    # 2.2. init logger, tensorboard & wandb
    if not coordinator.is_master():
        logger = create_logger(None)
    else:
        logger = create_logger(exp_dir)
        logger.info(f"Experiment directory created at {exp_dir}")

        writer = create_tensorboard_writer(exp_dir)
        if cfg.wandb:
            wandb.init(project="minisora", name=exp_name, config=cfg._cfg_dict)

    # 2.3. initialize ColossalAI booster
    if cfg.plugin == "zero2":
        plugin = LowLevelZeroPlugin(
            stage=2,
            precision=cfg.dtype,
            initial_scale=2**16,
            max_norm=cfg.grad_clip,
        )
        set_data_parallel_group(dist.group.WORLD)
    elif cfg.plugin == "zero2-seq":
        plugin = ZeroSeqParallelPlugin(
            sp_size=cfg.sp_size,
            stage=2,
            precision=cfg.dtype,
            initial_scale=2**16,
            max_norm=cfg.grad_clip,
        )
        set_sequence_parallel_group(plugin.sp_group)
        set_data_parallel_group(plugin.dp_group)
    else:
        raise ValueError(f"Unknown plugin {cfg.plugin}")
    booster = Booster(plugin=plugin)

    # ======================================================
    # 3. build dataset and dataloader
    # ======================================================
    dataset = build_module(cfg.dataset, DATASETS)

    # TODO: use plugin's prepare dataloader
    # a batch contains:
    # {
    #      "video": torch.Tensor,  # [B, C, T, H, W],
    #      "text": List[str],
    # }
    if cfg.dataset.type == "VideoTextDataset":
        dataloader = prepare_dataloader(
            dataset,
            batch_size=cfg.batch_size,
            num_workers=cfg.num_workers,
            shuffle=True,
            drop_last=True,
            pin_memory=True,
            process_group=get_data_parallel_group(),
        )
    else:
        dataloader = prepare_dataloader_with_batchsampler(
            dataset,
            batch_size=cfg.batch_size,
            batch_size_bucket=dataset.batch_size_bucket,
            bucket=dataset.bucket,
            num_workers=cfg.num_workers,
            shuffle=True,
            drop_last=True,
            pin_memory=True,
            process_group=get_data_parallel_group(),
        )
        print(dataset.batch_size_bucket)
    logger.info(f"Dataset contains {len(dataset):,} videos ({dataset.data_path})")

    total_batch_size = cfg.batch_size * dist.get_world_size() // cfg.sp_size
    logger.info(f"Total batch size: {total_batch_size}")

    # ======================================================
    # 4. build model
    # ======================================================
    # 4.1. build model
    text_encoder = build_module(cfg.text_encoder, MODELS, device=device)
    vae = build_module(cfg.vae, MODELS)
    input_size = (dataset.num_frames, *dataset.image_size)
    latent_size = vae.get_latent_size(input_size)
    model = build_module(
        cfg.model,
        MODELS,
        input_size=latent_size,
        in_channels=vae.out_channels,
        caption_channels=text_encoder.output_dim,
        model_max_length=text_encoder.model_max_length,
        dtype=dtype,
    )
    model_numel, model_numel_trainable = get_model_numel(model)
    logger.info(
        f"Trainable model params: {format_numel_str(model_numel_trainable)}, Total model params: {format_numel_str(model_numel)}"
    )

    # 4.2. create ema
    ema = deepcopy(model).to(torch.float32).to(device)
    requires_grad(ema, False)
    ema_shape_dict = record_model_param_shape(ema)

    # 4.3. move to device
    vae = vae.to(device, dtype)
    model = model.to(device, dtype)

    # 4.4. build scheduler
    scheduler = build_module(cfg.scheduler, SCHEDULERS)

    # 4.5. setup optimizer
    optimizer = HybridAdam(
        filter(lambda p: p.requires_grad, model.parameters()), lr=cfg.lr, weight_decay=0, adamw_mode=True
    )
    lr_scheduler = None

    # 4.6. prepare for training
    if cfg.grad_checkpoint:
        set_grad_checkpoint(model)
    model.train()
    update_ema(ema, model, decay=0, sharded=False)
    ema.eval()
    if cfg.mask_ratios is not None:
        mask_generator = MaskGenerator(cfg.mask_ratios)

    # =======================================================
    # 5. boost model for distributed training with colossalai
    # =======================================================
    torch.set_default_dtype(dtype)
    model, optimizer, _, dataloader, lr_scheduler = booster.boost(
        model=model, optimizer=optimizer, lr_scheduler=lr_scheduler, dataloader=dataloader
    )
    torch.set_default_dtype(torch.float)
    num_steps_per_epoch = len(dataloader)
    logger.info("Boost model for distributed training")

    # =======================================================
    # 6. training loop
    # =======================================================
    start_epoch = start_step = log_step = sampler_start_idx = 0
    running_loss = 0.0

    # 6.1. resume training
    if cfg.load is not None:
        logger.info("Loading checkpoint")
        start_epoch, start_step, sampler_start_idx = load(booster, model, ema, optimizer, lr_scheduler, cfg.load)
        logger.info(f"Loaded checkpoint {cfg.load} at epoch {start_epoch} step {start_step}")
    logger.info(f"Training for {cfg.epochs} epochs with {num_steps_per_epoch} steps per epoch")

    if cfg.dataset.type == "VideoTextDataset":
        dataloader.sampler.set_start_index(sampler_start_idx)
    model_sharding(ema)

    # 6.2. training loop
    for epoch in range(start_epoch, cfg.epochs):
        if cfg.dataset.type == "VideoTextDataset":
            dataloader.sampler.set_epoch(epoch)
        dataloader_iter = iter(dataloader)
        logger.info(f"Beginning epoch {epoch}...")

        with tqdm(
            range(start_step, num_steps_per_epoch),
            desc=f"Epoch {epoch}",
            disable=not coordinator.is_master(),
            total=num_steps_per_epoch,
            initial=start_step,
        ) as pbar:
            for step in pbar:
                batch = next(dataloader_iter)
                x = batch["video"].to(device, dtype)  # [B, C, T, H, W]
                y = batch["text"]

                # Visual and text encoding
                with torch.no_grad():
                    # Prepare visual inputs
                    x = vae.encode(x)  # [B, C, T, H/P, W/P]
                    # Prepare text inputs
                    model_args = text_encoder.encode(y)

                # Mask
                if cfg.mask_ratios is not None:
                    mask = mask_generator.get_masks(x)
                    model_args["x_mask"] = mask
                else:
                    mask = None

                # Video info
                if "num_frames" in batch:
                    model_args["num_frames"] = batch["num_frames"].to(device, dtype)

                # Diffusion
                t = torch.randint(0, scheduler.num_timesteps, (x.shape[0],), device=device)
                loss_dict = scheduler.training_losses(model, x, t, model_args, mask=mask)

                # Backward & update
                loss = loss_dict["loss"].mean()
                booster.backward(loss=loss, optimizer=optimizer)
                optimizer.step()
                optimizer.zero_grad()

                # Update EMA
                update_ema(ema, model.module, optimizer=optimizer)

                # Log loss values:
                all_reduce_mean(loss)
                running_loss += loss.item()
                global_step = epoch * num_steps_per_epoch + step
                log_step += 1

                # Log to tensorboard
                if coordinator.is_master() and (global_step + 1) % cfg.log_every == 0:
                    avg_loss = running_loss / log_step
                    pbar.set_postfix({"loss": avg_loss, "step": step, "global_step": global_step})
                    running_loss = 0
                    log_step = 0
                    writer.add_scalar("loss", loss.item(), global_step)
                    if cfg.wandb:
                        wandb.log(
                            {
                                "iter": global_step,
                                "num_samples": global_step * total_batch_size,
                                "epoch": epoch,
                                "loss": loss.item(),
                                "avg_loss": avg_loss,
                            },
                            step=global_step,
                        )

                # Save checkpoint
                if cfg.ckpt_every > 0 and (global_step + 1) % cfg.ckpt_every == 0:
                    save(
                        booster,
                        model,
                        ema,
                        optimizer,
                        lr_scheduler,
                        epoch,
                        step + 1,
                        global_step + 1,
                        cfg.batch_size,
                        coordinator,
                        exp_dir,
                        ema_shape_dict,
                    )
                    logger.info(
                        f"Saved checkpoint at epoch {epoch} step {step + 1} global_step {global_step + 1} to {exp_dir}"
                    )

        # the continue epochs are not resumed, so we need to reset the sampler start index and start step
        dataloader.sampler.set_start_index(0)
        start_step = 0


if __name__ == "__main__":
    main()