import os from copy import deepcopy from datetime import timedelta from pprint import pformat import torch import torch.distributed as dist import wandb from colossalai.booster import Booster from colossalai.cluster import DistCoordinator from colossalai.nn.optimizer import HybridAdam from colossalai.utils import get_current_device, set_seed from tqdm import tqdm from opensora.acceleration.checkpoint import set_grad_checkpoint from opensora.acceleration.parallel_states import get_data_parallel_group from opensora.datasets.dataloader import prepare_dataloader from opensora.registry import DATASETS, MODELS, SCHEDULERS, build_module from opensora.utils.lr_scheduler import LinearWarmupLR from opensora.utils.ckpt_utils import load, model_gathering, model_sharding, record_model_param_shape, save from opensora.utils.config_utils import define_experiment_workspace, parse_configs, save_training_config from opensora.utils.misc import ( Timer, all_reduce_mean, create_logger, create_tensorboard_writer, format_numel_str, get_model_numel, requires_grad, to_torch_dtype, ) from opensora.utils.train_utils import MaskGenerator, create_colossalai_plugin, update_ema def main(): # ====================================================== # 1. configs & runtime variables # ====================================================== # == parse configs == cfg = parse_configs(training=True) # == device and dtype == assert torch.cuda.is_available(), "Training currently requires at least one GPU." cfg_dtype = cfg.get("dtype", "bf16") assert cfg_dtype in ["fp16", "bf16"], f"Unknown mixed precision {cfg_dtype}" dtype = to_torch_dtype(cfg.get("dtype", "bf16")) # == colossalai init distributed training == # NOTE: A very large timeout is set to avoid some processes exit early dist.init_process_group(backend="nccl", timeout=timedelta(hours=24)) torch.cuda.set_device(dist.get_rank() % torch.cuda.device_count()) set_seed(cfg.get("seed", 1024)) coordinator = DistCoordinator() device = get_current_device() # == init exp_dir == exp_name, exp_dir = define_experiment_workspace(cfg) coordinator.block_all() if coordinator.is_master(): os.makedirs(exp_dir, exist_ok=True) save_training_config(cfg.to_dict(), exp_dir) coordinator.block_all() # == init logger, tensorboard & wandb == logger = create_logger(exp_dir) logger.info("Experiment directory created at %s", exp_dir) logger.info("Training configuration:\n %s", pformat(cfg.to_dict())) if coordinator.is_master(): tb_writer = create_tensorboard_writer(exp_dir) if cfg.get("wandb", False): wandb.init(project="Open-Sora", name=exp_name, config=cfg.to_dict(), dir="./outputs/wandb") # == init ColossalAI booster == plugin = create_colossalai_plugin( plugin=cfg.get("plugin", "zero2"), dtype=cfg_dtype, grad_clip=cfg.get("grad_clip", 0), sp_size=cfg.get("sp_size", 1), ) booster = Booster(plugin=plugin) torch.set_num_threads(1) # ====================================================== # 2. build dataset and dataloader # ====================================================== logger.info("Building dataset...") # == build dataset == dataset = build_module(cfg.dataset, DATASETS) logger.info("Dataset contains %s samples.", len(dataset)) # == build dataloader == dataloader_args = dict( dataset=dataset, batch_size=cfg.get("batch_size", None), num_workers=cfg.get("num_workers", 4), seed=cfg.get("seed", 1024), shuffle=True, drop_last=True, pin_memory=True, process_group=get_data_parallel_group(), ) dataloader, sampler = prepare_dataloader( bucket_config=cfg.get("bucket_config", None), num_bucket_build_workers=cfg.get("num_bucket_build_workers", 1), **dataloader_args, ) num_steps_per_epoch = len(dataloader) # ====================================================== # 3. build model # ====================================================== logger.info("Building models...") # == build text-encoder and vae == text_encoder = build_module(cfg.get("text_encoder", None), MODELS, device=device, dtype=dtype) if text_encoder is not None: text_encoder_output_dim = text_encoder.output_dim text_encoder_model_max_length = text_encoder.model_max_length else: text_encoder_output_dim = cfg.get("text_encoder_output_dim", 4096) text_encoder_model_max_length = cfg.get("text_encoder_model_max_length", 300) # == build vae == vae = build_module(cfg.get("vae", None), MODELS) if vae is not None: vae = vae.to(device, dtype).eval() if vae is not None: input_size = (dataset.num_frames, *dataset.image_size) latent_size = vae.get_latent_size(input_size) vae_out_channels = vae.out_channels else: latent_size = (None, None, None) vae_out_channels = cfg.get("vae_out_channels", 4) # == build diffusion model == 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, ) .to(device, dtype) .train() ) model_numel, model_numel_trainable = get_model_numel(model) logger.info( "[Diffusion] Trainable model params: %s, Total model params: %s", format_numel_str(model_numel_trainable), format_numel_str(model_numel), ) # == build ema for diffusion model == ema = deepcopy(model).to(torch.float32).to(device) requires_grad(ema, False) ema_shape_dict = record_model_param_shape(ema) ema.eval() update_ema(ema, model, decay=0, sharded=False) # == setup loss function, build scheduler == scheduler = build_module(cfg.scheduler, SCHEDULERS) # == setup optimizer == optimizer = HybridAdam( filter(lambda p: p.requires_grad, model.parameters()), adamw_mode=True, lr=cfg.get("lr", 1e-4), weight_decay=cfg.get("weight_decay", 0), eps=cfg.get("adam_eps", 1e-8), ) warmup_steps = cfg.get("warmup_steps", None) if warmup_steps is None: lr_scheduler = None else: lr_scheduler = LinearWarmupLR(optimizer, warmup_steps=cfg.get("warmup_steps")) # == additional preparation == if cfg.get("grad_checkpoint", False): set_grad_checkpoint(model) if cfg.get("mask_ratios", None) is not None: mask_generator = MaskGenerator(cfg.mask_ratios) # ======================================================= # 4. distributed training preparation with colossalai # ======================================================= logger.info("Preparing for distributed training...") # == boosting == # NOTE: we set dtype first to make initialization of model consistent with the dtype; then reset it to the fp32 as we make diffusion scheduler in fp32 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) logger.info("Boosting model for distributed training") # == global variables == cfg_epochs = cfg.get("epochs", 1000) start_epoch = start_step = log_step = acc_step = 0 running_loss = 0.0 logger.info("Training for %s epochs with %s steps per epoch", cfg_epochs, num_steps_per_epoch) # == resume == if cfg.get("load", None) is not None: logger.info("Loading checkpoint") ret = load( booster, cfg.load, model=model, ema=ema, optimizer=optimizer, lr_scheduler=lr_scheduler, sampler=None if cfg.get("start_from_scratch", False) else sampler, ) if not cfg.get("start_from_scratch", False): start_epoch, start_step = ret logger.info("Loaded checkpoint %s at epoch %s step %s", cfg.load, start_epoch, start_step) model_sharding(ema) # ======================================================= # 5. training loop # ======================================================= dist.barrier() for epoch in range(start_epoch, cfg_epochs): # == set dataloader to new epoch == sampler.set_epoch(epoch) dataloader_iter = iter(dataloader) logger.info("Beginning epoch %s...", epoch) # == training loop in an epoch == with tqdm( enumerate(dataloader_iter, start=start_step), desc=f"Epoch {epoch}", disable=not coordinator.is_master(), initial=start_step, total=num_steps_per_epoch, ) as pbar: for step, batch in pbar: timer_list = [] with Timer("move data") as move_data_t: x = batch.pop("video").to(device, dtype) # [B, C, T, H, W] y = batch.pop("text") timer_list.append(move_data_t) # == visual and text encoding == with Timer("encode") as encode_t: with torch.no_grad(): # Prepare visual inputs if cfg.get("load_video_features", False): x = x.to(device, dtype) else: x = vae.encode(x) # [B, C, T, H/P, W/P] # Prepare text inputs if cfg.get("load_text_features", False): model_args = {"y": y.to(device, dtype)} mask = batch.pop("mask") if isinstance(mask, torch.Tensor): mask = mask.to(device, dtype) model_args["mask"] = mask else: model_args = text_encoder.encode(y) coordinator.block_all() timer_list.append(encode_t) # == mask == with Timer("mask") as mask_t: mask = None if cfg.get("mask_ratios", None) is not None: mask = mask_generator.get_masks(x) model_args["x_mask"] = mask coordinator.block_all() timer_list.append(mask_t) # == video meta info == for k, v in batch.items(): if isinstance(v, torch.Tensor): model_args[k] = v.to(device, dtype) # == diffusion loss computation == with Timer("diffusion") as loss_t: loss_dict = scheduler.training_losses(model, x, model_args, mask=mask) coordinator.block_all() timer_list.append(loss_t) # == backward & update == with Timer("backward") as backward_t: loss = loss_dict["loss"].mean() booster.backward(loss=loss, optimizer=optimizer) optimizer.step() optimizer.zero_grad() # update learning rate if lr_scheduler is not None: lr_scheduler.step() coordinator.block_all() timer_list.append(backward_t) # == update EMA == with Timer("update_ema") as ema_t: update_ema(ema, model.module, optimizer=optimizer, decay=cfg.get("ema_decay", 0.9999)) coordinator.block_all() timer_list.append(ema_t) # == update log info == with Timer("reduce_loss") as reduce_loss_t: all_reduce_mean(loss) running_loss += loss.item() global_step = epoch * num_steps_per_epoch + step log_step += 1 acc_step += 1 coordinator.block_all() timer_list.append(reduce_loss_t) # == logging == if coordinator.is_master() and (global_step + 1) % cfg.get("log_every", 1) == 0: avg_loss = running_loss / log_step # progress bar pbar.set_postfix({"loss": avg_loss, "step": step, "global_step": global_step}) # tensorboard tb_writer.add_scalar("loss", loss.item(), global_step) # wandb if cfg.get("wandb", False): wandb.log( { "iter": global_step, "acc_step": acc_step, "epoch": epoch, "loss": loss.item(), "avg_loss": avg_loss, "lr": optimizer.param_groups[0]["lr"], "debug/move_data_time": move_data_t.elapsed_time, "debug/encode_time": encode_t.elapsed_time, "debug/mask_time": mask_t.elapsed_time, "debug/diffusion_time": loss_t.elapsed_time, "debug/backward_time": backward_t.elapsed_time, "debug/update_ema_time": ema_t.elapsed_time, "debug/reduce_loss_time": reduce_loss_t.elapsed_time, }, step=global_step, ) running_loss = 0.0 log_step = 0 # == checkpoint saving == ckpt_every = cfg.get("ckpt_every", 0) if ckpt_every > 0 and (global_step + 1) % ckpt_every == 0: model_gathering(ema, ema_shape_dict) save_dir = save( booster, exp_dir, model=model, ema=ema, optimizer=optimizer, lr_scheduler=lr_scheduler, sampler=sampler, epoch=epoch, step=step + 1, global_step=global_step + 1, batch_size=cfg.get("batch_size", None), ) if dist.get_rank() == 0: model_sharding(ema) logger.info( "Saved checkpoint at epoch %s, step %s, global_step %s to %s", epoch, step + 1, global_step + 1, save_dir, ) log_str = f"Rank {dist.get_rank()} | Epoch {epoch} | Step {step} | " for timer in timer_list: log_str += f"{timer.name}: {timer.elapsed_time:.3f}s | " print(log_str) coordinator.block_all() sampler.reset() start_step = 0 if __name__ == "__main__": main()