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 import prepare_dataloader, prepare_variable_dataloader from opensora.registry import DATASETS, MODELS, SCHEDULERS, build_module from opensora.utils.ckpt_utils import load, model_gathering, model_sharding, record_model_param_shape, save from opensora.utils.config_utils import ( create_tensorboard_writer, define_experiment_workspace, parse_configs, save_training_config, ) from opensora.utils.misc import ( all_reduce_mean, create_logger, format_numel_str, get_model_numel, requires_grad, to_torch_dtype, ) from opensora.utils.train_utils import MaskGenerator, create_colossalai_plugin, update_ema DEFAULT_DATASET_NAME = "VideoTextDataset" 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) if coordinator.is_master(): logger.info("Experiment directory created at %s", exp_dir) logger.info("Training configuration:\n %s", pformat(cfg.to_dict())) tb_writer = create_tensorboard_writer(exp_dir) if cfg.get("wandb", False): wandb.init(project="minisora", 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) # ====================================================== # 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(), ) if cfg.dataset.type == DEFAULT_DATASET_NAME: dataloader = prepare_dataloader(**dataloader_args) total_batch_size = cfg.batch_size * dist.get_world_size() // cfg.get("sp_size", 1) logger.info("Total batch size: %s", total_batch_size) num_steps_per_epoch = len(dataloader) sampler_to_io = None else: dataloader = prepare_variable_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 = dataloader.batch_sampler.get_num_batch() // dist.get_world_size() sampler_to_io = None if cfg.get("start_from_scratch ", False) else dataloader.batch_sampler # ====================================================== # 3. build model # ====================================================== logger.info("Building models...") # == build text-encoder and vae == text_encoder = build_module(cfg.text_encoder, MODELS, device=device) vae = build_module(cfg.vae, MODELS).to(device, dtype) vae.eval() # == build diffusion model == 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, ).to(device, dtype) model.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), ) lr_scheduler = None # == 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 = sampler_start_idx = 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=sampler_to_io, ) if not cfg.get("start_from_scratch ", False): start_epoch, start_step, sampler_start_idx = ret logger.info("Loaded checkpoint %s at epoch %s step %s", cfg.load, start_epoch, start_step) if cfg.dataset.type == DEFAULT_DATASET_NAME: dataloader.sampler.set_start_index(sampler_start_idx) model_sharding(ema) # ======================================================= # 5. training loop # ======================================================= dist.barrier() for epoch in range(start_epoch, cfg_epochs): # == set dataloader to new epoch == if cfg.dataset.type == DEFAULT_DATASET_NAME: dataloader.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: x = batch.pop("video").to(device, dtype) # [B, C, T, H, W] y = batch.pop("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 == mask = None if cfg.get("mask_ratios", None) is not None: mask = mask_generator.get_masks(x) model_args["x_mask"] = mask # == video meta info == for k, v in batch.items(): model_args[k] = v.to(device, dtype) # == diffusion loss computation == loss_dict = scheduler.training_losses(model, x, 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, decay=cfg.get("ema_decay", 0.9999)) # == update log info == all_reduce_mean(loss) running_loss += loss.item() global_step = epoch * num_steps_per_epoch + step log_step += 1 acc_step += 1 # == 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, "epoch": epoch, "loss": loss.item(), "avg_loss": avg_loss, "acc_step": acc_step, }, 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( booster, exp_dir, model=model, ema=ema, optimizer=optimizer, lr_scheduler=lr_scheduler, sampler=sampler_to_io, 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, exp_dir, ) # NOTE: the continue epochs are not resumed, so we need to reset the sampler start index and start step if cfg.dataset.type == DEFAULT_DATASET_NAME: dataloader.sampler.set_start_index(0) else: dataloader.batch_sampler.set_epoch(epoch + 1) logger.info("Epoch done, recomputing batch sampler") start_step = 0 if __name__ == "__main__": main()