mirror of
https://github.com/hpcaitech/Open-Sora.git
synced 2026-04-10 12:49:38 +02:00
359 lines
13 KiB
Python
359 lines
13 KiB
Python
import os
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from copy import deepcopy
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from datetime import timedelta
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from pprint import pformat
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import torch
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import torch.distributed as dist
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import wandb
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from colossalai.booster import Booster
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from colossalai.cluster import DistCoordinator
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from colossalai.nn.optimizer import HybridAdam
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from colossalai.utils import get_current_device, set_seed
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from tqdm import tqdm
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from opensora.acceleration.checkpoint import set_grad_checkpoint
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from opensora.acceleration.parallel_states import get_data_parallel_group
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from opensora.datasets.dataloader import build_batch_dataloader
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from opensora.registry import DATASETS, MODELS, SCHEDULERS, build_module
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from opensora.utils.ckpt_utils import load, model_gathering, model_sharding, record_model_param_shape, save
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from opensora.utils.config_utils import define_experiment_workspace, parse_configs, save_training_config
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from opensora.utils.misc import (
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all_reduce_mean,
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create_logger,
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create_tensorboard_writer,
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format_numel_str,
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get_model_numel,
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requires_grad,
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to_torch_dtype,
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)
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from opensora.utils.train_utils import MaskGenerator, create_colossalai_plugin, update_ema
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DEFAULT_DATASET_NAME = "VideoTextDataset"
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def main():
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# ======================================================
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# 1. configs & runtime variables
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# ======================================================
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# == parse configs ==
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cfg = parse_configs(training=True)
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# == device and dtype ==
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assert torch.cuda.is_available(), "Training currently requires at least one GPU."
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cfg_dtype = cfg.get("dtype", "bf16")
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assert cfg_dtype in ["fp16", "bf16"], f"Unknown mixed precision {cfg_dtype}"
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dtype = to_torch_dtype(cfg.get("dtype", "bf16"))
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# == colossalai init distributed training ==
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# NOTE: A very large timeout is set to avoid some processes exit early
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dist.init_process_group(backend="nccl", timeout=timedelta(hours=24))
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torch.cuda.set_device(dist.get_rank() % torch.cuda.device_count())
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set_seed(cfg.get("seed", 1024))
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coordinator = DistCoordinator()
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device = get_current_device()
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# == init exp_dir ==
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exp_name, exp_dir = define_experiment_workspace(cfg)
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coordinator.block_all()
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if coordinator.is_master():
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os.makedirs(exp_dir, exist_ok=True)
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save_training_config(cfg.to_dict(), exp_dir)
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coordinator.block_all()
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# == init logger, tensorboard & wandb ==
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logger = create_logger(exp_dir)
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logger.info("Experiment directory created at %s", exp_dir)
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logger.info("Training configuration:\n %s", pformat(cfg.to_dict()))
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if coordinator.is_master():
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tb_writer = create_tensorboard_writer(exp_dir)
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if cfg.get("wandb", False):
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wandb.init(project="minisora", name=exp_name, config=cfg.to_dict(), dir="./outputs/wandb")
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# == init ColossalAI booster ==
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plugin = create_colossalai_plugin(
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plugin=cfg.get("plugin", "zero2"),
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dtype=cfg_dtype,
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grad_clip=cfg.get("grad_clip", 0),
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sp_size=cfg.get("sp_size", 1),
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)
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booster = Booster(plugin=plugin)
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# ======================================================
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# 2. build dataset and dataloader
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# ======================================================
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logger.info("Building dataset...")
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# == build dataset ==
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dataset = build_module(cfg.dataset, DATASETS)
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logger.info("Dataset contains %s samples.", len(dataset))
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# == build dataloader ==
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# modify here
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dataloader_args = dict(
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dataset=dataset,
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# batch_size=cfg.get("batch_size", 1),
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num_workers=cfg.get("num_workers", 4),
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seed=cfg.get("seed", 1024),
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# shuffle=True,
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# drop_last=True,
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pin_memory=True,
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process_group=get_data_parallel_group(),
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)
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dataloader = build_batch_dataloader(**dataloader_args)
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num_steps_per_epoch = len(dataset) // dist.get_world_size()
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sampler_to_io = None
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"""
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TODO:
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- prefetch
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- collate fn
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- resume
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- sampler_to_io ?
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- remove text_encoder & caption_embedder
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- currently only support 1 epoch; every epoch is the same
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"""
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# if cfg.dataset.type == DEFAULT_DATASET_NAME:
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# dataloader = prepare_dataloader(**dataloader_args)
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# total_batch_size = cfg.batch_size * dist.get_world_size() // cfg.get("sp_size", 1)
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# logger.info("Total batch size: %s", total_batch_size)
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# num_steps_per_epoch = len(dataloader)
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# sampler_to_io = None
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# else:
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# dataloader = prepare_variable_dataloader(
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# bucket_config=cfg.get("bucket_config", None),
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# num_bucket_build_workers=cfg.get("num_bucket_build_workers", 1),
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# **dataloader_args,
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# )
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# num_steps_per_epoch = dataloader.batch_sampler.get_num_batch() // dist.get_world_size()
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# sampler_to_io = None if cfg.get("start_from_scratch ", False) else dataloader.batch_sampler
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# ======================================================
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# 3. build model
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# ======================================================
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logger.info("Building models...")
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# == build text-encoder and vae ==
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text_encoder = build_module(cfg.text_encoder, MODELS, device=device, dtype=dtype)
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vae = build_module(cfg.vae, MODELS).to(device, dtype).eval()
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# == build diffusion model ==
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# modify here
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# input_size = (dataset.num_frames, *dataset.image_size)
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# latent_size = vae.get_latent_size(input_size)
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latent_size = None, None, None
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model = (
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build_module(
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cfg.model,
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MODELS,
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input_size=latent_size,
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in_channels=vae.out_channels,
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caption_channels=text_encoder.output_dim,
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model_max_length=text_encoder.model_max_length,
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)
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.to(device, dtype)
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.train()
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)
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model_numel, model_numel_trainable = get_model_numel(model)
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logger.info(
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"[Diffusion] Trainable model params: %s, Total model params: %s",
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format_numel_str(model_numel_trainable),
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format_numel_str(model_numel),
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)
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# == build ema for diffusion model ==
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ema = deepcopy(model).to(torch.float32).to(device)
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requires_grad(ema, False)
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ema_shape_dict = record_model_param_shape(ema)
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ema.eval()
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update_ema(ema, model, decay=0, sharded=False)
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# == setup loss function, build scheduler ==
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scheduler = build_module(cfg.scheduler, SCHEDULERS)
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# == setup optimizer ==
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optimizer = HybridAdam(
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filter(lambda p: p.requires_grad, model.parameters()),
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adamw_mode=True,
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lr=cfg.get("lr", 1e-4),
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weight_decay=cfg.get("weight_decay", 0),
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eps=cfg.get("adam_eps", 1e-8),
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)
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lr_scheduler = None
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# == additional preparation ==
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if cfg.get("grad_checkpoint", False):
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set_grad_checkpoint(model)
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if cfg.get("mask_ratios", None) is not None:
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mask_generator = MaskGenerator(cfg.mask_ratios)
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# =======================================================
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# 4. distributed training preparation with colossalai
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# =======================================================
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logger.info("Preparing for distributed training...")
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# == boosting ==
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# 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
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torch.set_default_dtype(dtype)
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model, optimizer, _, dataloader, lr_scheduler = booster.boost(
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model=model,
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optimizer=optimizer,
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lr_scheduler=lr_scheduler,
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dataloader=dataloader,
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)
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torch.set_default_dtype(torch.float)
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logger.info("Boosting model for distributed training")
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# == global variables ==
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# modify here
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cfg_epochs = cfg.get("epochs", 1)
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assert cfg_epochs == 1
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start_epoch = start_step = log_step = sampler_start_idx = acc_step = 0
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running_loss = 0.0
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logger.info("Training for %s epochs with %s steps per epoch", cfg_epochs, num_steps_per_epoch)
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# == resume ==
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if cfg.get("load", None) is not None:
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logger.info("Loading checkpoint")
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ret = load(
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booster,
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cfg.load,
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model=model,
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ema=ema,
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optimizer=optimizer,
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lr_scheduler=lr_scheduler,
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sampler=sampler_to_io,
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)
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if not cfg.get("start_from_scratch ", False):
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start_epoch, start_step, sampler_start_idx = ret
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logger.info("Loaded checkpoint %s at epoch %s step %s", cfg.load, start_epoch, start_step)
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if cfg.dataset.type == DEFAULT_DATASET_NAME:
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dataloader.sampler.set_start_index(sampler_start_idx)
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model_sharding(ema)
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# =======================================================
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# 5. training loop
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# =======================================================
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dist.barrier()
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for epoch in range(start_epoch, cfg_epochs):
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# == set dataloader to new epoch ==
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if cfg.dataset.type == DEFAULT_DATASET_NAME:
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dataloader.sampler.set_epoch(epoch)
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dataloader_iter = iter(dataloader)
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logger.info("Beginning epoch %s...", epoch)
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# == training loop in an epoch ==
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with tqdm(
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enumerate(dataloader_iter, start=start_step),
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desc=f"Epoch {epoch}",
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disable=not coordinator.is_master(),
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initial=start_step,
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total=num_steps_per_epoch,
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) as pbar:
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for step, batch in pbar:
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# modify here
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x = batch["x"].to(device, dtype) # feat of vae encoder
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print(step, dist.get_rank(), batch["x"].shape)
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continue
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# x = batch.pop("video").to(device, dtype) # [B, C, T, H, W]
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# y = batch.pop("text")
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# == visual and text encoding ==
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# with torch.no_grad():
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# # Prepare visual inputs
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# x = vae.encode(x) # [B, C, T, H/P, W/P]
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# # Prepare text inputs
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# model_args = text_encoder.encode(y)
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model_args = {}
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# == mask ==
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mask = None
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if cfg.get("mask_ratios", None) is not None:
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mask = mask_generator.get_masks(x)
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model_args["x_mask"] = mask
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# == video meta info ==
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for k, v in batch.items():
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model_args[k] = v.to(device, dtype)
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# == diffusion loss computation ==
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loss_dict = scheduler.training_losses(model, x, model_args, mask=mask)
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# == backward & update ==
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loss = loss_dict["loss"].mean()
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booster.backward(loss=loss, optimizer=optimizer)
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optimizer.step()
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optimizer.zero_grad()
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# == update EMA ==
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update_ema(ema, model.module, optimizer=optimizer, decay=cfg.get("ema_decay", 0.9999))
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# == update log info ==
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all_reduce_mean(loss)
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running_loss += loss.item()
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global_step = epoch * num_steps_per_epoch + step
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log_step += 1
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acc_step += 1
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# == logging ==
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if coordinator.is_master() and (global_step + 1) % cfg.get("log_every", 1) == 0:
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avg_loss = running_loss / log_step
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# progress bar
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pbar.set_postfix({"loss": avg_loss, "step": step, "global_step": global_step})
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# tensorboard
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tb_writer.add_scalar("loss", loss.item(), global_step)
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# wandb
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if cfg.get("wandb", False):
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wandb.log(
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{
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"iter": global_step,
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"epoch": epoch,
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"loss": loss.item(),
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"avg_loss": avg_loss,
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"acc_step": acc_step,
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},
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step=global_step,
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)
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running_loss = 0.0
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log_step = 0
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# == checkpoint saving ==
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ckpt_every = cfg.get("ckpt_every", 0)
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if ckpt_every > 0 and (global_step + 1) % ckpt_every == 0:
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model_gathering(ema, ema_shape_dict)
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save(
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booster,
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exp_dir,
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model=model,
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ema=ema,
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optimizer=optimizer,
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lr_scheduler=lr_scheduler,
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sampler=sampler_to_io,
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epoch=epoch,
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step=step + 1,
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global_step=global_step + 1,
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batch_size=cfg.get("batch_size", None),
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)
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if dist.get_rank() == 0:
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model_sharding(ema)
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logger.info(
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"Saved checkpoint at epoch %s step %s global_step %s to %s",
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epoch,
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step + 1,
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global_step + 1,
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exp_dir,
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)
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# NOTE: the continue epochs are not resumed, so we need to reset the sampler start index and start step
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if cfg.dataset.type == DEFAULT_DATASET_NAME:
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dataloader.sampler.set_start_index(0)
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else:
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dataloader.batch_sampler.set_epoch(epoch + 1)
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logger.info("Epoch done, recomputing batch sampler")
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start_step = 0
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if __name__ == "__main__":
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main()
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