Open-Sora / scripts /train.py
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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 DatasetFromCSV, get_transforms_image, get_transforms_video, prepare_dataloader
from opensora.registry import 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 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 = DatasetFromCSV(
cfg.data_path,
# TODO: change transforms
transform=(
get_transforms_video(cfg.image_size[0])
if not cfg.use_image_transform
else get_transforms_image(cfg.image_size[0])
),
num_frames=cfg.num_frames,
frame_interval=cfg.frame_interval,
root=cfg.root,
)
# TODO: use plugin's prepare dataloader
# a batch contains:
# {
# "video": torch.Tensor, # [B, C, T, H, W],
# "text": List[str],
# }
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(),
)
logger.info(f"Dataset contains {len(dataset):,} videos ({cfg.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
input_size = (cfg.num_frames, *cfg.image_size)
vae = build_module(cfg.vae, MODELS)
latent_size = vae.get_latent_size(input_size)
text_encoder = build_module(cfg.text_encoder, MODELS, device=device) # T5 must be fp32
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()
# =======================================================
# 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")
dataloader.sampler.set_start_index(sampler_start_idx)
model_sharding(ema)
# 6.2. training loop
for epoch in range(start_epoch, cfg.epochs):
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"]
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)
# Diffusion
t = torch.randint(0, scheduler.num_timesteps, (x.shape[0],), device=device)
loss_dict = scheduler.training_losses(model, x, t, model_args)
# 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()