mirror of
https://github.com/hpcaitech/Open-Sora.git
synced 2026-04-10 04:37:45 +02:00
70ca63f30b
* [feature] support async ckpt * [feature] support pin memory cache * [doc] update readme
442 lines
17 KiB
Python
442 lines
17 KiB
Python
import os
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import subprocess
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from contextlib import nullcontext
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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 prepare_dataloader
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from opensora.datasets.pin_memory_cache import PinMemoryCache
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from opensora.registry import DATASETS, MODELS, SCHEDULERS, build_module
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from opensora.utils.ckpt_utils import CheckpointIO, model_sharding, record_model_param_shape
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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.lr_scheduler import LinearWarmupLR
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from opensora.utils.misc import (
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Timer,
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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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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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record_time = cfg.get("record_time", False)
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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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checkpoint_io = CheckpointIO()
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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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PinMemoryCache.force_dtype = dtype
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pin_memory_cache_pre_alloc_numels = cfg.get("pin_memory_cache_pre_alloc_numels", [])
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PinMemoryCache.pre_alloc_numels = pin_memory_cache_pre_alloc_numels
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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="Open-Sora", 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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reduce_bucket_size_in_m=cfg.get("reduce_bucket_size_in_m", 20),
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)
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booster = Booster(plugin=plugin)
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torch.set_num_threads(1)
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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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cache_pin_memory = cfg.get("cache_pin_memory", False)
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dataloader_args = dict(
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dataset=dataset,
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batch_size=cfg.get("batch_size", None),
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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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prefetch_factor=cfg.get("prefetch_factor", None),
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cache_pin_memory=cache_pin_memory,
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)
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dataloader, sampler = prepare_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 = len(dataloader)
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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.get("text_encoder", None), MODELS, device=device, dtype=dtype)
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if text_encoder is not None:
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text_encoder_output_dim = text_encoder.output_dim
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text_encoder_model_max_length = text_encoder.model_max_length
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else:
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text_encoder_output_dim = cfg.get("text_encoder_output_dim", 4096)
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text_encoder_model_max_length = cfg.get("text_encoder_model_max_length", 300)
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# == build vae ==
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vae = build_module(cfg.get("vae", None), MODELS)
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if vae is not None:
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vae = vae.to(device, dtype).eval()
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if vae is not None:
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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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vae_out_channels = vae.out_channels
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else:
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latent_size = (None, None, None)
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vae_out_channels = cfg.get("vae_out_channels", 4)
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# == build diffusion model ==
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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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enable_sequence_parallelism=cfg.get("sp_size", 1) > 1,
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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).cpu().to(torch.float32)
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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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# == 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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warmup_steps = cfg.get("warmup_steps", None)
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if warmup_steps is None:
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lr_scheduler = None
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else:
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lr_scheduler = LinearWarmupLR(optimizer, warmup_steps=cfg.get("warmup_steps"))
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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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cfg_epochs = cfg.get("epochs", 1000)
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start_epoch = start_step = log_step = 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 = checkpoint_io.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=None if cfg.get("start_from_scratch", False) else sampler,
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)
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if not cfg.get("start_from_scratch", False):
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start_epoch, start_step = 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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model_sharding(ema, device=device)
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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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timers = {}
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timer_keys = [
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"move_data",
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"encode",
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"mask",
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"diffusion",
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"backward",
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"update_ema",
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"reduce_loss",
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"optim",
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"ckpt",
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]
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for key in timer_keys:
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if record_time:
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timers[key] = Timer(key, coordinator=coordinator)
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else:
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timers[key] = nullcontext()
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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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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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# if cache_pin_memory:
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# print(f"==debug== rank{dist.get_rank()} {dataloader_iter.get_cache_info()}")
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timer_list = []
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with timers["move_data"] as move_data_t:
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pinned_video = batch.pop("video")
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x = pinned_video.to(device, dtype, non_blocking=True) # [B, C, T, H, W]
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y = batch.pop("text")
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if record_time:
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timer_list.append(move_data_t)
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# == visual and text encoding ==
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with timers["encode"] as encode_t:
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with torch.no_grad():
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# Prepare visual inputs
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if cfg.get("load_video_features", False):
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x = x.to(device, dtype)
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else:
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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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if cfg.get("load_text_features", False):
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model_args = {"y": y.to(device, dtype)}
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mask = batch.pop("mask")
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if isinstance(mask, torch.Tensor):
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mask = mask.to(device, dtype)
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model_args["mask"] = mask
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else:
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model_args = text_encoder.encode(y)
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if record_time:
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timer_list.append(encode_t)
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# == mask ==
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with timers["mask"] as mask_t:
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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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if record_time:
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timer_list.append(mask_t)
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# == video meta info ==
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for k, v in batch.items():
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if isinstance(v, torch.Tensor):
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model_args[k] = v.to(device, dtype)
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if cache_pin_memory:
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dataloader_iter.remove_cache(pinned_video)
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# == diffusion loss computation ==
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with timers["diffusion"] as loss_t:
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loss_dict = scheduler.training_losses(model, x, model_args, mask=mask)
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if record_time:
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timer_list.append(loss_t)
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# == backward & update ==
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with timers["backward"] as backward_t:
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loss = loss_dict["loss"].mean()
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booster.backward(loss=loss, optimizer=optimizer)
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if record_time:
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timer_list.append(backward_t)
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with timers["optim"] as optim_t:
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optimizer.step()
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optimizer.zero_grad()
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# update learning rate
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if lr_scheduler is not None:
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lr_scheduler.step()
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if record_time:
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timer_list.append(optim_t)
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# == update EMA ==
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with timers["update_ema"] as ema_t:
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update_ema(ema, model.module, optimizer=optimizer, decay=cfg.get("ema_decay", 0.9999))
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if record_time:
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timer_list.append(ema_t)
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# == update log info ==
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with timers["reduce_loss"] as reduce_loss_t:
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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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if record_time:
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timer_list.append(reduce_loss_t)
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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_dict = {
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"iter": global_step,
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"acc_step": acc_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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"lr": optimizer.param_groups[0]["lr"],
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}
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if record_time:
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wandb_dict.update(
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{
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"debug/move_data_time": move_data_t.elapsed_time,
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"debug/encode_time": encode_t.elapsed_time,
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"debug/mask_time": mask_t.elapsed_time,
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"debug/diffusion_time": loss_t.elapsed_time,
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"debug/backward_time": backward_t.elapsed_time,
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"debug/update_ema_time": ema_t.elapsed_time,
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"debug/reduce_loss_time": reduce_loss_t.elapsed_time,
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}
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)
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wandb.log(wandb_dict, step=global_step)
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running_loss = 0.0
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log_step = 0
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# == uncomment to clear ram cache ==
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# if ckpt_every > 0 and (global_step + 1) % ckpt_every == 0 and coordinator.is_master():
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# subprocess.run("sync && sudo sh -c \"echo 3 > /proc/sys/vm/drop_caches\"", shell=True)
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# == checkpoint saving ==
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ckpt_every = cfg.get("ckpt_every", 0)
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with timers["ckpt"] as ckpt_t:
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if ckpt_every > 0 and (global_step + 1) % ckpt_every == 0:
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save_dir = checkpoint_io.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,
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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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ema_shape_dict=ema_shape_dict,
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async_io=True,
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)
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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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save_dir,
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)
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if record_time:
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timer_list.append(ckpt_t)
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# uncomment below 3 lines to benchmark checkpoint
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# if ckpt_every > 0 and (global_step + 1) % ckpt_every == 0:
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# booster.checkpoint_io._sync_io()
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# checkpoint_io._sync_io()
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if record_time:
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log_str = f"Rank {dist.get_rank()} | Epoch {epoch} | Step {step} | "
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for timer in timer_list:
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log_str += f"{timer.name}: {timer.elapsed_time:.3f}s | "
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print(log_str)
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sampler.reset()
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start_step = 0
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if __name__ == "__main__":
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main()
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