import gc import logging from model import CausalDiffusion from utils.dataset import ShardingLMDBDataset, cycle from utils.misc import set_seed import torch.distributed as dist from omegaconf import OmegaConf import torch import wandb import time import os from utils.distributed import EMA_FSDP, barrier, fsdp_wrap, fsdp_state_dict, launch_distributed_job class Trainer: def __init__(self, config): self.config = config self.step = 0 # Step 1: Initialize the distributed training environment (rank, seed, dtype, logging etc.) torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cudnn.allow_tf32 = True launch_distributed_job() global_rank = dist.get_rank() self.dtype = torch.bfloat16 if config.mixed_precision else torch.float32 self.device = torch.cuda.current_device() self.is_main_process = global_rank == 0 self.causal = config.causal self.disable_wandb = config.disable_wandb # use a random seed for the training if config.seed == 0: random_seed = torch.randint(0, 10000000, (1,), device=self.device) dist.broadcast(random_seed, src=0) config.seed = random_seed.item() set_seed(config.seed + global_rank) if self.is_main_process and not self.disable_wandb: wandb.login(host=config.wandb_host, key=config.wandb_key) wandb.init( config=OmegaConf.to_container(config, resolve=True), name=config.config_name, mode="online", entity=config.wandb_entity, project=config.wandb_project, dir=config.wandb_save_dir ) self.output_path = config.logdir # Step 2: Initialize the model and optimizer self.model = CausalDiffusion(config, device=self.device) self.model.generator = fsdp_wrap( self.model.generator, sharding_strategy=config.sharding_strategy, mixed_precision=config.mixed_precision, wrap_strategy=config.generator_fsdp_wrap_strategy ) self.model.text_encoder = fsdp_wrap( self.model.text_encoder, sharding_strategy=config.sharding_strategy, mixed_precision=config.mixed_precision, wrap_strategy=config.text_encoder_fsdp_wrap_strategy ) if not config.no_visualize or config.load_raw_video: self.model.vae = self.model.vae.to( device=self.device, dtype=torch.bfloat16 if config.mixed_precision else torch.float32) self.generator_optimizer = torch.optim.AdamW( [param for param in self.model.generator.parameters() if param.requires_grad], lr=config.lr, betas=(config.beta1, config.beta2), weight_decay=config.weight_decay ) # Step 3: Initialize the dataloader dataset = ShardingLMDBDataset(config.data_path, max_pair=int(1e8)) sampler = torch.utils.data.distributed.DistributedSampler( dataset, shuffle=True, drop_last=True) dataloader = torch.utils.data.DataLoader( dataset, batch_size=config.batch_size, sampler=sampler, num_workers=8) if dist.get_rank() == 0: print("DATASET SIZE %d" % len(dataset)) self.dataloader = cycle(dataloader) ############################################################################################################## # 6. Set up EMA parameter containers rename_param = ( lambda name: name.replace("_fsdp_wrapped_module.", "") .replace("_checkpoint_wrapped_module.", "") .replace("_orig_mod.", "") ) self.name_to_trainable_params = {} for n, p in self.model.generator.named_parameters(): if not p.requires_grad: continue renamed_n = rename_param(n) self.name_to_trainable_params[renamed_n] = p ema_weight = config.ema_weight self.generator_ema = None if (ema_weight is not None) and (ema_weight > 0.0): print(f"Setting up EMA with weight {ema_weight}") self.generator_ema = EMA_FSDP(self.model.generator, decay=ema_weight) ############################################################################################################## # 7. (If resuming) Load the model and optimizer, lr_scheduler, ema's statedicts if getattr(config, "generator_ckpt", False): print(f"Loading pretrained generator from {config.generator_ckpt}") state_dict = torch.load(config.generator_ckpt, map_location="cpu") if "generator" in state_dict: state_dict = state_dict["generator"] elif "model" in state_dict: state_dict = state_dict["model"] self.model.generator.load_state_dict( state_dict, strict=True ) ############################################################################################################## # Let's delete EMA params for early steps to save some computes at training and inference if self.step < config.ema_start_step: self.generator_ema = None self.max_grad_norm = 10.0 self.previous_time = None def save(self): print("Start gathering distributed model states...") generator_state_dict = fsdp_state_dict( self.model.generator) if self.config.ema_start_step < self.step: state_dict = { "generator": generator_state_dict, "generator_ema": self.generator_ema.state_dict(), } else: state_dict = { "generator": generator_state_dict, } if self.is_main_process: os.makedirs(os.path.join(self.output_path, f"checkpoint_model_{self.step:06d}"), exist_ok=True) torch.save(state_dict, os.path.join(self.output_path, f"checkpoint_model_{self.step:06d}", "model.pt")) print("Model saved to", os.path.join(self.output_path, f"checkpoint_model_{self.step:06d}", "model.pt")) def train_one_step(self, batch): self.log_iters = 1 if self.step % 20 == 0: torch.cuda.empty_cache() # Step 1: Get the next batch of text prompts text_prompts = batch["prompts"] if not self.config.load_raw_video: # precomputed latent clean_latent = batch["ode_latent"][:, -1].to( device=self.device, dtype=self.dtype) else: # encode raw video to latent frames = batch["frames"].to( device=self.device, dtype=self.dtype) with torch.no_grad(): clean_latent = self.model.vae.encode_to_latent( frames).to(device=self.device, dtype=self.dtype) image_latent = clean_latent[:, 0:1, ] batch_size = len(text_prompts) image_or_video_shape = list(self.config.image_or_video_shape) image_or_video_shape[0] = batch_size # Step 2: Extract the conditional infos with torch.no_grad(): conditional_dict = self.model.text_encoder( text_prompts=text_prompts) if not getattr(self, "unconditional_dict", None): unconditional_dict = self.model.text_encoder( text_prompts=[self.config.negative_prompt] * batch_size) unconditional_dict = {k: v.detach() for k, v in unconditional_dict.items()} self.unconditional_dict = unconditional_dict # cache the unconditional_dict else: unconditional_dict = self.unconditional_dict # Step 3: Train the generator generator_loss, log_dict = self.model.generator_loss( image_or_video_shape=image_or_video_shape, conditional_dict=conditional_dict, unconditional_dict=unconditional_dict, clean_latent=clean_latent, initial_latent=image_latent ) self.generator_optimizer.zero_grad() generator_loss.backward() generator_grad_norm = self.model.generator.clip_grad_norm_( self.max_grad_norm) self.generator_optimizer.step() # Increment the step since we finished gradient update self.step += 1 wandb_loss_dict = { "generator_loss": generator_loss.item(), "generator_grad_norm": generator_grad_norm.item(), } # Step 4: Logging if self.is_main_process: if not self.disable_wandb: wandb.log(wandb_loss_dict, step=self.step) if self.step % self.config.gc_interval == 0: if dist.get_rank() == 0: logging.info("DistGarbageCollector: Running GC.") gc.collect() # Step 5. Create EMA params # TODO: Implement EMA def generate_video(self, pipeline, prompts, image=None): batch_size = len(prompts) sampled_noise = torch.randn( [batch_size, 21, 16, 60, 104], device="cuda", dtype=self.dtype ) video, _ = pipeline.inference( noise=sampled_noise, text_prompts=prompts, return_latents=True ) current_video = video.permute(0, 1, 3, 4, 2).cpu().numpy() * 255.0 return current_video def train(self): while True: batch = next(self.dataloader) self.train_one_step(batch) if (not self.config.no_save) and self.step % self.config.log_iters == 0: torch.cuda.empty_cache() self.save() torch.cuda.empty_cache() barrier() if self.is_main_process: current_time = time.time() if self.previous_time is None: self.previous_time = current_time else: if not self.disable_wandb: wandb.log({"per iteration time": current_time - self.previous_time}, step=self.step) self.previous_time = current_time