import gc import logging from utils.dataset import cycle from utils.dataset import LatentLMDBDataset from utils.distributed import EMA_FSDP, fsdp_wrap, fsdp_state_dict, launch_distributed_job from utils.misc import ( set_seed, merge_dict_list ) import torch.distributed as dist from omegaconf import OmegaConf import torch import wandb import time import os from model import NaiveConsistency 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.world_size = dist.get_world_size() 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 = NaiveConsistency(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, cpu_offload=True ) self.model.generator_ema = fsdp_wrap( self.model.generator_ema, sharding_strategy=config.sharding_strategy, mixed_precision=config.mixed_precision, wrap_strategy=config.generator_fsdp_wrap_strategy, cpu_offload=True ) self.model.teacher = fsdp_wrap( self.model.teacher, sharding_strategy=config.sharding_strategy, mixed_precision=config.mixed_precision, wrap_strategy=config.real_score_fsdp_wrap_strategy, cpu_offload=True ) 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, cpu_offload=True ) 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 ) self.generator_ema = EMA_FSDP(self.model.generator, decay=self.config.ema_weight) # Step 3: Initialize the dataloader dataset = LatentLMDBDataset( 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. Load the causal diffusion model as the teacher model 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"] fixed = {} for k, v in state_dict.items(): if k.startswith("model._fsdp_wrapped_module."): k = k.replace("model._fsdp_wrapped_module.", "model.", 1) fixed[k] = v state_dict = fixed elif "model" in state_dict: state_dict = state_dict["model"] elif "generator_ema" in state_dict: gen_sd = state_dict["generator_ema"] fixed = {} for k, v in gen_sd.items(): if k.startswith("model._fsdp_wrapped_module."): k = k.replace("model._fsdp_wrapped_module.", "model.", 1) fixed[k] = v state_dict = fixed self.model.generator.load_state_dict( state_dict, strict=True ) self.model.teacher.load_state_dict( state_dict, strict=True ) ############################################################################################################# self.max_grad_norm_generator = getattr(config, "max_grad_norm_generator", 10.0) self.max_grad_norm_critic = getattr(config, "max_grad_norm_critic", 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_ema": self.generator_ema.full_state_dict(self.model.generator), } 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 fwdbwd_one_step(self, batch, clean_latent=None): self.model.eval() if self.step % 20 == 0: torch.cuda.empty_cache() # Step 1: Get the next batch of text prompts text_prompts = batch["prompts"] 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: Store gradients for the generator (if training the generator) generator_loss, generator_log_dict = self.model.generator_loss( conditional_dict=conditional_dict, unconditional_dict=unconditional_dict, clean_latent=clean_latent, ema_model = self.generator_ema ) generator_loss.backward() generator_grad_norm = self.model.generator.clip_grad_norm_( self.max_grad_norm_generator) generator_log_dict.update({"generator_loss": generator_loss, "generator_grad_norm": generator_grad_norm}) return generator_log_dict def train(self): start_step = self.step while True: self.generator_optimizer.zero_grad(set_to_none=True) batch = next(self.dataloader) generator_log_dict = self.fwdbwd_one_step(batch, clean_latent=batch["clean_latent"]) self.generator_optimizer.step() if self.generator_ema is not None: self.generator_ema.update(self.model.generator) # Increment the step since we finished gradient update self.step += 1 # Save the model if (not self.config.no_save) and (self.step - start_step) > 0 and self.step % self.config.log_iters == 0: torch.cuda.empty_cache() self.save() torch.cuda.empty_cache() # Logging if self.is_main_process: wandb_loss_dict = {} wandb_loss_dict.update( { "generator_loss": generator_log_dict["generator_loss"].mean().item(), "generator_grad_norm": generator_log_dict["generator_grad_norm"].mean().item() } ) 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() torch.cuda.empty_cache() 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