import argparse import hashlib import math import os import shutil from pathlib import Path from typing import Optional import torch import torch.nn.functional as F import torch.utils.checkpoint from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel from diffusers.optimization import get_scheduler from huggingface_hub import HfFolder, Repository, create_repo, whoami from PIL import Image from torch.utils.data import Dataset from torchvision import transforms from tqdm.auto import tqdm from transformers import AutoTokenizer, PretrainedConfig import colossalai from colossalai.booster import Booster from colossalai.booster.plugin import GeminiPlugin, LowLevelZeroPlugin, TorchDDPPlugin from colossalai.context.parallel_mode import ParallelMode from colossalai.core import global_context as gpc from colossalai.logging import disable_existing_loggers, get_dist_logger from colossalai.nn.optimizer import HybridAdam from colossalai.utils import get_current_device from colossalai.zero import ColoInitContext from colossalai.zero.gemini import get_static_torch_model disable_existing_loggers() logger = get_dist_logger() def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str): text_encoder_config = PretrainedConfig.from_pretrained( pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, ) model_class = text_encoder_config.architectures[0] if model_class == "CLIPTextModel": from transformers import CLIPTextModel return CLIPTextModel elif model_class == "RobertaSeriesModelWithTransformation": from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation return RobertaSeriesModelWithTransformation else: raise ValueError(f"{model_class} is not supported.") def parse_args(input_args=None): parser = argparse.ArgumentParser(description="Simple example of a training script.") parser.add_argument( "--pretrained_model_name_or_path", type=str, default=None, required=True, help="Path to pretrained model or model identifier from huggingface.co/models.", ) parser.add_argument( "--externel_unet_path", type=str, default=None, required=False, help="Path to the externel unet model.", ) parser.add_argument( "--revision", type=str, default=None, required=False, help="Revision of pretrained model identifier from huggingface.co/models.", ) parser.add_argument( "--tokenizer_name", type=str, default=None, help="Pretrained tokenizer name or path if not the same as model_name", ) parser.add_argument( "--instance_data_dir", type=str, default=None, required=True, help="A folder containing the training data of instance images.", ) parser.add_argument( "--class_data_dir", type=str, default=None, required=False, help="A folder containing the training data of class images.", ) parser.add_argument( "--instance_prompt", type=str, default="a photo of sks dog", required=False, help="The prompt with identifier specifying the instance", ) parser.add_argument( "--class_prompt", type=str, default=None, help="The prompt to specify images in the same class as provided instance images.", ) parser.add_argument( "--with_prior_preservation", default=False, action="store_true", help="Flag to add prior preservation loss.", ) parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.") parser.add_argument( "--num_class_images", type=int, default=100, help=("Minimal class images for prior preservation loss. If there are not enough images already present in" " class_data_dir, additional images will be sampled with class_prompt."), ) parser.add_argument( "--output_dir", type=str, default="text-inversion-model", help="The output directory where the model predictions and checkpoints will be written.", ) parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") parser.add_argument( "--resolution", type=int, default=512, help=("The resolution for input images, all the images in the train/validation dataset will be resized to this" " resolution"), ) parser.add_argument( "--offload_optim_frac", type=float, default=1.0, help="Fraction of optimizer states to be offloaded. Valid when using colossalai as dist plan.", ) parser.add_argument( "--center_crop", default=False, action="store_true", help=("Whether to center crop the input images to the resolution. If not set, the images will be randomly" " cropped. The images will be resized to the resolution first before cropping."), ) parser.add_argument("--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader.") parser.add_argument("--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images.") parser.add_argument("--num_train_epochs", type=int, default=1) parser.add_argument( "--max_train_steps", type=int, default=None, help="Total number of training steps to perform. If provided, overrides num_train_epochs.", ) parser.add_argument("--save_steps", type=int, default=500, help="Save checkpoint every X updates steps.") parser.add_argument( "--gradient_checkpointing", action="store_true", help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.", ) parser.add_argument( "--learning_rate", type=float, default=5e-6, help="Initial learning rate (after the potential warmup period) to use.", ) parser.add_argument( "--scale_lr", action="store_true", default=False, help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", ) parser.add_argument( "--lr_scheduler", type=str, default="constant", help=('The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' ' "constant", "constant_with_warmup"]'), ) parser.add_argument("--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler.") parser.add_argument("--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes.") parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.") parser.add_argument("--test_run", default=False, help="Whether to use a smaller dataset for test run.") parser.add_argument( "--hub_model_id", type=str, default=None, help="The name of the repository to keep in sync with the local `output_dir`.", ) parser.add_argument('-p', '--plugin', type=str, default='torch_ddp', choices=['torch_ddp', 'torch_ddp_fp16', 'gemini', 'low_level_zero'], help="plugin to use") parser.add_argument( "--logging_dir", type=str, default="logs", help=("[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to" " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."), ) parser.add_argument( "--mixed_precision", type=str, default=None, choices=["no", "fp16", "bf16"], help=( "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=" " 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the" " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."), ) parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank") if input_args is not None: args = parser.parse_args(input_args) else: args = parser.parse_args() env_local_rank = int(os.environ.get("LOCAL_RANK", -1)) if env_local_rank != -1 and env_local_rank != args.local_rank: args.local_rank = env_local_rank if args.with_prior_preservation: if args.class_data_dir is None: raise ValueError("You must specify a data directory for class images.") if args.class_prompt is None: raise ValueError("You must specify prompt for class images.") else: if args.class_data_dir is not None: logger.warning("You need not use --class_data_dir without --with_prior_preservation.") if args.class_prompt is not None: logger.warning("You need not use --class_prompt without --with_prior_preservation.") return args class DreamBoothDataset(Dataset): """ A dataset to prepare the instance and class images with the prompts for fine-tuning the model. It pre-processes the images and the tokenizes prompts. """ def __init__( self, instance_data_root, instance_prompt, tokenizer, class_data_root=None, class_prompt=None, size=512, center_crop=False, test=False, ): self.size = size self.center_crop = center_crop self.tokenizer = tokenizer self.instance_data_root = Path(instance_data_root) if not self.instance_data_root.exists(): raise ValueError("Instance images root doesn't exists.") self.instance_images_path = list(Path(instance_data_root).iterdir()) if test: self.instance_images_path = self.instance_images_path[:10] self.num_instance_images = len(self.instance_images_path) self.instance_prompt = instance_prompt self._length = self.num_instance_images if class_data_root is not None: self.class_data_root = Path(class_data_root) self.class_data_root.mkdir(parents=True, exist_ok=True) self.class_images_path = list(self.class_data_root.iterdir()) self.num_class_images = len(self.class_images_path) self._length = max(self.num_class_images, self.num_instance_images) self.class_prompt = class_prompt else: self.class_data_root = None self.image_transforms = transforms.Compose([ transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR), transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size), transforms.ToTensor(), transforms.Normalize([0.5], [0.5]), ]) def __len__(self): return self._length def __getitem__(self, index): example = {} instance_image = Image.open(self.instance_images_path[index % self.num_instance_images]) if not instance_image.mode == "RGB": instance_image = instance_image.convert("RGB") example["instance_images"] = self.image_transforms(instance_image) example["instance_prompt_ids"] = self.tokenizer( self.instance_prompt, padding="do_not_pad", truncation=True, max_length=self.tokenizer.model_max_length, ).input_ids if self.class_data_root: class_image = Image.open(self.class_images_path[index % self.num_class_images]) if not class_image.mode == "RGB": class_image = class_image.convert("RGB") example["class_images"] = self.image_transforms(class_image) example["class_prompt_ids"] = self.tokenizer( self.class_prompt, padding="do_not_pad", truncation=True, max_length=self.tokenizer.model_max_length, ).input_ids return example class PromptDataset(Dataset): "A simple dataset to prepare the prompts to generate class images on multiple GPUs." def __init__(self, prompt, num_samples): self.prompt = prompt self.num_samples = num_samples def __len__(self): return self.num_samples def __getitem__(self, index): example = {} example["prompt"] = self.prompt example["index"] = index return example def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None): if token is None: token = HfFolder.get_token() if organization is None: username = whoami(token)["name"] return f"{username}/{model_id}" else: return f"{organization}/{model_id}" def main(args): if args.seed is None: colossalai.launch_from_torch(config={}) else: colossalai.launch_from_torch(config={}, seed=args.seed) local_rank = gpc.get_local_rank(ParallelMode.DATA) world_size = gpc.get_world_size(ParallelMode.DATA) if args.with_prior_preservation: class_images_dir = Path(args.class_data_dir) if not class_images_dir.exists(): class_images_dir.mkdir(parents=True) cur_class_images = len(list(class_images_dir.iterdir())) if cur_class_images < args.num_class_images: torch_dtype = torch.float16 if get_current_device() == "cuda" else torch.float32 pipeline = DiffusionPipeline.from_pretrained( args.pretrained_model_name_or_path, torch_dtype=torch_dtype, safety_checker=None, revision=args.revision, ) pipeline.set_progress_bar_config(disable=True) num_new_images = args.num_class_images - cur_class_images logger.info(f"Number of class images to sample: {num_new_images}.") sample_dataset = PromptDataset(args.class_prompt, num_new_images) sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size) pipeline.to(get_current_device()) for example in tqdm( sample_dataloader, desc="Generating class images", disable=not local_rank == 0, ): images = pipeline(example["prompt"]).images for i, image in enumerate(images): hash_image = hashlib.sha256(image.tobytes()).hexdigest() image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg" image.save(image_filename) del pipeline # Handle the repository creation if local_rank == 0: if args.push_to_hub: if args.hub_model_id is None: repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token) else: repo_name = args.hub_model_id create_repo(repo_name, exist_ok=True, token=args.hub_token) repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token) with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore: if "step_*" not in gitignore: gitignore.write("step_*\n") if "epoch_*" not in gitignore: gitignore.write("epoch_*\n") elif args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) # Load the tokenizer if args.tokenizer_name: logger.info(f"Loading tokenizer from {args.tokenizer_name}", ranks=[0]) tokenizer = AutoTokenizer.from_pretrained( args.tokenizer_name, revision=args.revision, use_fast=False, ) elif args.pretrained_model_name_or_path: logger.info("Loading tokenizer from pretrained model", ranks=[0]) tokenizer = AutoTokenizer.from_pretrained( args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision, use_fast=False, ) # import correct text encoder class text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path) # Load models and create wrapper for stable diffusion logger.info(f"Loading text_encoder from {args.pretrained_model_name_or_path}", ranks=[0]) text_encoder = text_encoder_cls.from_pretrained( args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, ) logger.info(f"Loading AutoencoderKL from {args.pretrained_model_name_or_path}", ranks=[0]) vae = AutoencoderKL.from_pretrained( args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision, ) if args.externel_unet_path is None: logger.info(f"Loading UNet2DConditionModel from {args.pretrained_model_name_or_path}", ranks=[0]) unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, low_cpu_mem_usage=False) else: logger.info(f"Loading UNet2DConditionModel from {args.externel_unet_path}", ranks=[0]) unet = UNet2DConditionModel.from_pretrained(args.externel_unet_path, revision=args.revision, low_cpu_mem_usage=False) vae.requires_grad_(False) text_encoder.requires_grad_(False) if args.gradient_checkpointing: unet.enable_gradient_checkpointing() if args.scale_lr: args.learning_rate = args.learning_rate * args.train_batch_size * world_size # Use Booster API to use Gemini/Zero with ColossalAI booster_kwargs = {} if args.plugin == 'torch_ddp_fp16': booster_kwargs['mixed_precision'] = 'fp16' if args.plugin.startswith('torch_ddp'): plugin = TorchDDPPlugin() elif args.plugin == 'gemini': plugin = GeminiPlugin(offload_optim_frac=args.offload_optim_frac, strict_ddp_mode=True, initial_scale=2**5) elif args.plugin == 'low_level_zero': plugin = LowLevelZeroPlugin(initial_scale=2**5) booster = Booster(plugin=plugin, **booster_kwargs) # config optimizer for colossalai zero optimizer = HybridAdam(unet.parameters(), lr=args.learning_rate, initial_scale=2**5, clipping_norm=args.max_grad_norm) # load noise_scheduler noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler") # prepare dataset logger.info(f"Prepare dataset from {args.instance_data_dir}", ranks=[0]) train_dataset = DreamBoothDataset(instance_data_root=args.instance_data_dir, instance_prompt=args.instance_prompt, class_data_root=args.class_data_dir if args.with_prior_preservation else None, class_prompt=args.class_prompt, tokenizer=tokenizer, size=args.resolution, center_crop=args.center_crop, test=args.test_run) def collate_fn(examples): input_ids = [example["instance_prompt_ids"] for example in examples] pixel_values = [example["instance_images"] for example in examples] # Concat class and instance examples for prior preservation. # We do this to avoid doing two forward passes. if args.with_prior_preservation: input_ids += [example["class_prompt_ids"] for example in examples] pixel_values += [example["class_images"] for example in examples] pixel_values = torch.stack(pixel_values) pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float() input_ids = tokenizer.pad( { "input_ids": input_ids }, padding="max_length", max_length=tokenizer.model_max_length, return_tensors="pt", ).input_ids batch = { "input_ids": input_ids, "pixel_values": pixel_values, } return batch train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=args.train_batch_size, shuffle=True, collate_fn=collate_fn, num_workers=1) # Scheduler and math around the number of training steps. overrode_max_train_steps = False num_update_steps_per_epoch = math.ceil(len(train_dataloader)) if args.max_train_steps is None: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch overrode_max_train_steps = True lr_scheduler = get_scheduler( args.lr_scheduler, optimizer=optimizer, num_warmup_steps=args.lr_warmup_steps, num_training_steps=args.max_train_steps, ) weight_dtype = torch.float32 if args.mixed_precision == "fp16": weight_dtype = torch.float16 elif args.mixed_precision == "bf16": weight_dtype = torch.bfloat16 # Move text_encode and vae to gpu. # For mixed precision training we cast the text_encoder and vae weights to half-precision # as these models are only used for inference, keeping weights in full precision is not required. vae.to(get_current_device(), dtype=weight_dtype) text_encoder.to(get_current_device(), dtype=weight_dtype) # We need to recalculate our total training steps as the size of the training dataloader may have changed. num_update_steps_per_epoch = math.ceil(len(train_dataloader)) if overrode_max_train_steps: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch # Afterwards we recalculate our number of training epochs args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) unet, optimizer, _, _, lr_scheduler = booster.boost(unet, optimizer, lr_scheduler=lr_scheduler) # Train! total_batch_size = args.train_batch_size * world_size logger.info("***** Running training *****", ranks=[0]) logger.info(f" Num examples = {len(train_dataset)}", ranks=[0]) logger.info(f" Num batches each epoch = {len(train_dataloader)}", ranks=[0]) logger.info(f" Num Epochs = {args.num_train_epochs}", ranks=[0]) logger.info(f" Instantaneous batch size per device = {args.train_batch_size}", ranks=[0]) logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}", ranks=[0]) logger.info(f" Total optimization steps = {args.max_train_steps}", ranks=[0]) # Only show the progress bar once on each machine. progress_bar = tqdm(range(args.max_train_steps), disable=not local_rank == 0) progress_bar.set_description("Steps") global_step = 0 torch.cuda.synchronize() for epoch in range(args.num_train_epochs): unet.train() for step, batch in enumerate(train_dataloader): torch.cuda.reset_peak_memory_stats() # Move batch to gpu for key, value in batch.items(): batch[key] = value.to(get_current_device(), non_blocking=True) # Convert images to latent space optimizer.zero_grad() latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample() latents = latents * 0.18215 # Sample noise that we'll add to the latents noise = torch.randn_like(latents) bsz = latents.shape[0] # Sample a random timestep for each image timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device) timesteps = timesteps.long() # Add noise to the latents according to the noise magnitude at each timestep # (this is the forward diffusion process) noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) # Get the text embedding for conditioning encoder_hidden_states = text_encoder(batch["input_ids"])[0] # Predict the noise residual model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample # Get the target for loss depending on the prediction type if noise_scheduler.config.prediction_type == "epsilon": target = noise elif noise_scheduler.config.prediction_type == "v_prediction": target = noise_scheduler.get_velocity(latents, noise, timesteps) else: raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}") if args.with_prior_preservation: # Chunk the noise and model_pred into two parts and compute the loss on each part separately. model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0) target, target_prior = torch.chunk(target, 2, dim=0) # Compute instance loss loss = F.mse_loss(model_pred.float(), target.float(), reduction="none").mean([1, 2, 3]).mean() # Compute prior loss prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean") # Add the prior loss to the instance loss. loss = loss + args.prior_loss_weight * prior_loss else: loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") optimizer.backward(loss) optimizer.step() lr_scheduler.step() logger.info(f"max GPU_mem cost is {torch.cuda.max_memory_allocated()/2**20} MB", ranks=[0]) # Checks if the accelerator has performed an optimization step behind the scenes progress_bar.update(1) global_step += 1 logs = { "loss": loss.detach().item(), "lr": optimizer.param_groups[0]["lr"], } # lr_scheduler.get_last_lr()[0]} progress_bar.set_postfix(**logs) if global_step % args.save_steps == 0: torch.cuda.synchronize() save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}") booster.save_model(unet, os.path.join(save_path, "diffusion_pytorch_model.bin")) if local_rank == 0: if not os.path.exists(os.path.join(save_path, "config.json")): shutil.copy(os.path.join(args.pretrained_model_name_or_path, "unet/config.json"), save_path) logger.info(f"Saving model checkpoint to {save_path}", ranks=[0]) if global_step >= args.max_train_steps: break torch.cuda.synchronize() booster.save_model(unet, os.path.join(args.output_dir, "diffusion_pytorch_model.bin")) logger.info(f"Saving model checkpoint to {args.output_dir} on rank {local_rank}") if local_rank == 0: if not os.path.exists(os.path.join(args.output_dir, "config.json")): shutil.copy(os.path.join(args.pretrained_model_name_or_path, "unet/config.json"), args.output_dir) if args.push_to_hub: repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True) if __name__ == "__main__": args = parse_args() main(args)