#!/usr/bin/env python3 """Trains Karras et al. (2022) diffusion models.""" import argparse from copy import deepcopy from functools import partial import importlib.util import math import json from pathlib import Path import time import accelerate import safetensors.torch as safetorch import torch import torch._dynamo from torch import distributed as dist from torch import multiprocessing as mp from torch import optim from torch.utils import data, flop_counter from torchvision import datasets, transforms, utils from tqdm.auto import tqdm import k_diffusion as K def ensure_distributed(): if not dist.is_initialized(): dist.init_process_group(world_size=1, rank=0, store=dist.HashStore()) def main(): p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.ArgumentDefaultsHelpFormatter) p.add_argument('--batch-size', type=int, default=64, help='the batch size') p.add_argument('--checkpointing', action='store_true', help='enable gradient checkpointing') p.add_argument('--clip-model', type=str, default='ViT-B/16', choices=K.evaluation.CLIPFeatureExtractor.available_models(), help='the CLIP model to use to evaluate') p.add_argument('--compile', action='store_true', help='compile the model') p.add_argument('--config', type=str, required=True, help='the configuration file') p.add_argument('--demo-every', type=int, default=500, help='save a demo grid every this many steps') p.add_argument('--dinov2-model', type=str, default='vitl14', choices=K.evaluation.DINOv2FeatureExtractor.available_models(), help='the DINOv2 model to use to evaluate') p.add_argument('--end-step', type=int, default=None, help='the step to end training at') p.add_argument('--evaluate-every', type=int, default=10000, help='evaluate every this many steps') p.add_argument('--evaluate-n', type=int, default=2000, help='the number of samples to draw to evaluate') p.add_argument('--evaluate-only', action='store_true', help='evaluate instead of training') p.add_argument('--evaluate-with', type=str, default='inception', choices=['inception', 'clip', 'dinov2'], help='the feature extractor to use for evaluation') p.add_argument('--gns', action='store_true', help='measure the gradient noise scale (DDP only, disables stratified sampling)') p.add_argument('--grad-accum-steps', type=int, default=1, help='the number of gradient accumulation steps') p.add_argument('--lr', type=float, help='the learning rate') p.add_argument('--mixed-precision', type=str, help='the mixed precision type') p.add_argument('--name', type=str, default='model', help='the name of the run') p.add_argument('--num-workers', type=int, default=8, help='the number of data loader workers') p.add_argument('--reset-ema', action='store_true', help='reset the EMA') p.add_argument('--resume', type=str, help='the checkpoint to resume from') p.add_argument('--resume-inference', type=str, help='the inference checkpoint to resume from') p.add_argument('--sample-n', type=int, default=64, help='the number of images to sample for demo grids') p.add_argument('--save-every', type=int, default=10000, help='save every this many steps') p.add_argument('--seed', type=int, help='the random seed') p.add_argument('--start-method', type=str, default='spawn', choices=['fork', 'forkserver', 'spawn'], help='the multiprocessing start method') p.add_argument('--wandb-entity', type=str, help='the wandb entity name') p.add_argument('--wandb-group', type=str, help='the wandb group name') p.add_argument('--wandb-project', type=str, help='the wandb project name (specify this to enable wandb)') p.add_argument('--wandb-save-model', action='store_true', help='save model to wandb') args = p.parse_args() mp.set_start_method(args.start_method) torch.backends.cuda.matmul.allow_tf32 = True try: torch._dynamo.config.automatic_dynamic_shapes = False except AttributeError: pass config = K.config.load_config(args.config) model_config = config['model'] dataset_config = config['dataset'] opt_config = config['optimizer'] sched_config = config['lr_sched'] ema_sched_config = config['ema_sched'] # TODO: allow non-square input sizes assert len(model_config['input_size']) == 2 and model_config['input_size'][0] == model_config['input_size'][1] size = model_config['input_size'] accelerator = accelerate.Accelerator(gradient_accumulation_steps=args.grad_accum_steps, mixed_precision=args.mixed_precision) ensure_distributed() device = accelerator.device unwrap = accelerator.unwrap_model print(f'Process {accelerator.process_index} using device: {device}', flush=True) accelerator.wait_for_everyone() if accelerator.is_main_process: print(f'World size: {accelerator.num_processes}', flush=True) print(f'Batch size: {args.batch_size * accelerator.num_processes}', flush=True) if args.seed is not None: seeds = torch.randint(-2 ** 63, 2 ** 63 - 1, [accelerator.num_processes], generator=torch.Generator().manual_seed(args.seed)) torch.manual_seed(seeds[accelerator.process_index]) demo_gen = torch.Generator().manual_seed(torch.randint(-2 ** 63, 2 ** 63 - 1, ()).item()) elapsed = 0.0 inner_model = K.config.make_model(config) inner_model_ema = deepcopy(inner_model) if args.compile: inner_model.compile() # inner_model_ema.compile() if accelerator.is_main_process: print(f'Parameters: {K.utils.n_params(inner_model):,}') # If logging to wandb, initialize the run use_wandb = accelerator.is_main_process and args.wandb_project if use_wandb: import wandb log_config = vars(args) log_config['config'] = config log_config['parameters'] = K.utils.n_params(inner_model) wandb.init(project=args.wandb_project, entity=args.wandb_entity, group=args.wandb_group, config=log_config, save_code=True) lr = opt_config['lr'] if args.lr is None else args.lr groups = inner_model.param_groups(lr) if opt_config['type'] == 'adamw': opt = optim.AdamW(groups, lr=lr, betas=tuple(opt_config['betas']), eps=opt_config['eps'], weight_decay=opt_config['weight_decay']) elif opt_config['type'] == 'adam8bit': import bitsandbytes as bnb opt = bnb.optim.Adam8bit(groups, lr=lr, betas=tuple(opt_config['betas']), eps=opt_config['eps'], weight_decay=opt_config['weight_decay']) elif opt_config['type'] == 'sgd': opt = optim.SGD(groups, lr=lr, momentum=opt_config.get('momentum', 0.), nesterov=opt_config.get('nesterov', False), weight_decay=opt_config.get('weight_decay', 0.)) else: raise ValueError('Invalid optimizer type') if sched_config['type'] == 'inverse': sched = K.utils.InverseLR(opt, inv_gamma=sched_config['inv_gamma'], power=sched_config['power'], warmup=sched_config['warmup']) elif sched_config['type'] == 'exponential': sched = K.utils.ExponentialLR(opt, num_steps=sched_config['num_steps'], decay=sched_config['decay'], warmup=sched_config['warmup']) elif sched_config['type'] == 'constant': sched = K.utils.ConstantLRWithWarmup(opt, warmup=sched_config['warmup']) else: raise ValueError('Invalid schedule type') assert ema_sched_config['type'] == 'inverse' ema_sched = K.utils.EMAWarmup(power=ema_sched_config['power'], max_value=ema_sched_config['max_value']) ema_stats = {} tf = transforms.Compose([ transforms.Resize(size[0], interpolation=transforms.InterpolationMode.BICUBIC), transforms.CenterCrop(size[0]), K.augmentation.KarrasAugmentationPipeline(model_config['augment_prob'], disable_all=model_config['augment_prob'] == 0), ]) if dataset_config['type'] == 'imagefolder': train_set = K.utils.FolderOfImages(dataset_config['location'], transform=tf) elif dataset_config['type'] == 'imagefolder-class': train_set = datasets.ImageFolder(dataset_config['location'], transform=tf) elif dataset_config['type'] == 'cifar10': train_set = datasets.CIFAR10(dataset_config['location'], train=True, download=True, transform=tf) elif dataset_config['type'] == 'mnist': train_set = datasets.MNIST(dataset_config['location'], train=True, download=True, transform=tf) elif dataset_config['type'] == 'huggingface': from datasets import load_dataset train_set = load_dataset(dataset_config['location']) train_set.set_transform(partial(K.utils.hf_datasets_augs_helper, transform=tf, image_key=dataset_config['image_key'])) train_set = train_set['train'] elif dataset_config['type'] == 'custom': location = (Path(args.config).parent / dataset_config['location']).resolve() spec = importlib.util.spec_from_file_location('custom_dataset', location) module = importlib.util.module_from_spec(spec) spec.loader.exec_module(module) get_dataset = getattr(module, dataset_config.get('get_dataset', 'get_dataset')) custom_dataset_config = dataset_config.get('config', {}) train_set = get_dataset(custom_dataset_config, transform=tf) else: raise ValueError('Invalid dataset type') if accelerator.is_main_process: try: print(f'Number of items in dataset: {len(train_set):,}') except TypeError: pass image_key = dataset_config.get('image_key', 0) num_classes = dataset_config.get('num_classes', 0) cond_dropout_rate = dataset_config.get('cond_dropout_rate', 0.1) class_key = dataset_config.get('class_key', 1) train_dl = data.DataLoader(train_set, args.batch_size, shuffle=True, drop_last=True, num_workers=args.num_workers, persistent_workers=True, pin_memory=True) inner_model, inner_model_ema, opt, train_dl = accelerator.prepare(inner_model, inner_model_ema, opt, train_dl) with torch.no_grad(), K.models.flops.flop_counter() as fc: x = torch.zeros([1, model_config['input_channels'], size[0], size[1]], device=device) sigma = torch.ones([1], device=device) extra_args = {} if getattr(unwrap(inner_model), "num_classes", 0): extra_args['class_cond'] = torch.zeros([1], dtype=torch.long, device=device) inner_model(x, sigma, **extra_args) if accelerator.is_main_process: print(f"Forward pass GFLOPs: {fc.flops / 1_000_000_000:,.3f}", flush=True) if use_wandb: wandb.watch(inner_model) if accelerator.num_processes == 1: args.gns = False if args.gns: gns_stats_hook = K.gns.DDPGradientStatsHook(inner_model) gns_stats = K.gns.GradientNoiseScale() else: gns_stats = None sigma_min = model_config['sigma_min'] sigma_max = model_config['sigma_max'] sample_density = K.config.make_sample_density(model_config) model = K.config.make_denoiser_wrapper(config)(inner_model) model_ema = K.config.make_denoiser_wrapper(config)(inner_model_ema) state_path = Path(f'{args.name}_state.json') if state_path.exists() or args.resume: if args.resume: ckpt_path = args.resume if not args.resume: state = json.load(open(state_path)) ckpt_path = state['latest_checkpoint'] if accelerator.is_main_process: print(f'Resuming from {ckpt_path}...') ckpt = torch.load(ckpt_path, map_location='cpu') unwrap(model.inner_model).load_state_dict(ckpt['model']) unwrap(model_ema.inner_model).load_state_dict(ckpt['model_ema']) opt.load_state_dict(ckpt['opt']) sched.load_state_dict(ckpt['sched']) ema_sched.load_state_dict(ckpt['ema_sched']) ema_stats = ckpt.get('ema_stats', ema_stats) epoch = ckpt['epoch'] + 1 step = ckpt['step'] + 1 if args.gns and ckpt.get('gns_stats', None) is not None: gns_stats.load_state_dict(ckpt['gns_stats']) demo_gen.set_state(ckpt['demo_gen']) elapsed = ckpt.get('elapsed', 0.0) del ckpt else: epoch = 0 step = 0 if args.reset_ema: unwrap(model.inner_model).load_state_dict(unwrap(model_ema.inner_model).state_dict()) ema_sched = K.utils.EMAWarmup(power=ema_sched_config['power'], max_value=ema_sched_config['max_value']) ema_stats = {} if args.resume_inference: if accelerator.is_main_process: print(f'Loading {args.resume_inference}...') ckpt = safetorch.load_file(args.resume_inference) unwrap(model.inner_model).load_state_dict(ckpt) unwrap(model_ema.inner_model).load_state_dict(ckpt) del ckpt evaluate_enabled = args.evaluate_every > 0 and args.evaluate_n > 0 metrics_log = None if evaluate_enabled: if args.evaluate_with == 'inception': extractor = K.evaluation.InceptionV3FeatureExtractor(device=device) elif args.evaluate_with == 'clip': extractor = K.evaluation.CLIPFeatureExtractor(args.clip_model, device=device) elif args.evaluate_with == 'dinov2': extractor = K.evaluation.DINOv2FeatureExtractor(args.dinov2_model, device=device) else: raise ValueError('Invalid evaluation feature extractor') train_iter = iter(train_dl) if accelerator.is_main_process: print('Computing features for reals...') reals_features = K.evaluation.compute_features(accelerator, lambda x: next(train_iter)[image_key][1], extractor, args.evaluate_n, args.batch_size) if accelerator.is_main_process and not args.evaluate_only: metrics_log = K.utils.CSVLogger(f'{args.name}_metrics.csv', ['step', 'time', 'loss', 'fid', 'kid']) del train_iter cfg_scale = 1. def make_cfg_model_fn(model): def cfg_model_fn(x, sigma, class_cond): x_in = torch.cat([x, x]) sigma_in = torch.cat([sigma, sigma]) class_uncond = torch.full_like(class_cond, num_classes) class_cond_in = torch.cat([class_uncond, class_cond]) out = model(x_in, sigma_in, class_cond=class_cond_in) out_uncond, out_cond = out.chunk(2) return out_uncond + (out_cond - out_uncond) * cfg_scale if cfg_scale != 1: return cfg_model_fn return model @torch.no_grad() @K.utils.eval_mode(model_ema) def demo(): if accelerator.is_main_process: tqdm.write('Sampling...') filename = f'{args.name}_demo_{step:08}.png' n_per_proc = math.ceil(args.sample_n / accelerator.num_processes) x = torch.randn([accelerator.num_processes, n_per_proc, model_config['input_channels'], size[0], size[1]], generator=demo_gen).to(device) dist.broadcast(x, 0) x = x[accelerator.process_index] * sigma_max model_fn, extra_args = model_ema, {} if num_classes: class_cond = torch.randint(0, num_classes, [accelerator.num_processes, n_per_proc], generator=demo_gen).to(device) dist.broadcast(class_cond, 0) extra_args['class_cond'] = class_cond[accelerator.process_index] model_fn = make_cfg_model_fn(model_ema) sigmas = K.sampling.get_sigmas_karras(50, sigma_min, sigma_max, rho=7., device=device) x_0 = K.sampling.sample_dpmpp_2m_sde(model_fn, x, sigmas, extra_args=extra_args, eta=0.0, solver_type='heun', disable=not accelerator.is_main_process) x_0 = accelerator.gather(x_0)[:args.sample_n] if accelerator.is_main_process: grid = utils.make_grid(x_0, nrow=math.ceil(args.sample_n ** 0.5), padding=0) K.utils.to_pil_image(grid).save(filename) if use_wandb: wandb.log({'demo_grid': wandb.Image(filename)}, step=step) @torch.no_grad() @K.utils.eval_mode(model_ema) def evaluate(): if not evaluate_enabled: return if accelerator.is_main_process: tqdm.write('Evaluating...') sigmas = K.sampling.get_sigmas_karras(50, sigma_min, sigma_max, rho=7., device=device) def sample_fn(n): x = torch.randn([n, model_config['input_channels'], size[0], size[1]], device=device) * sigma_max model_fn, extra_args = model_ema, {} if num_classes: extra_args['class_cond'] = torch.randint(0, num_classes, [n], device=device) model_fn = make_cfg_model_fn(model_ema) x_0 = K.sampling.sample_dpmpp_2m_sde(model_fn, x, sigmas, extra_args=extra_args, eta=0.0, solver_type='heun', disable=True) return x_0 fakes_features = K.evaluation.compute_features(accelerator, sample_fn, extractor, args.evaluate_n, args.batch_size) if accelerator.is_main_process: fid = K.evaluation.fid(fakes_features, reals_features) kid = K.evaluation.kid(fakes_features, reals_features) print(f'FID: {fid.item():g}, KID: {kid.item():g}') if accelerator.is_main_process and metrics_log is not None: metrics_log.write(step, elapsed, ema_stats['loss'], fid.item(), kid.item()) if use_wandb: wandb.log({'FID': fid.item(), 'KID': kid.item()}, step=step) def save(): accelerator.wait_for_everyone() filename = f'{args.name}_{step:08}.pth' if accelerator.is_main_process: tqdm.write(f'Saving to {filename}...') inner_model = unwrap(model.inner_model) inner_model_ema = unwrap(model_ema.inner_model) obj = { 'config': config, 'model': inner_model.state_dict(), 'model_ema': inner_model_ema.state_dict(), 'opt': opt.state_dict(), 'sched': sched.state_dict(), 'ema_sched': ema_sched.state_dict(), 'epoch': epoch, 'step': step, 'gns_stats': gns_stats.state_dict() if gns_stats is not None else None, 'ema_stats': ema_stats, 'demo_gen': demo_gen.get_state(), 'elapsed': elapsed, } accelerator.save(obj, filename) if accelerator.is_main_process: state_obj = {'latest_checkpoint': filename} json.dump(state_obj, open(state_path, 'w')) if args.wandb_save_model and use_wandb: wandb.save(filename) if args.evaluate_only: if not evaluate_enabled: raise ValueError('--evaluate-only requested but evaluation is disabled') evaluate() return losses_since_last_print = [] try: while True: for batch in tqdm(train_dl, smoothing=0.1, disable=not accelerator.is_main_process): if device.type == 'cuda': start_timer = torch.cuda.Event(enable_timing=True) end_timer = torch.cuda.Event(enable_timing=True) torch.cuda.synchronize() start_timer.record() else: start_timer = time.time() with accelerator.accumulate(model): reals, _, aug_cond = batch[image_key] class_cond, extra_args = None, {} if num_classes: class_cond = batch[class_key] drop = torch.rand(class_cond.shape, device=class_cond.device) class_cond.masked_fill_(drop < cond_dropout_rate, num_classes) extra_args['class_cond'] = class_cond noise = torch.randn_like(reals) with K.utils.enable_stratified_accelerate(accelerator, disable=args.gns): sigma = sample_density([reals.shape[0]], device=device) with K.models.checkpointing(args.checkpointing): losses = model.loss(reals, noise, sigma, aug_cond=aug_cond, **extra_args) loss = accelerator.gather(losses).mean().item() losses_since_last_print.append(loss) accelerator.backward(losses.mean()) if args.gns: sq_norm_small_batch, sq_norm_large_batch = gns_stats_hook.get_stats() gns_stats.update(sq_norm_small_batch, sq_norm_large_batch, reals.shape[0], reals.shape[0] * accelerator.num_processes) if accelerator.sync_gradients: accelerator.clip_grad_norm_(model.parameters(), 1.) opt.step() sched.step() opt.zero_grad() ema_decay = ema_sched.get_value() K.utils.ema_update_dict(ema_stats, {'loss': loss}, ema_decay ** (1 / args.grad_accum_steps)) if accelerator.sync_gradients: K.utils.ema_update(model, model_ema, ema_decay) ema_sched.step() if device.type == 'cuda': end_timer.record() torch.cuda.synchronize() elapsed += start_timer.elapsed_time(end_timer) / 1000 else: elapsed += time.time() - start_timer if step % 25 == 0: loss_disp = sum(losses_since_last_print) / len(losses_since_last_print) losses_since_last_print.clear() avg_loss = ema_stats['loss'] if accelerator.is_main_process: if args.gns: tqdm.write(f'Epoch: {epoch}, step: {step}, loss: {loss_disp:g}, avg loss: {avg_loss:g}, gns: {gns_stats.get_gns():g}') else: tqdm.write(f'Epoch: {epoch}, step: {step}, loss: {loss_disp:g}, avg loss: {avg_loss:g}') if use_wandb: log_dict = { 'epoch': epoch, 'loss': loss, 'lr': sched.get_last_lr()[0], 'ema_decay': ema_decay, } if args.gns: log_dict['gradient_noise_scale'] = gns_stats.get_gns() wandb.log(log_dict, step=step) step += 1 if step % args.demo_every == 0: demo() if evaluate_enabled and step > 0 and step % args.evaluate_every == 0: evaluate() if step == args.end_step or (step > 0 and step % args.save_every == 0): save() if step == args.end_step: if accelerator.is_main_process: tqdm.write('Done!') return epoch += 1 except KeyboardInterrupt: pass if __name__ == '__main__': main()