import uuid import argparse, os, sys, datetime, glob, importlib, csv import numpy as np import time import torch import torchvision import pytorch_lightning as pl from packaging import version from omegaconf import OmegaConf from torch.utils.data import random_split, DataLoader, Dataset, Subset from functools import partial from PIL import Image from pytorch_lightning import seed_everything from pytorch_lightning.trainer import Trainer from pytorch_lightning.callbacks import ModelCheckpoint, Callback, LearningRateMonitor from pytorch_lightning.utilities.distributed import rank_zero_only from pytorch_lightning.utilities import rank_zero_info from ldm.data.base import Txt2ImgIterableBaseDataset from ldm.util import instantiate_from_config def get_parser(**parser_kwargs): def str2bool(v): if isinstance(v, bool): return v if v.lower() in ("yes", "true", "t", "y", "1"): return True elif v.lower() in ("no", "false", "f", "n", "0"): return False else: raise argparse.ArgumentTypeError("Boolean value expected.") parser = argparse.ArgumentParser(**parser_kwargs) parser.add_argument( "-n", "--name", type=str, const=True, default="", nargs="?", help="postfix for logdir", ) parser.add_argument( "-r", "--resume", type=str, const=True, default="", nargs="?", help="resume from logdir or checkpoint in logdir", ) parser.add_argument( "-b", "--base", nargs="*", metavar="base_config.yaml", help="paths to base configs. Loaded from left-to-right. " "Parameters can be overwritten or added with command-line options of the form `--key value`.", default=list(), ) parser.add_argument( "-t", "--train", type=str2bool, const=True, default=False, nargs="?", help="train", ) parser.add_argument( "--no-test", type=str2bool, const=True, default=False, nargs="?", help="disable test", ) parser.add_argument( "-p", "--project", help="name of new or path to existing project" ) parser.add_argument( "-d", "--debug", type=str2bool, nargs="?", const=True, default=False, help="enable post-mortem debugging", ) parser.add_argument( "-s", "--seed", type=int, default=23, help="seed for seed_everything", ) parser.add_argument( "-f", "--postfix", type=str, default="", help="post-postfix for default name", ) parser.add_argument( "-l", "--logdir", type=str, default="logs", help="directory for logging dat shit", ) parser.add_argument( "--scale_lr", type=str2bool, nargs="?", const=True, default=True, help="scale base-lr by ngpu * batch_size * n_accumulate", ) return parser def nondefault_trainer_args(opt): parser = argparse.ArgumentParser() parser = Trainer.add_argparse_args(parser) args = parser.parse_args([]) return sorted(k for k in vars(args) if getattr(opt, k) != getattr(args, k)) class WrappedDataset(Dataset): """Wraps an arbitrary object with __len__ and __getitem__ into a pytorch dataset""" def __init__(self, dataset): self.data = dataset def __len__(self): return len(self.data) def __getitem__(self, idx): return self.data[idx] def worker_init_fn(_): worker_info = torch.utils.data.get_worker_info() dataset = worker_info.dataset worker_id = worker_info.id if isinstance(dataset, Txt2ImgIterableBaseDataset): split_size = dataset.num_records // worker_info.num_workers # reset num_records to the true number to retain reliable length information dataset.sample_ids = dataset.valid_ids[worker_id * split_size:(worker_id + 1) * split_size] current_id = np.random.choice(len(np.random.get_state()[1]), 1) return np.random.seed(np.random.get_state()[1][current_id] + worker_id) else: return np.random.seed(np.random.get_state()[1][0] + worker_id) class DataModuleFromConfig(pl.LightningDataModule): def __init__(self, batch_size, train=None, validation=None, test=None, predict=None, wrap=False, num_workers=None, shuffle_test_loader=False, use_worker_init_fn=False, shuffle_val_dataloader=False): super().__init__() self.batch_size = batch_size self.dataset_configs = dict() self.num_workers = num_workers if num_workers is not None else batch_size * 2 self.use_worker_init_fn = use_worker_init_fn if train is not None: self.dataset_configs["train"] = train self.train_dataloader = self._train_dataloader if validation is not None: self.dataset_configs["validation"] = validation self.val_dataloader = partial(self._val_dataloader, shuffle=shuffle_val_dataloader) if test is not None: self.dataset_configs["test"] = test self.test_dataloader = partial(self._test_dataloader, shuffle=shuffle_test_loader) if predict is not None: self.dataset_configs["predict"] = predict self.predict_dataloader = self._predict_dataloader self.wrap = wrap def prepare_data(self): for data_cfg in self.dataset_configs.values(): instantiate_from_config(data_cfg) def setup(self, stage=None): self.datasets = dict( (k, instantiate_from_config(self.dataset_configs[k])) for k in self.dataset_configs) if self.wrap: for k in self.datasets: self.datasets[k] = WrappedDataset(self.datasets[k]) def _train_dataloader(self): is_iterable_dataset = isinstance(self.datasets['train'], Txt2ImgIterableBaseDataset) if is_iterable_dataset or self.use_worker_init_fn: init_fn = worker_init_fn else: init_fn = None return DataLoader(self.datasets["train"], batch_size=self.batch_size, num_workers=self.num_workers, shuffle=False if is_iterable_dataset else True, worker_init_fn=init_fn) def _val_dataloader(self, shuffle=False): if isinstance(self.datasets['validation'], Txt2ImgIterableBaseDataset) or self.use_worker_init_fn: init_fn = worker_init_fn else: init_fn = None return DataLoader(self.datasets["validation"], batch_size=self.batch_size, num_workers=self.num_workers, worker_init_fn=init_fn, shuffle=shuffle) def _test_dataloader(self, shuffle=False): is_iterable_dataset = isinstance(self.datasets['train'], Txt2ImgIterableBaseDataset) if is_iterable_dataset or self.use_worker_init_fn: init_fn = worker_init_fn else: init_fn = None # do not shuffle dataloader for iterable dataset shuffle = shuffle and (not is_iterable_dataset) return DataLoader(self.datasets["test"], batch_size=self.batch_size, num_workers=self.num_workers, worker_init_fn=init_fn, shuffle=shuffle) def _predict_dataloader(self, shuffle=False): if isinstance(self.datasets['predict'], Txt2ImgIterableBaseDataset) or self.use_worker_init_fn: init_fn = worker_init_fn else: init_fn = None return DataLoader(self.datasets["predict"], batch_size=self.batch_size, num_workers=self.num_workers, worker_init_fn=init_fn) class SetupCallback(Callback): def __init__(self, resume, now, logdir, ckptdir, cfgdir, config, lightning_config): super().__init__() self.resume = resume self.now = now self.logdir = logdir self.ckptdir = ckptdir self.cfgdir = cfgdir self.config = config self.lightning_config = lightning_config def on_keyboard_interrupt(self, trainer, pl_module): if trainer.global_rank == 0: print("Summoning checkpoint.") ckpt_path = os.path.join(self.ckptdir, "last.ckpt") trainer.save_checkpoint(ckpt_path) def on_pretrain_routine_start(self, trainer, pl_module): if trainer.global_rank == 0: # Create logdirs and save configs os.makedirs(self.logdir, exist_ok=True) os.makedirs(self.ckptdir, exist_ok=True) os.makedirs(self.cfgdir, exist_ok=True) if "callbacks" in self.lightning_config: if 'metrics_over_trainsteps_checkpoint' in self.lightning_config['callbacks']: os.makedirs(os.path.join(self.ckptdir, 'trainstep_checkpoints'), exist_ok=True) print("Project config") print(OmegaConf.to_yaml(self.config)) OmegaConf.save(self.config, os.path.join(self.cfgdir, "{}-project.yaml".format(self.now))) print("Lightning config") print(OmegaConf.to_yaml(self.lightning_config)) OmegaConf.save(OmegaConf.create({"lightning": self.lightning_config}), os.path.join(self.cfgdir, "{}-lightning.yaml".format(self.now))) else: # ModelCheckpoint callback created log directory --- remove it if not self.resume and os.path.exists(self.logdir): dst, name = os.path.split(self.logdir) dst = os.path.join(dst, "child_runs", name) os.makedirs(os.path.split(dst)[0], exist_ok=True) try: os.rename(self.logdir, dst) except FileNotFoundError: pass class ImageLogger(Callback): def __init__(self, batch_frequency, max_images, clamp=True, increase_log_steps=True, rescale=True, disabled=False, log_on_batch_idx=False, log_first_step=False, log_images_kwargs=None): super().__init__() self.rescale = rescale self.batch_freq = batch_frequency self.max_images = max_images self.logger_log_images = { pl.loggers.WandbLogger: self._testtube, } self.log_steps = [2 ** n for n in range(int(np.log2(self.batch_freq)) + 1)] if not increase_log_steps: self.log_steps = [self.batch_freq] self.clamp = clamp self.disabled = disabled self.log_on_batch_idx = log_on_batch_idx self.log_images_kwargs = log_images_kwargs if log_images_kwargs else {} self.log_first_step = log_first_step @rank_zero_only def _testtube(self, pl_module, images, batch_idx, split): for k in images: grid = torchvision.utils.make_grid(images[k]) grid = (grid + 1.0) / 2.0 # -1,1 -> 0,1; c,h,w tag = f"{split}/{k}" pl_module.logger.experiment.log( {'tag': tag, 'examples': grid}, step=pl_module.global_step ) @rank_zero_only def log_local(self, save_dir, split, images, global_step, current_epoch, batch_idx): root = os.path.join(save_dir, "images", split) for k in images: grid = torchvision.utils.make_grid(images[k], nrow=4) if self.rescale: grid = (grid + 1.0) / 2.0 # -1,1 -> 0,1; c,h,w grid = grid.transpose(0, 1).transpose(1, 2).squeeze(-1) grid = grid.numpy() grid = (grid * 255).astype(np.uint8) filename = "{}_gs-{:06}_e-{:06}_b-{:06}.png".format( k, global_step, current_epoch, batch_idx) path = os.path.join(root, filename) os.makedirs(os.path.split(path)[0], exist_ok=True) Image.fromarray(grid).save(path) def log_img(self, pl_module, batch, batch_idx, split="train"): check_idx = batch_idx if self.log_on_batch_idx else pl_module.global_step if (self.check_frequency(check_idx) and # batch_idx % self.batch_freq == 0 hasattr(pl_module, "log_images") and callable(pl_module.log_images) and self.max_images > 0): logger = type(pl_module.logger) is_train = pl_module.training if is_train: pl_module.eval() with torch.no_grad(): with torch.autocast('cuda'): images = pl_module.log_images(batch, split=split, **self.log_images_kwargs) for k in images: N = min(images[k].shape[0], self.max_images) images[k] = images[k][:N] if isinstance(images[k], torch.Tensor): images[k] = images[k].detach().cpu().to(torch.float32) if self.clamp: images[k] = torch.clamp(images[k], -1., 1.) self.log_local(pl_module.logger.save_dir, split, images, pl_module.global_step, pl_module.current_epoch, batch_idx) logger_log_images = self.logger_log_images.get(logger, lambda *args, **kwargs: None) logger_log_images(pl_module, images, pl_module.global_step, split) if is_train: pl_module.train() def check_frequency(self, check_idx): if ((check_idx % self.batch_freq) == 0 or (check_idx in self.log_steps)) and ( check_idx > 0 or self.log_first_step): try: self.log_steps.pop(0) except IndexError as e: print(e) pass return True return False def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx): if not self.disabled and (pl_module.global_step > 0 or self.log_first_step): self.log_img(pl_module, batch, batch_idx, split="train") def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx): if not self.disabled and pl_module.global_step > 0: self.log_img(pl_module, batch, batch_idx, split="val") if hasattr(pl_module, 'calibrate_grad_norm'): if (pl_module.calibrate_grad_norm and batch_idx % 25 == 0) and batch_idx > 0: self.log_gradients(trainer, pl_module, batch_idx=batch_idx) class CUDACallback(Callback): # see https://github.com/SeanNaren/minGPT/blob/master/mingpt/callback.py def on_train_epoch_start(self, trainer, pl_module): # Reset the memory use counter torch.cuda.reset_peak_memory_stats(trainer.root_gpu) torch.cuda.synchronize(trainer.root_gpu) self.start_time = time.time() def on_train_epoch_end(self, trainer, pl_module): torch.cuda.synchronize(trainer.root_gpu) max_memory = torch.cuda.max_memory_allocated(trainer.root_gpu) / 2 ** 20 epoch_time = time.time() - self.start_time try: max_memory = trainer.training_type_plugin.reduce(max_memory) epoch_time = trainer.training_type_plugin.reduce(epoch_time) rank_zero_info(f"Average Epoch time: {epoch_time:.2f} seconds") rank_zero_info(f"Average Peak memory {max_memory:.2f}MiB") except AttributeError: pass if __name__ == "__main__": # custom parser to specify config files, train, test and debug mode, # postfix, resume. # `--key value` arguments are interpreted as arguments to the trainer. # `nested.key=value` arguments are interpreted as config parameters. # configs are merged from left-to-right followed by command line parameters. # model: # base_learning_rate: float # target: path to lightning module # params: # key: value # data: # target: main.DataModuleFromConfig # params: # batch_size: int # wrap: bool # train: # target: path to train dataset # params: # key: value # validation: # target: path to validation dataset # params: # key: value # test: # target: path to test dataset # params: # key: value # lightning: (optional, has sane defaults and can be specified on cmdline) # trainer: # additional arguments to trainer # logger: # logger to instantiate # modelcheckpoint: # modelcheckpoint to instantiate # callbacks: # callback1: # target: importpath # params: # key: value now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S") # add cwd for convenience and to make classes in this file available when # running as `python main.py` # (in particular `main.DataModuleFromConfig`) sys.path.append(os.getcwd()) parser = get_parser() parser = Trainer.add_argparse_args(parser) opt, unknown = parser.parse_known_args() if opt.name and opt.resume: raise ValueError( "-n/--name and -r/--resume cannot be specified both." "If you want to resume training in a new log folder, " "use -n/--name in combination with --resume_from_checkpoint" ) if opt.resume: if not os.path.exists(opt.resume): raise ValueError("Cannot find {}".format(opt.resume)) if os.path.isfile(opt.resume): paths = opt.resume.split("/") # idx = len(paths)-paths[::-1].index("logs")+1 # logdir = "/".join(paths[:idx]) logdir = "/".join(paths[:-2]) ckpt = opt.resume else: assert os.path.isdir(opt.resume), opt.resume logdir = opt.resume.rstrip("/") ckpt = os.path.join(logdir, "checkpoints", "last.ckpt") opt.resume_from_checkpoint = ckpt base_configs = sorted(glob.glob(os.path.join(logdir, "configs/*.yaml"))) opt.base = base_configs + opt.base _tmp = logdir.split("/") nowname = _tmp[-1] else: if opt.name: name = "_" + opt.name elif opt.base: cfg_fname = os.path.split(opt.base[0])[-1] cfg_name = os.path.splitext(cfg_fname)[0] name = "_" + cfg_name else: name = "" nowname = now + name + opt.postfix logdir = os.path.join(opt.logdir, nowname) ckptdir = os.path.join(logdir, "checkpoints") cfgdir = os.path.join(logdir, "configs") seed_everything(opt.seed) try: # init and save configs configs = [OmegaConf.load(cfg) for cfg in opt.base] cli = OmegaConf.from_dotlist(unknown) config = OmegaConf.merge(*configs, cli) lightning_config = config.pop("lightning", OmegaConf.create()) # merge trainer cli with config trainer_config = lightning_config.get("trainer", OmegaConf.create()) # default to ddp trainer_config["accelerator"] = "gpu" for k in nondefault_trainer_args(opt): trainer_config[k] = getattr(opt, k) if not "gpus" in trainer_config: del trainer_config["accelerator"] cpu = True else: gpuinfo = trainer_config["gpus"] print(f"Running on GPUs {gpuinfo}") cpu = False trainer_opt = argparse.Namespace(**trainer_config) lightning_config.trainer = trainer_config # model model = instantiate_from_config(config.model) # trainer and callbacks trainer_kwargs = dict() # default logger configs default_logger_cfgs = { "wandb": { "target": "pytorch_lightning.loggers.WandbLogger", "params": { "name": nowname, "save_dir": logdir, "offline": opt.debug, "id": str(uuid.uuid1()), } }, "testtube": { "target": "pytorch_lightning.loggers.TestTubeLogger", "params": { "name": "testtube", "save_dir": logdir, } }, } default_logger_cfg = default_logger_cfgs["wandb"] if "logger" in lightning_config: logger_cfg = lightning_config.logger else: logger_cfg = OmegaConf.create() logger_cfg = OmegaConf.merge(default_logger_cfg, logger_cfg) trainer_kwargs["logger"] = instantiate_from_config(logger_cfg) # modelcheckpoint - use TrainResult/EvalResult(checkpoint_on=metric) to # specify which metric is used to determine best models default_modelckpt_cfg = { "target": "pytorch_lightning.callbacks.ModelCheckpoint", "params": { "dirpath": ckptdir, "filename": "{epoch:06}", "verbose": True, "save_last": True, } } if hasattr(model, "monitor"): print(f"Monitoring {model.monitor} as checkpoint metric.") default_modelckpt_cfg["params"]["monitor"] = model.monitor default_modelckpt_cfg["params"]["save_top_k"] = 3 if "modelcheckpoint" in lightning_config: modelckpt_cfg = lightning_config.modelcheckpoint else: modelckpt_cfg = OmegaConf.create() modelckpt_cfg = OmegaConf.merge(default_modelckpt_cfg, modelckpt_cfg) print(f"Merged modelckpt-cfg: \n{modelckpt_cfg}") if version.parse(pl.__version__) < version.parse('1.4.0'): trainer_kwargs["checkpoint_callback"] = instantiate_from_config(modelckpt_cfg) # add callback which sets up log directory default_callbacks_cfg = { "setup_callback": { "target": "main.SetupCallback", "params": { "resume": opt.resume, "now": now, "logdir": logdir, "ckptdir": ckptdir, "cfgdir": cfgdir, "config": config, "lightning_config": lightning_config, } }, "image_logger": { "target": "main.ImageLogger", "params": { "batch_frequency": 750, "max_images": 4, "clamp": True } }, "learning_rate_logger": { "target": "main.LearningRateMonitor", "params": { "logging_interval": "step", # "log_momentum": True } }, "cuda_callback": { "target": "main.CUDACallback" }, } if version.parse(pl.__version__) >= version.parse('1.4.0'): default_callbacks_cfg.update({'checkpoint_callback': modelckpt_cfg}) if "callbacks" in lightning_config: callbacks_cfg = lightning_config.callbacks else: callbacks_cfg = OmegaConf.create() if 'metrics_over_trainsteps_checkpoint' in callbacks_cfg: print( 'Caution: Saving checkpoints every n train steps without deleting. This might require some free space.') default_metrics_over_trainsteps_ckpt_dict = { 'metrics_over_trainsteps_checkpoint': {"target": 'pytorch_lightning.callbacks.ModelCheckpoint', 'params': { "dirpath": os.path.join(ckptdir, 'trainstep_checkpoints'), "filename": "{epoch:06}-{step:09}", "verbose": True, 'save_top_k': -1, 'every_n_train_steps': 10000, 'save_weights_only': True } } } default_callbacks_cfg.update(default_metrics_over_trainsteps_ckpt_dict) callbacks_cfg = OmegaConf.merge(default_callbacks_cfg, callbacks_cfg) if 'ignore_keys_callback' in callbacks_cfg and hasattr(trainer_opt, 'resume_from_checkpoint'): callbacks_cfg.ignore_keys_callback.params['ckpt_path'] = trainer_opt.resume_from_checkpoint elif 'ignore_keys_callback' in callbacks_cfg: del callbacks_cfg['ignore_keys_callback'] trainer_kwargs["callbacks"] = [instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg] trainer_kwargs["plugins"] = list() from pytorch_lightning.plugins import DDPPlugin, NativeMixedPrecisionPlugin #trainer_kwargs["plugins"].append(DDPPlugin(find_unused_parameters=False)) trainer_kwargs["plugins"].append(NativeMixedPrecisionPlugin(16, 'cuda', torch.cuda.amp.GradScaler(enabled=True))) trainer = Trainer.from_argparse_args(trainer_opt, **trainer_kwargs) #trainer = Trainer(gpus=1, precision=16, amp_backend="native", strategy="deepspeed_stage_2_offload", benchmark=True, limit_val_batches=0, num_sanity_val_steps=0, accumulate_grad_batches=1) trainer.logdir = logdir ### # data data = instantiate_from_config(config.data) # NOTE according to https://pytorch-lightning.readthedocs.io/en/latest/datamodules.html # calling these ourselves should not be necessary but it is. # lightning still takes care of proper multiprocessing though data.prepare_data() data.setup() print("#### Data #####") for k in data.datasets: print(f"{k}, {data.datasets[k].__class__.__name__}, {len(data.datasets[k])}") # configure learning rate bs, base_lr = config.data.params.batch_size, config.model.base_learning_rate if not cpu: ngpu = len(lightning_config.trainer.gpus.strip(",").split(',')) else: ngpu = 1 if 'accumulate_grad_batches' in lightning_config.trainer: accumulate_grad_batches = lightning_config.trainer.accumulate_grad_batches else: accumulate_grad_batches = 1 print(f"accumulate_grad_batches = {accumulate_grad_batches}") lightning_config.trainer.accumulate_grad_batches = accumulate_grad_batches if opt.scale_lr: model.learning_rate = accumulate_grad_batches * ngpu * bs * base_lr print( "Setting learning rate to {:.2e} = {} (accumulate_grad_batches) * {} (num_gpus) * {} (batchsize) * {:.2e} (base_lr)".format( model.learning_rate, accumulate_grad_batches, ngpu, bs, base_lr)) else: model.learning_rate = base_lr print("++++ NOT USING LR SCALING ++++") print(f"Setting learning rate to {model.learning_rate:.2e}") # allow checkpointing via USR1 def melk(*args, **kwargs): # run all checkpoint hooks if trainer.global_rank == 0: print("Summoning checkpoint.") ckpt_path = os.path.join(ckptdir, "last.ckpt") trainer.save_checkpoint(ckpt_path) def divein(*args, **kwargs): if trainer.global_rank == 0: import pudb; pudb.set_trace() import signal signal.signal(signal.SIGUSR1, melk) signal.signal(signal.SIGUSR2, divein) # run if opt.train: try: trainer.fit(model, data) except Exception: melk() raise if not opt.no_test and not trainer.interrupted: trainer.test(model, data) except Exception: if opt.debug and trainer.global_rank == 0: try: import pudb as debugger except ImportError: import pdb as debugger debugger.post_mortem() raise finally: # move newly created debug project to debug_runs if opt.debug and not opt.resume and trainer.global_rank == 0: dst, name = os.path.split(logdir) dst = os.path.join(dst, "debug_runs", name) os.makedirs(os.path.split(dst)[0], exist_ok=True) os.rename(logdir, dst) if trainer.global_rank == 0: print(trainer.profiler.summary())