"""Training code for the detector model""" import argparse import os import subprocess import sys from itertools import count from multiprocessing import Process import torch import torch.distributed as dist from torch import nn from torch.nn.parallel import DistributedDataParallel from torch.optim import Adam from torch.utils.data import DataLoader, DistributedSampler, RandomSampler from tqdm import tqdm from transformers import * from .dataset import Corpus, EncodedDataset from .download import download from .utils import summary, distributed def setup_distributed(port=29500): if not dist.is_available() or not torch.cuda.is_available() or torch.cuda.device_count() <= 1: return 0, 1 if 'MPIR_CVAR_CH3_INTERFACE_HOSTNAME' in os.environ: from mpi4py import MPI mpi_rank = MPI.COMM_WORLD.Get_rank() mpi_size = MPI.COMM_WORLD.Get_size() os.environ["MASTER_ADDR"] = '127.0.0.1' os.environ["MASTER_PORT"] = str(port) dist.init_process_group(backend="nccl", world_size=mpi_size, rank=mpi_rank) return mpi_rank, mpi_size dist.init_process_group(backend="nccl", init_method="env://") return dist.get_rank(), dist.get_world_size() def load_datasets(data_dir, real_dataset, fake_dataset, tokenizer, batch_size, max_sequence_length, random_sequence_length, epoch_size=None, token_dropout=None, seed=None): if fake_dataset == 'TWO': download(real_dataset, 'xl-1542M', 'xl-1542M-nucleus', data_dir=data_dir) elif fake_dataset == 'THREE': download(real_dataset, 'xl-1542M', 'xl-1542M-k40', 'xl-1542M-nucleus', data_dir=data_dir) else: download(real_dataset, fake_dataset, data_dir=data_dir) real_corpus = Corpus(real_dataset, data_dir=data_dir) if fake_dataset == "TWO": real_train, real_valid = real_corpus.train * 2, real_corpus.valid * 2 fake_corpora = [Corpus(name, data_dir=data_dir) for name in ['xl-1542M', 'xl-1542M-nucleus']] fake_train = sum([corpus.train for corpus in fake_corpora], []) fake_valid = sum([corpus.valid for corpus in fake_corpora], []) elif fake_dataset == "THREE": real_train, real_valid = real_corpus.train * 3, real_corpus.valid * 3 fake_corpora = [Corpus(name, data_dir=data_dir) for name in ['xl-1542M', 'xl-1542M-k40', 'xl-1542M-nucleus']] fake_train = sum([corpus.train for corpus in fake_corpora], []) fake_valid = sum([corpus.valid for corpus in fake_corpora], []) else: fake_corpus = Corpus(fake_dataset, data_dir=data_dir) real_train, real_valid = real_corpus.train, real_corpus.valid fake_train, fake_valid = fake_corpus.train, fake_corpus.valid Sampler = DistributedSampler if distributed() and dist.get_world_size() > 1 else RandomSampler min_sequence_length = 10 if random_sequence_length else None train_dataset = EncodedDataset(real_train, fake_train, tokenizer, max_sequence_length, min_sequence_length, epoch_size, token_dropout, seed) train_loader = DataLoader(train_dataset, batch_size, sampler=Sampler(train_dataset), num_workers=0) validation_dataset = EncodedDataset(real_valid, fake_valid, tokenizer) validation_loader = DataLoader(validation_dataset, batch_size=1, sampler=Sampler(validation_dataset)) return train_loader, validation_loader def accuracy_sum(logits, labels): if list(logits.shape) == list(labels.shape) + [2]: # 2-d outputs classification = (logits[..., 0] < logits[..., 1]).long().flatten() else: classification = (logits > 0).long().flatten() assert classification.shape == labels.shape return (classification == labels).float().sum().item() def train(model: nn.Module, optimizer, device: str, loader: DataLoader, desc='Train'): model.train() train_accuracy = 0 train_epoch_size = 0 train_loss = 0 with tqdm(loader, desc=desc, disable=distributed() and dist.get_rank() > 0) as loop: for texts, masks, labels in loop: texts, masks, labels = texts.to(device), masks.to(device), labels.to(device) batch_size = texts.shape[0] optimizer.zero_grad() loss, logits = model(texts, attention_mask=masks, labels=labels) loss.backward() optimizer.step() batch_accuracy = accuracy_sum(logits, labels) train_accuracy += batch_accuracy train_epoch_size += batch_size train_loss += loss.item() * batch_size loop.set_postfix(loss=loss.item(), acc=train_accuracy / train_epoch_size) return { "train/accuracy": train_accuracy, "train/epoch_size": train_epoch_size, "train/loss": train_loss } def validate(model: nn.Module, device: str, loader: DataLoader, votes=1, desc='Validation'): model.eval() validation_accuracy = 0 validation_epoch_size = 0 validation_loss = 0 records = [record for v in range(votes) for record in tqdm(loader, desc=f'Preloading data ... {v}', disable=dist.is_available() and dist.get_rank() > 0)] records = [[records[v * len(loader) + i] for v in range(votes)] for i in range(len(loader))] with tqdm(records, desc=desc, disable=distributed() and dist.get_rank() > 0) as loop, torch.no_grad(): for example in loop: losses = [] logit_votes = [] for texts, masks, labels in example: texts, masks, labels = texts.to(device), masks.to(device), labels.to(device) batch_size = texts.shape[0] loss, logits = model(texts, attention_mask=masks, labels=labels) losses.append(loss) logit_votes.append(logits) loss = torch.stack(losses).mean(dim=0) logits = torch.stack(logit_votes).mean(dim=0) batch_accuracy = accuracy_sum(logits, labels) validation_accuracy += batch_accuracy validation_epoch_size += batch_size validation_loss += loss.item() * batch_size loop.set_postfix(loss=loss.item(), acc=validation_accuracy / validation_epoch_size) return { "validation/accuracy": validation_accuracy, "validation/epoch_size": validation_epoch_size, "validation/loss": validation_loss } def _all_reduce_dict(d, device): # wrap in tensor and use reduce to gpu0 tensor output_d = {} for (key, value) in sorted(d.items()): tensor_input = torch.tensor([[value]]).to(device) torch.distributed.all_reduce(tensor_input) output_d[key] = tensor_input.item() return output_d def run(max_epochs=None, device=None, batch_size=24, max_sequence_length=128, random_sequence_length=False, epoch_size=None, seed=None, data_dir='data', real_dataset='webtext', fake_dataset='xl-1542M-nucleus', token_dropout=None, large=False, learning_rate=2e-5, weight_decay=0, **kwargs): args = locals() rank, world_size = setup_distributed() if device is None: device = f'cuda:{rank}' if torch.cuda.is_available() else 'cpu' print('rank:', rank, 'world_size:', world_size, 'device:', device) import torch.distributed as dist if distributed() and rank > 0: dist.barrier() model_name = 'roberta-large' if large else 'roberta-base' tokenization_utils.logger.setLevel('ERROR') tokenizer = RobertaTokenizer.from_pretrained(model_name) model = RobertaForSequenceClassification.from_pretrained(model_name).to(device) if rank == 0: summary(model) if distributed(): dist.barrier() if world_size > 1: model = DistributedDataParallel(model, [rank], output_device=rank, find_unused_parameters=True) train_loader, validation_loader = load_datasets(data_dir, real_dataset, fake_dataset, tokenizer, batch_size, max_sequence_length, random_sequence_length, epoch_size, token_dropout, seed) optimizer = Adam(model.parameters(), lr=learning_rate, weight_decay=weight_decay) epoch_loop = count(1) if max_epochs is None else range(1, max_epochs + 1) logdir = os.environ.get("OPENAI_LOGDIR", "logs") os.makedirs(logdir, exist_ok=True) from torch.utils.tensorboard import SummaryWriter writer = SummaryWriter(logdir) if rank == 0 else None best_validation_accuracy = 0 for epoch in epoch_loop: if world_size > 1: train_loader.sampler.set_epoch(epoch) validation_loader.sampler.set_epoch(epoch) train_metrics = train(model, optimizer, device, train_loader, f'Epoch {epoch}') validation_metrics = validate(model, device, validation_loader) combined_metrics = _all_reduce_dict({**validation_metrics, **train_metrics}, device) combined_metrics["train/accuracy"] /= combined_metrics["train/epoch_size"] combined_metrics["train/loss"] /= combined_metrics["train/epoch_size"] combined_metrics["validation/accuracy"] /= combined_metrics["validation/epoch_size"] combined_metrics["validation/loss"] /= combined_metrics["validation/epoch_size"] if rank == 0: for key, value in combined_metrics.items(): writer.add_scalar(key, value, global_step=epoch) if combined_metrics["validation/accuracy"] > best_validation_accuracy: best_validation_accuracy = combined_metrics["validation/accuracy"] model_to_save = model.module if hasattr(model, 'module') else model torch.save(dict( epoch=epoch, model_state_dict=model_to_save.state_dict(), optimizer_state_dict=optimizer.state_dict(), args=args ), os.path.join(logdir, "best-model.pt") ) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--max-epochs', type=int, default=None) parser.add_argument('--device', type=str, default=None) parser.add_argument('--batch-size', type=int, default=24) parser.add_argument('--max-sequence-length', type=int, default=128) parser.add_argument('--random-sequence-length', action='store_true') parser.add_argument('--epoch-size', type=int, default=None) parser.add_argument('--seed', type=int, default=None) parser.add_argument('--data-dir', type=str, default='data') parser.add_argument('--real-dataset', type=str, default='webtext') parser.add_argument('--fake-dataset', type=str, default='xl-1542M-k40') parser.add_argument('--token-dropout', type=float, default=None) parser.add_argument('--large', action='store_true', help='use the roberta-large model instead of roberta-base') parser.add_argument('--learning-rate', type=float, default=2e-5) parser.add_argument('--weight-decay', type=float, default=0) args = parser.parse_args() nproc = int(subprocess.check_output([sys.executable, '-c', "import torch;" "print(torch.cuda.device_count() if torch.cuda.is_available() else 1)"])) if nproc > 1: print(f'Launching {nproc} processes ...', file=sys.stderr) os.environ["MASTER_ADDR"] = '127.0.0.1' os.environ["MASTER_PORT"] = str(29500) os.environ['WORLD_SIZE'] = str(nproc) os.environ['OMP_NUM_THREAD'] = str(1) subprocesses = [] for i in range(nproc): os.environ['RANK'] = str(i) os.environ['LOCAL_RANK'] = str(i) process = Process(target=run, kwargs=vars(args)) process.start() subprocesses.append(process) for process in subprocesses: process.join() else: run(**vars(args))