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| import os | |
| import json | |
| import argparse | |
| import math | |
| import torch | |
| from torch import nn, optim | |
| from torch.nn import functional as F | |
| from torch.utils.data import DataLoader | |
| from torch.utils.tensorboard import SummaryWriter | |
| import torch.multiprocessing as mp | |
| import torch.distributed as dist | |
| from apex.parallel import DistributedDataParallel as DDP | |
| from apex import amp | |
| from data_utils import TextMelLoader, TextMelCollate | |
| import models | |
| import commons | |
| import utils | |
| global_step = 0 | |
| def main(): | |
| """Assume Single Node Multi GPUs Training Only""" | |
| assert torch.cuda.is_available(), "CPU training is not allowed." | |
| n_gpus = torch.cuda.device_count() | |
| os.environ["MASTER_ADDR"] = "localhost" | |
| os.environ["MASTER_PORT"] = "80000" | |
| hps = utils.get_hparams() | |
| mp.spawn( | |
| train_and_eval, | |
| nprocs=n_gpus, | |
| args=( | |
| n_gpus, | |
| hps, | |
| ), | |
| ) | |
| def train_and_eval(rank, n_gpus, hps): | |
| global global_step | |
| if rank == 0: | |
| logger = utils.get_logger(hps.log_dir) | |
| logger.info(hps) | |
| utils.check_git_hash(hps.log_dir) | |
| writer = SummaryWriter(log_dir=hps.log_dir) | |
| writer_eval = SummaryWriter(log_dir=os.path.join(hps.log_dir, "eval")) | |
| dist.init_process_group( | |
| backend="nccl", init_method="env://", world_size=n_gpus, rank=rank | |
| ) | |
| torch.manual_seed(hps.train.seed) | |
| torch.cuda.set_device(rank) | |
| train_dataset = TextMelLoader(hps.data.training_files, hps.data) | |
| train_sampler = torch.utils.data.distributed.DistributedSampler( | |
| train_dataset, num_replicas=n_gpus, rank=rank, shuffle=True | |
| ) | |
| collate_fn = TextMelCollate(1) | |
| train_loader = DataLoader( | |
| train_dataset, | |
| num_workers=8, | |
| shuffle=False, | |
| batch_size=hps.train.batch_size, | |
| pin_memory=True, | |
| drop_last=True, | |
| collate_fn=collate_fn, | |
| sampler=train_sampler, | |
| ) | |
| if rank == 0: | |
| val_dataset = TextMelLoader(hps.data.validation_files, hps.data) | |
| val_loader = DataLoader( | |
| val_dataset, | |
| num_workers=8, | |
| shuffle=False, | |
| batch_size=hps.train.batch_size, | |
| pin_memory=True, | |
| drop_last=True, | |
| collate_fn=collate_fn, | |
| ) | |
| symbols = hps.data.punc + hps.data.chars | |
| generator = models.FlowGenerator( | |
| n_vocab=len(symbols) + getattr(hps.data, "add_blank", False), | |
| out_channels=hps.data.n_mel_channels, | |
| **hps.model | |
| ).cuda(rank) | |
| optimizer_g = commons.Adam( | |
| generator.parameters(), | |
| scheduler=hps.train.scheduler, | |
| dim_model=hps.model.hidden_channels, | |
| warmup_steps=hps.train.warmup_steps, | |
| lr=hps.train.learning_rate, | |
| betas=hps.train.betas, | |
| eps=hps.train.eps, | |
| ) | |
| if hps.train.fp16_run: | |
| generator, optimizer_g._optim = amp.initialize( | |
| generator, optimizer_g._optim, opt_level="O1" | |
| ) | |
| generator = DDP(generator) | |
| epoch_str = 1 | |
| global_step = 0 | |
| try: | |
| _, _, _, epoch_str = utils.load_checkpoint( | |
| utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), | |
| generator, | |
| optimizer_g, | |
| ) | |
| epoch_str += 1 | |
| optimizer_g.step_num = (epoch_str - 1) * len(train_loader) | |
| optimizer_g._update_learning_rate() | |
| global_step = (epoch_str - 1) * len(train_loader) | |
| except: | |
| if hps.train.ddi and os.path.isfile(os.path.join(hps.model_dir, "ddi_G.pth")): | |
| _ = utils.load_checkpoint( | |
| os.path.join(hps.model_dir, "ddi_G.pth"), generator, optimizer_g | |
| ) | |
| for epoch in range(epoch_str, hps.train.epochs + 1): | |
| if rank == 0: | |
| train( | |
| rank, epoch, hps, generator, optimizer_g, train_loader, logger, writer | |
| ) | |
| evaluate( | |
| rank, | |
| epoch, | |
| hps, | |
| generator, | |
| optimizer_g, | |
| val_loader, | |
| logger, | |
| writer_eval, | |
| ) | |
| if epoch % hps.train.save_epoch == 0: | |
| utils.save_checkpoint( | |
| generator, | |
| optimizer_g, | |
| hps.train.learning_rate, | |
| epoch, | |
| os.path.join(hps.model_dir, "G_{}.pth".format(epoch)), | |
| ) | |
| else: | |
| train(rank, epoch, hps, generator, optimizer_g, train_loader, None, None) | |
| def train(rank, epoch, hps, generator, optimizer_g, train_loader, logger, writer): | |
| train_loader.sampler.set_epoch(epoch) | |
| global global_step | |
| generator.train() | |
| for batch_idx, (x, x_lengths, y, y_lengths) in enumerate(train_loader): | |
| x, x_lengths = x.cuda(rank, non_blocking=True), x_lengths.cuda( | |
| rank, non_blocking=True | |
| ) | |
| y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda( | |
| rank, non_blocking=True | |
| ) | |
| # Train Generator | |
| optimizer_g.zero_grad() | |
| ( | |
| (z, z_m, z_logs, logdet, z_mask), | |
| (x_m, x_logs, x_mask), | |
| (attn, logw, logw_), | |
| ) = generator(x, x_lengths, y, y_lengths, gen=False) | |
| l_mle = commons.mle_loss(z, z_m, z_logs, logdet, z_mask) | |
| l_length = commons.duration_loss(logw, logw_, x_lengths) | |
| loss_gs = [l_mle, l_length] | |
| loss_g = sum(loss_gs) | |
| if hps.train.fp16_run: | |
| with amp.scale_loss(loss_g, optimizer_g._optim) as scaled_loss: | |
| scaled_loss.backward() | |
| grad_norm = commons.clip_grad_value_( | |
| amp.master_params(optimizer_g._optim), 5 | |
| ) | |
| else: | |
| loss_g.backward() | |
| grad_norm = commons.clip_grad_value_(generator.parameters(), 5) | |
| optimizer_g.step() | |
| if rank == 0: | |
| if batch_idx % hps.train.log_interval == 0: | |
| (y_gen, *_), *_ = generator.module(x[:1], x_lengths[:1], gen=True) | |
| logger.info( | |
| "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format( | |
| epoch, | |
| batch_idx * len(x), | |
| len(train_loader.dataset), | |
| 100.0 * batch_idx / len(train_loader), | |
| loss_g.item(), | |
| ) | |
| ) | |
| logger.info( | |
| [x.item() for x in loss_gs] + [global_step, optimizer_g.get_lr()] | |
| ) | |
| scalar_dict = { | |
| "loss/g/total": loss_g, | |
| "learning_rate": optimizer_g.get_lr(), | |
| "grad_norm": grad_norm, | |
| } | |
| scalar_dict.update( | |
| {"loss/g/{}".format(i): v for i, v in enumerate(loss_gs)} | |
| ) | |
| utils.summarize( | |
| writer=writer, | |
| global_step=global_step, | |
| images={ | |
| "y_org": utils.plot_spectrogram_to_numpy( | |
| y[0].data.cpu().numpy() | |
| ), | |
| "y_gen": utils.plot_spectrogram_to_numpy( | |
| y_gen[0].data.cpu().numpy() | |
| ), | |
| "attn": utils.plot_alignment_to_numpy( | |
| attn[0, 0].data.cpu().numpy() | |
| ), | |
| }, | |
| scalars=scalar_dict, | |
| ) | |
| global_step += 1 | |
| if rank == 0: | |
| logger.info("====> Epoch: {}".format(epoch)) | |
| def evaluate(rank, epoch, hps, generator, optimizer_g, val_loader, logger, writer_eval): | |
| if rank == 0: | |
| global global_step | |
| generator.eval() | |
| losses_tot = [] | |
| with torch.no_grad(): | |
| for batch_idx, (x, x_lengths, y, y_lengths) in enumerate(val_loader): | |
| x, x_lengths = x.cuda(rank, non_blocking=True), x_lengths.cuda( | |
| rank, non_blocking=True | |
| ) | |
| y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda( | |
| rank, non_blocking=True | |
| ) | |
| ( | |
| (z, z_m, z_logs, logdet, z_mask), | |
| (x_m, x_logs, x_mask), | |
| (attn, logw, logw_), | |
| ) = generator(x, x_lengths, y, y_lengths, gen=False) | |
| l_mle = commons.mle_loss(z, z_m, z_logs, logdet, z_mask) | |
| l_length = commons.duration_loss(logw, logw_, x_lengths) | |
| loss_gs = [l_mle, l_length] | |
| loss_g = sum(loss_gs) | |
| if batch_idx == 0: | |
| losses_tot = loss_gs | |
| else: | |
| losses_tot = [x + y for (x, y) in zip(losses_tot, loss_gs)] | |
| if batch_idx % hps.train.log_interval == 0: | |
| logger.info( | |
| "Eval Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format( | |
| epoch, | |
| batch_idx * len(x), | |
| len(val_loader.dataset), | |
| 100.0 * batch_idx / len(val_loader), | |
| loss_g.item(), | |
| ) | |
| ) | |
| logger.info([x.item() for x in loss_gs]) | |
| losses_tot = [x / len(val_loader) for x in losses_tot] | |
| loss_tot = sum(losses_tot) | |
| scalar_dict = {"loss/g/total": loss_tot} | |
| scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(losses_tot)}) | |
| utils.summarize( | |
| writer=writer_eval, global_step=global_step, scalars=scalar_dict | |
| ) | |
| logger.info("====> Epoch: {}".format(epoch)) | |
| if __name__ == "__main__": | |
| main() | |