s2_train.py

import os
import torch
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 torch.nn.parallel import DistributedDataParallel as DDP
from torch.cuda.amp import autocast, GradScaler
from tqdm import tqdm
import logging
logging.getLogger("matplotlib").setLevel(logging.INFO)
logging.getLogger("h5py").setLevel(logging.INFO)
logging.getLogger("numba").setLevel(logging.INFO)

from module import commons
import utils
from module.data_utils import (
    TextAudioSpeakerLoader,
    TextAudioSpeakerCollate,
    DistributedBucketSampler
)
from module.models import (
    SynthesizerTrn,
    MultiPeriodDiscriminator,
)
from module.losses import (
    generator_loss,
    discriminator_loss,
    feature_loss,
    kl_loss
)
from module.mel_processing import mel_spectrogram_torch, spec_to_mel_torch

torch.backends.cudnn.benchmark = True
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'] = '8000'

    hps = utils.get_hparams(stage=2)
    mp.spawn(run, nprocs=n_gpus, args=(n_gpus, hps,))


def run(rank, n_gpus, hps):
    global global_step
    if rank == 0:
        logger = utils.get_logger(hps.s2_ckpt_dir)
        logger.info(hps)
        utils.check_git_hash(hps.s2_ckpt_dir)
        writer = SummaryWriter(log_dir=hps.s2_ckpt_dir)
        writer_eval = SummaryWriter(log_dir=os.path.join(hps.s2_ckpt_dir, "eval"))

    dist.init_process_group(backend='gloo' if os.name == 'nt' else 'nccl', init_method='env://', world_size=n_gpus,
                            rank=rank)
    torch.manual_seed(hps.train.seed)
    torch.cuda.set_device(rank)

    train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps.data)
    train_sampler = DistributedBucketSampler(
        train_dataset,
        hps.train.batch_size,
        [32, 300, 400, 500, 600, 700, 800, 900, 1000],
        num_replicas=n_gpus,
        rank=rank,
        shuffle=True)
    collate_fn = TextAudioSpeakerCollate()
    train_loader = DataLoader(train_dataset, num_workers=6, shuffle=False, pin_memory=True,
                              collate_fn=collate_fn, batch_sampler=train_sampler, persistent_workers=True)
    if rank == 0:
        eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data, val=True)
        eval_loader = DataLoader(eval_dataset, num_workers=0, shuffle=False,
                                 batch_size=1, pin_memory=True,
                                 drop_last=False, collate_fn=collate_fn)

    net_g = SynthesizerTrn(
        hps.data.filter_length // 2 + 1,
        hps.train.segment_size // hps.data.hop_length,
        n_speakers=hps.data.n_speakers,
        **hps.model).cuda(rank)

    net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank)
    for name, param in net_g.named_parameters():
        if not param.requires_grad:
            print(name,"not requires_grad")
    optim_g = torch.optim.AdamW(
        filter(lambda p: p.requires_grad, net_g.parameters()),
        hps.train.learning_rate,
        betas=hps.train.betas,
        eps=hps.train.eps)
    optim_d = torch.optim.AdamW(
        net_d.parameters(),
        hps.train.learning_rate,
        betas=hps.train.betas,
        eps=hps.train.eps)
    net_g = DDP(net_g, device_ids=[rank])
    net_d = DDP(net_d, device_ids=[rank])

    pretrain_dir = hps.pretrain
    if pretrain_dir is None:
        _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.s2_ckpt_dir, "G_*.pth"), net_g,
                                                   optim_g, False)
        _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.s2_ckpt_dir, "D_*.pth"), net_d,
                                                   optim_d, False)
        epoch_str = max(epoch_str, 1)
        global_step = (epoch_str - 1) * len(train_loader)
    else:
        _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(pretrain_dir, "G_*.pth"), net_g,
                                                   optim_g, True)
        _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(pretrain_dir, "D_*.pth"), net_d,
                                                   optim_d, True)
        epoch_str = 1
        global_step = 0

    if hps.resume_step != None:
        global_step = hps.resume_step


    scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
    scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)

    scaler = GradScaler(enabled=hps.train.fp16_run)

    for epoch in range(epoch_str, hps.train.epochs + 1):
        if rank == 0:
            train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler,
                               [train_loader, eval_loader], logger, [writer, writer_eval])
        else:
            train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler,
                               [train_loader, None], None, None)
        scheduler_g.step()
        scheduler_d.step()


def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers):
    net_g, net_d = nets
    optim_g, optim_d = optims
    scheduler_g, scheduler_d = schedulers
    train_loader, eval_loader = loaders
    if writers is not None:
        writer, writer_eval = writers

    train_loader.batch_sampler.set_epoch(epoch)
    global global_step

    net_g.train()
    net_d.train()
    for batch_idx, (ssl, ssl_lengths, spec, spec_lengths, y, y_lengths, text, text_lengths) in tqdm(enumerate(train_loader)):
        spec, spec_lengths = spec.cuda(rank, non_blocking=True), spec_lengths.cuda(rank, non_blocking=True)
        y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda(rank, non_blocking=True)
        ssl = ssl.cuda(rank, non_blocking=True)
        ssl_lengths = ssl_lengths.cuda(rank, non_blocking=True)
        text, text_lengths = text.cuda(rank, non_blocking=True), text_lengths.cuda(rank, non_blocking=True)

        with autocast(enabled=hps.train.fp16_run):
            y_hat, kl_ssl, ids_slice, x_mask, z_mask, \
                (z, z_p, m_p, logs_p, m_q, logs_q), stats_ssl = net_g(ssl, spec, spec_lengths, text, text_lengths)

            mel = spec_to_mel_torch(
                spec,
                hps.data.filter_length,
                hps.data.n_mel_channels,
                hps.data.sampling_rate,
                hps.data.mel_fmin,
                hps.data.mel_fmax)
            y_mel = commons.slice_segments(mel, ids_slice, hps.train.segment_size // hps.data.hop_length)
            y_hat_mel = mel_spectrogram_torch(
                y_hat.squeeze(1),
                hps.data.filter_length,
                hps.data.n_mel_channels,
                hps.data.sampling_rate,
                hps.data.hop_length,
                hps.data.win_length,
                hps.data.mel_fmin,
                hps.data.mel_fmax
            )

            y = commons.slice_segments(y, ids_slice * hps.data.hop_length, hps.train.segment_size)  # slice

            # Discriminator
            y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
            with autocast(enabled=False):
                loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g)
                loss_disc_all = loss_disc
        optim_d.zero_grad()
        scaler.scale(loss_disc_all).backward()
        scaler.unscale_(optim_d)
        grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
        scaler.step(optim_d)

        with autocast(enabled=hps.train.fp16_run):
            # Generator
            y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
            with autocast(enabled=False):
                loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
                loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl

                loss_fm = feature_loss(fmap_r, fmap_g)
                loss_gen, losses_gen = generator_loss(y_d_hat_g)
                loss_gen_all = loss_gen + loss_fm + loss_mel + kl_ssl * 1 + loss_kl

        optim_g.zero_grad()
        scaler.scale(loss_gen_all).backward()
        scaler.unscale_(optim_g)
        grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None)
        scaler.step(optim_g)
        scaler.update()

        if rank == 0:
            if global_step % hps.train.log_interval == 0:
                lr = optim_g.param_groups[0]['lr']
                losses = [loss_disc, loss_gen, loss_fm, loss_mel, kl_ssl, loss_kl]
                logger.info('Train Epoch: {} [{:.0f}%]'.format(
                    epoch,
                    100. * batch_idx / len(train_loader)))
                logger.info([x.item() for x in losses] + [global_step, lr])

                scalar_dict = {"loss/g/total": loss_gen_all, "loss/d/total": loss_disc_all, "learning_rate": lr,
                               "grad_norm_d": grad_norm_d, "grad_norm_g": grad_norm_g}
                scalar_dict.update(
                    {"loss/g/fm": loss_fm, "loss/g/mel": loss_mel, "loss/g/kl_ssl": kl_ssl, "loss/g/kl": loss_kl})

                # scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)})
                # scalar_dict.update({"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)})
                # scalar_dict.update({"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)})
                image_dict = {
                    "slice/mel_org": utils.plot_spectrogram_to_numpy(y_mel[0].data.cpu().numpy()),
                    "slice/mel_gen": utils.plot_spectrogram_to_numpy(y_hat_mel[0].data.cpu().numpy()),
                    "all/mel": utils.plot_spectrogram_to_numpy(mel[0].data.cpu().numpy()),
                    "all/stats_ssl": utils.plot_spectrogram_to_numpy(stats_ssl[0].data.cpu().numpy()),
                }
                utils.summarize(
                    writer=writer,
                    global_step=global_step,
                    images=image_dict,
                    scalars=scalar_dict)

            if global_step % hps.train.eval_interval == 0:
                evaluate(hps, net_g, eval_loader, writer_eval)
                utils.save_checkpoint(net_g, optim_g, hps.train.learning_rate, epoch,
                                      os.path.join(hps.s2_ckpt_dir, "G_{}.pth".format(global_step)))
                utils.save_checkpoint(net_d, optim_d, hps.train.learning_rate, epoch,
                                      os.path.join(hps.s2_ckpt_dir, "D_{}.pth".format(global_step)))
                keep_ckpts = getattr(hps.train, 'keep_ckpts', 3)
                if keep_ckpts > 0:
                    utils.clean_checkpoints(path_to_models=hps.s2_ckpt_dir, n_ckpts_to_keep=keep_ckpts, sort_by_time=True)


        global_step += 1

    if rank == 0:
        logger.info('====> Epoch: {}'.format(epoch))



def evaluate(hps, generator, eval_loader, writer_eval):
    generator.eval()
    image_dict = {}
    audio_dict = {}
    print("Evaluating ...")
    with torch.no_grad():
        for batch_idx, (ssl, ssl_lengths, spec, spec_lengths, y, y_lengths, text, text_lengths) in enumerate(eval_loader):
            print(111)
            spec, spec_lengths = spec.cuda(), spec_lengths.cuda()
            y, y_lengths = y.cuda(), y_lengths.cuda()
            ssl = ssl.cuda()
            text, text_lengths = text.cuda(), text_lengths.cuda()
            for test in [0, 1]:

                y_hat, mask, *_ = generator.module.infer(ssl,spec,  spec_lengths,text, text_lengths, test=test)
                y_hat_lengths = mask.sum([1, 2]).long() * hps.data.hop_length

                mel = spec_to_mel_torch(
                    spec,
                    hps.data.filter_length,
                    hps.data.n_mel_channels,
                    hps.data.sampling_rate,
                    hps.data.mel_fmin,
                    hps.data.mel_fmax)
                y_hat_mel = mel_spectrogram_torch(
                    y_hat.squeeze(1).float(),
                    hps.data.filter_length,
                    hps.data.n_mel_channels,
                    hps.data.sampling_rate,
                    hps.data.hop_length,
                    hps.data.win_length,
                    hps.data.mel_fmin,
                    hps.data.mel_fmax
                )
                image_dict.update({
                    f"gen/mel_{batch_idx}_{test}": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy())
                })
                audio_dict.update({
                    f"gen/audio_{batch_idx}_{test}": y_hat[0, :, :y_hat_lengths[0]]
                })
                image_dict.update({f"gt/mel_{batch_idx}": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy())})
                audio_dict.update({f"gt/audio_{batch_idx}": y[0, :, :y_lengths[0]]})

        # y_hat, mask, *_ = generator.module.infer(ssl, spec_lengths, speakers, y=None)
        # audio_dict.update({
        #     f"gen/audio_{batch_idx}_style_pred": y_hat[0, :, :]
        # })

    utils.summarize(
        writer=writer_eval,
        global_step=global_step,
        images=image_dict,
        audios=audio_dict,
        audio_sampling_rate=hps.data.sampling_rate
    )
    generator.train()

if __name__ == "__main__":
    main()