train_ms.py

import os
import json
import argparse
import itertools
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 torch.nn.parallel import DistributedDataParallel as DDP
from torch.cuda.amp import autocast, GradScaler
from tqdm import tqdm
import logging
logging.getLogger('numba').setLevel(logging.WARNING)
import commons
import utils
from data_utils import (
    TextAudioSpeakerLoader,
    TextAudioSpeakerCollate,
    DistributedBucketSampler
)
from models import (
    SynthesizerTrn,
    MultiPeriodDiscriminator,
    DurationDiscriminator,
)
from losses import (
    generator_loss,
    discriminator_loss,
    feature_loss,
    kl_loss
)
from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
from text.symbols import symbols

torch.backends.cudnn.benchmark = True
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
torch.set_float32_matmul_precision('medium')
torch.backends.cuda.sdp_kernel("flash")
torch.backends.cuda.enable_flash_sdp(True)
torch.backends.cuda.enable_mem_efficient_sdp(True)
torch.backends.cuda.enable_math_sdp(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'] = '65280'

    hps = utils.get_hparams()
    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.model_dir)
        logger.info(hps)
        utils.check_git_hash(hps.model_dir)
        writer = SummaryWriter(log_dir=hps.model_dir)
        writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval"))

    dist.init_process_group(backend='gloo', 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=4, shuffle=False, pin_memory=True,
                              collate_fn=collate_fn, batch_sampler=train_sampler,
                              persistent_workers=True,prefetch_factor=4)  #256G Memory suitable loader.
    if rank == 0:
        eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data)
        eval_loader = DataLoader(eval_dataset, num_workers=0, shuffle=False,
                                 batch_size=1, pin_memory=True,
                                 drop_last=False, collate_fn=collate_fn)
    if "use_noise_scaled_mas" in hps.model.keys() and hps.model.use_noise_scaled_mas == True:
        print("Using noise scaled MAS for VITS2")
        use_noise_scaled_mas = True
        mas_noise_scale_initial = 0.01
        noise_scale_delta = 2e-6
    else:
        print("Using normal MAS for VITS1")
        use_noise_scaled_mas = False
        mas_noise_scale_initial = 0.0
        noise_scale_delta = 0.0
    if "use_duration_discriminator" in hps.model.keys() and hps.model.use_duration_discriminator == True:
        print("Using duration discriminator for VITS2")
        use_duration_discriminator = True
        net_dur_disc = DurationDiscriminator(
         hps.model.hidden_channels, 
         hps.model.hidden_channels, 
         3, 
         0.1, 
         gin_channels=hps.model.gin_channels if hps.data.n_speakers != 0 else 0,
         ).cuda(rank)
    if "use_spk_conditioned_encoder" in hps.model.keys() and hps.model.use_spk_conditioned_encoder == True:
        if hps.data.n_speakers == 0:
            raise ValueError("n_speakers must be > 0 when using spk conditioned encoder to train multi-speaker model")
        use_spk_conditioned_encoder = True
    else:
        print("Using normal encoder for VITS1")
        use_spk_conditioned_encoder = False

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

    freeze_enc = getattr(hps.model, "freeze_enc", False)
    if freeze_enc:
        print("freeze encoder !!!")
        for param in net_g.enc_p.parameters():
            param.requires_grad = False

    net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank)
    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)
    if net_dur_disc is not None:
        optim_dur_disc = torch.optim.AdamW(
        net_dur_disc.parameters(),
        hps.train.learning_rate,
        betas=hps.train.betas,
        eps=hps.train.eps)
    else:
        optim_dur_disc = None
    net_g = DDP(net_g, device_ids=[rank], find_unused_parameters=True)
    net_d = DDP(net_d, device_ids=[rank], find_unused_parameters=True)
    if net_dur_disc is not None:
        net_dur_disc = DDP(net_dur_disc, device_ids=[rank], find_unused_parameters=True)
    try:
        if net_dur_disc is not None:
            _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "DUR_*.pth"), net_dur_disc, optim_dur_disc, skip_optimizer=True)
        _, optim_g, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g,
                                                   optim_g, skip_optimizer=True)
        _, optim_d, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d,
                                                   optim_d, skip_optimizer=True)
            
        epoch_str = max(epoch_str, 1)
        global_step = (epoch_str - 1) * len(train_loader)
    except Exception as e:
        print(e)
        epoch_str = 1
        global_step = 0


    scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hps.train.lr_decay)
    scheduler_g.last_epoch = epoch_str - 2
    scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=hps.train.lr_decay)
    scheduler_d.last_epoch = epoch_str - 2
    if net_dur_disc is not None:
        scheduler_dur_disc = torch.optim.lr_scheduler.ExponentialLR(optim_dur_disc, gamma=hps.train.lr_decay)
        scheduler_dur_disc.last_epoch = epoch_str - 2
    else:
        scheduler_dur_disc = None
    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, net_dur_disc], [optim_g, optim_d, optim_dur_disc], [scheduler_g, scheduler_d, scheduler_dur_disc], scaler, [train_loader, eval_loader], logger, [writer, writer_eval])
        else:
            train_and_evaluate(rank, epoch, hps, [net_g, net_d, net_dur_disc], [optim_g, optim_d, optim_dur_disc], [scheduler_g, scheduler_d, scheduler_dur_disc], scaler, [train_loader, None], None, None)
        scheduler_g.step()
        scheduler_d.step()
        if net_dur_disc is not None:
            scheduler_dur_disc.step()


def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers):
    net_g, net_d, net_dur_disc = nets
    optim_g, optim_d, optim_dur_disc = optims
    scheduler_g, scheduler_d, scheduler_dur_disc = 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()
    if net_dur_disc is not None:
        net_dur_disc.train()
    for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers, tone, language, bert) in tqdm(enumerate(train_loader)):
        if net_g.module.use_noise_scaled_mas:
            current_mas_noise_scale = net_g.module.mas_noise_scale_initial - net_g.module.noise_scale_delta * global_step
            net_g.module.current_mas_noise_scale = max(current_mas_noise_scale, 0.0)
        x, x_lengths = x.cuda(rank, non_blocking=True), x_lengths.cuda(rank, non_blocking=True)
        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)
        speakers = speakers.cuda(rank, non_blocking=True)
        tone = tone.cuda(rank, non_blocking=True)
        language = language.cuda(rank, non_blocking=True)
        bert = bert.cuda(rank, non_blocking=True)

        with autocast(enabled=hps.train.fp16_run):
            y_hat, l_length, attn, ids_slice, x_mask, z_mask, \
                (z, z_p, m_p, logs_p, m_q, logs_q), (hidden_x, logw, logw_) = net_g(x, x_lengths, spec, spec_lengths, speakers, tone, language, bert)
            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
            if net_dur_disc is not None:
                y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x.detach(), x_mask.detach(), logw.detach(), logw_.detach())
                with autocast(enabled=False):
                 # TODO: I think need to mean using the mask, but for now, just mean all
                    loss_dur_disc, losses_dur_disc_r, losses_dur_disc_g = discriminator_loss(y_dur_hat_r, y_dur_hat_g)
                    loss_dur_disc_all = loss_dur_disc
                optim_dur_disc.zero_grad()
                scaler.scale(loss_dur_disc_all).backward()
                scaler.unscale_(optim_dur_disc)
                grad_norm_dur_disc = commons.clip_grad_value_(net_dur_disc.parameters(), None)
                scaler.step(optim_dur_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)
            if net_dur_disc is not None:
                y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x, x_mask, logw, logw_)
            with autocast(enabled=False):
                loss_dur = torch.sum(l_length.float())
                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 + loss_dur + loss_kl
                if net_dur_disc is not None:
                    loss_dur_gen, losses_dur_gen = generator_loss(y_dur_hat_g)
                    loss_gen_all += loss_dur_gen
        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, loss_dur, 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/dur": loss_dur, "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/attn": utils.plot_alignment_to_numpy(attn[0, 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.model_dir, "G_{}.pth".format(global_step)))
                utils.save_checkpoint(net_d, optim_d, hps.train.learning_rate, epoch,
                                      os.path.join(hps.model_dir, "D_{}.pth".format(global_step)))
                if net_dur_disc is not None:
                    utils.save_checkpoint(net_dur_disc, optim_dur_disc, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "DUR_{}.pth".format(global_step)))    
                keep_ckpts = getattr(hps.train, 'keep_ckpts', 5)
                if keep_ckpts > 0:
                    utils.clean_checkpoints(path_to_models=hps.model_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, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers, tone, language, bert) in enumerate(eval_loader):
            x, x_lengths = x.cuda(), x_lengths.cuda()
            spec, spec_lengths = spec.cuda(), spec_lengths.cuda()
            y, y_lengths = y.cuda(), y_lengths.cuda()
            speakers = speakers.cuda()
            bert = bert.cuda()
            tone = tone.cuda()
            language = language.cuda()
            for use_sdp in [True, False]:
                y_hat, attn, mask, *_ = generator.module.infer(x, x_lengths, speakers, tone, language, bert, y=spec, max_len=1000, sdp_ratio=0.0 if not use_sdp else 1.0)
                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}": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy())
                })
                audio_dict.update({
                    f"gen/audio_{batch_idx}_{use_sdp}": 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]]})

    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()