vits-finetuning/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

import commons
import utils
from data_utils import (
  TextAudioSpeakerLoader,
  TextAudioSpeakerCollate,
  DistributedBucketSampler
)
from models import (
  SynthesizerTrn,
  MultiPeriodDiscriminator,
  MultiCoMBDiscriminator,
  MultiSubBandDiscriminator
)
from losses import (
  generator_loss,
  discriminator_loss,
  feature_loss,
  kl_loss,
  ForwardSumLoss
)
from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
from text.symbols import symbols

import logging

torch.backends.cudnn.benchmark = False
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'] = '35000'

  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"))
    numba_logger = logging.getLogger('numba')
    numba_logger.setLevel(logging.WARNING)

  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 = 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=8, shuffle=False, pin_memory=True,
      collate_fn=collate_fn, batch_sampler=train_sampler)
  if rank == 0:
    eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data)
    eval_loader = DataLoader(eval_dataset, num_workers=8, shuffle=False,
        batch_size=hps.train.batch_size, pin_memory=True,
        drop_last=False, collate_fn=collate_fn)

  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,
      **hps.model).cuda(rank)
  
  mcmbd = MultiCoMBDiscriminator(hps.disc.combd_kernels, hps.disc.combd_channels, hps.disc.combd_groups, hps.disc.combd_strides).cuda(rank)
  msbd = MultiSubBandDiscriminator(hps.disc.tkernels, hps.disc.fkernel, hps.disc.tchannels, hps.disc.fchannels, hps.disc.tstrides, hps.disc.fstride,
                                     hps.disc.tdilations, hps.disc.fdilations, hps.disc.tsubband, hps.disc.pqmf_n, hps.disc.pqmf_m, hps.disc.freq_init_ch).cuda(rank)
  optim_g = torch.optim.AdamW(
      net_g.parameters(), 
      hps.train.learning_rate, 
      betas=hps.train.betas, 
      eps=hps.train.eps)
  optim_d = torch.optim.AdamW(
      itertools.chain(msbd.parameters(), mcmbd.parameters()),
      hps.train.learning_rate, 
      betas=hps.train.betas, 
      eps=hps.train.eps)
  net_g = DDP(net_g, device_ids=[rank])
  mcmbd = DDP(mcmbd, device_ids=[rank],find_unused_parameters=True)
  msbd = DDP(msbd, device_ids=[rank])

  try:
    _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, optim_g)
    _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D0_*.pth"), mcmbd, optim_d)
    _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D1_*.pth"), msbd, optim_d)
    global_step = (epoch_str - 1) * len(train_loader)
  except:
    epoch_str = 1
    global_step = 0
  
  if len(hps.pt_path) > 1 and global_step == 0:
    if rank == 0:
        logger.info(f"Trying to load pretrained weights from {hps.pt_path}")
    _, _, _, s_ = utils.load_checkpoint(utils.latest_checkpoint_path(hps.pt_path, "G_*.pth"), net_g, None)
    _, _, _, s_ = utils.load_checkpoint(utils.latest_checkpoint_path(hps.pt_path, "D0_*.pth"), mcmbd, None)
    _, _, _, s_ = utils.load_checkpoint(utils.latest_checkpoint_path(hps.pt_path, "D1_*.pth"), msbd, None)
    

  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,  mcmbd, msbd], [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,  mcmbd, msbd], [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, mcmbd, msbd = 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)
  forward_sum = ForwardSumLoss()
  global global_step


  net_g.train()
  mcmbd.train()
  msbd.train()
  for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers) in enumerate(train_loader):
    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)

    with autocast(enabled=hps.train.fp16_run):
      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, x1, l_length, attn, attn_logprob, ids_slice, x_mask, z_mask,\
      (z, z_p, m_p, logs_p, m_q, logs_q) = net_g(x, x_lengths, spec, spec_lengths,mel,speakers)


      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 

      # MSBD first
      y_d_hat_r, y_d_hat_g, _, _ =  msbd(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)
        
      #MCMBD second
      
      y_d1_hat_r, y_d1_hat_g, _, _ = mcmbd(y, y_hat.detach(), x1.detach())
      with autocast(enabled=False):
        loss_disc_d1, losses_disc_d1_r, losses_disc_d1_g = discriminator_loss(y_d1_hat_r, y_d1_hat_g)
        loss_disc_all = loss_disc + loss_disc_d1
      
      
    
    optim_d.zero_grad()
    scaler.scale(loss_disc_all).backward()
    scaler.unscale_(optim_d)
    grad_norm_d = commons.clip_grad_value_(msbd.parameters(), None) + commons.clip_grad_value_(mcmbd.parameters(), None)
    scaler.step(optim_d)

    with autocast(enabled=hps.train.fp16_run):
      # Generator
      y_df_hat_r, y_df_hat_g, fmap_f_r, fmap_f_g = mcmbd(y, y_hat, x1)
      y_ds_hat_r, y_ds_hat_g, fmap_s_r, fmap_s_g = msbd(y, y_hat)
      with autocast(enabled=False):
        loss_dur = torch.sum(l_length.float())
        ctc_loss = forward_sum(attn_logprob=attn_logprob, in_lens=x_lengths, out_lens=spec_lengths)
        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_f = 2 * feature_loss(fmap_f_r, fmap_f_g)
        loss_fm_s = 2 * feature_loss(fmap_s_r, fmap_s_g)
        loss_fm = loss_fm_f + loss_fm_s
        
        loss_gen_f, losses_gen_f = generator_loss(y_df_hat_g)
        loss_gen_s, losses_gen_s = generator_loss(y_ds_hat_g)
        
        loss_gen = loss_gen_f + loss_gen_s
        losses_gen = losses_gen_f + losses_gen_s
        
        loss_gen_all = loss_gen + loss_fm + loss_mel + loss_dur + loss_kl + ctc_loss
    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, ctc_loss]
        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, "loss/g/ctc" : ctc_loss})

        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(mcmbd, optim_d, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "D0_{}.pth".format(global_step)))
        utils.save_checkpoint(msbd, optim_d, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "D1_{}.pth".format(global_step)))
        
    global_step += 1
  
  if rank == 0:
    logger.info('====> Epoch: {}'.format(epoch))

 
def evaluate(hps, generator, eval_loader, writer_eval):
    generator.eval()
    with torch.no_grad():
      for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers) in enumerate(eval_loader):
        x, x_lengths = x.cuda(0), x_lengths.cuda(0)
        spec, spec_lengths = spec.cuda(0), spec_lengths.cuda(0)
        y, y_lengths = y.cuda(0), y_lengths.cuda(0)
        speakers = speakers.cuda(0)

        # remove else
        x = x[:1]
        x_lengths = x_lengths[:1]
        spec = spec[:1]
        spec_lengths = spec_lengths[:1]
        y = y[:1]
        y_lengths = y_lengths[:1]
        speakers = speakers[:1]
        break
      y_hat, attn, mask, *_ = generator.module.infer(x, x_lengths, speakers, max_len=1000)
      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 = {
      "gen/mel": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy())
    }
    audio_dict = {
      "gen/audio": y_hat[0,:,:y_hat_lengths[0]]
    }
    if global_step == 0:
      image_dict.update({"gt/mel": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy())})
      audio_dict.update({"gt/audio": 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()