train_wandb.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 bitsandbytes as bnb
import wandb

import commons
import utils
from data_utils import (
  TextAudioLoader,
  TextAudioCollate,
  DistributedBucketSampler
)
from models import (
  SynthesizerTrn,
  MultiPeriodDiscriminator,
)
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
from text import text_to_sequence

import matplotlib.pyplot as plt

def get_text(text, hps):
    text_norm = text_to_sequence(text, hps.data.text_cleaners)
    if hps.data.add_blank:
        text_norm = commons.intersperse(text_norm, 0)
    text_norm = torch.LongTensor(text_norm)
    return text_norm

from scipy.io.wavfile import write


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()
  mp.spawn(run, nprocs=n_gpus, args=(n_gpus, hps,))
#   run(0, 1, hps)


def run(rank, n_gpus, hps):
  with wandb.init(project=f"vits_finetuning_{hps.data.name}", config=hps, resume=hps.train.wandb_resume, id=wandb.util.generate_id()):
    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='nccl', init_method='env://', world_size=n_gpus, rank=rank)
    torch.manual_seed(hps.train.seed)
    torch.cuda.set_device(rank)

    train_dataset = TextAudioLoader(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 = TextAudioCollate()
    train_loader = DataLoader(train_dataset, num_workers=2, shuffle=False, pin_memory=True,
        collate_fn=collate_fn, batch_sampler=train_sampler)
    if rank == 0:
      eval_dataset = TextAudioLoader(hps.data.validation_files, hps.data)
      eval_loader = DataLoader(eval_dataset, num_workers=2, 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,
        **hps.model).cuda(rank)
    net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank)
    optim_g = bnb.optim.AdamW(
        net_g.parameters(), 
        hps.train.learning_rate, 
        betas=hps.train.betas, 
        eps=hps.train.eps)
    optim_d = bnb.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])
    wandb.watch([net_g, net_d], log='all')

    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, "D_*.pth"), net_d, optim_d)
      global_step = (epoch_str - 1) * len(train_loader)
    except:
      if hps.train.finetune:
        print("loading pretrained generator")
        generator_state_dict = torch.load('./pretrained/generator.pth')
        if hasattr(net_g, 'module'):
          net_g.module.load_state_dict(generator_state_dict['model'])
          print("pretrained generator loaded")
        else:
          net_g.load_state_dict(generator_state_dict['model'])
          print("pretrained generator loaded")

        print("loading pretrained discriminator")
        if hasattr(net_d, 'module'):
          net_d.module.load_state_dict(torch.load('./pretrained/discriminator.pth'))
          print("pretrained discriminator loaded")
        else:
          net_d.load_state_dict(torch.load('./pretrained/discriminator.pth'))
          print("pretrained discriminator loaded")
      epoch_str = 1
      global_step = 0

    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, (x, x_lengths, spec, spec_lengths, y, y_lengths) 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)

    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) = net_g(x, x_lengths, spec, spec_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_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
    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]
        losses_names = ['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)))
        
        logging_losses = [f"Epoch:{epoch}, [{round(100 * batch_idx / len(train_loader), 1)}%]"]
        for i in range(len(losses)):
          logging_losses.append(f"{losses_names[i]}:{round(losses[i].item(), 3)}")
        logging_losses.append(f"global_step:{global_step}")
        logging_losses.append(f"lr:{lr}")

        logger.info(logging_losses)
        # logger.info([f"{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)})

        wandb.log(scalar_dict, step=global_step)


        # 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)
        stn_tst = get_text("My name is Optimus Prime, and this is a test of my voice generated by VITS text to speech.", hps)
        net_g.eval()
        with torch.no_grad():
            x_tst = stn_tst.unsqueeze(0)
            x_tst_lengths = torch.LongTensor([stn_tst.size(0)])
            audio = net_g.module.infer(x_tst.cuda(0), x_tst_lengths.cuda(0), noise_scale=.667, noise_scale_w=0.8, length_scale=1)[0][0,0].data.float().cpu().numpy()
            wandb.log({"epoch":epoch, "global_step":global_step, "inferred_audio" : wandb.Audio(audio, sample_rate=hps.data.sampling_rate, caption='inference_test')})
        net_g.train(
        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)))
    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) 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)

        # 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]
        break
      y_hat, attn, mask, *_ = generator.module.infer(x, x_lengths, 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()