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train.py
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train.py
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import os
import tqdm
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 typing import List
import utils.task as task
from utils.hparams import get_hparams
from model.models import SynthesizerTrn
from model.discriminator import MultiPeriodDiscriminator
from data_utils import TextAudioLoader, TextAudioCollate, DistributedBucketSampler
from losses import generator_loss, discriminator_loss, feature_loss, kl_loss, kl_loss_normal
from utils.mel_processing import wav_to_mel, spec_to_mel, spectral_norm
from utils.model import slice_segments, clip_grad_value_
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 = 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 = task.get_logger(hps.model_dir)
logger.info(hps)
task.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=8, 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=8, shuffle=False, batch_size=hps.train.batch_size, pin_memory=True, drop_last=False, collate_fn=collate_fn)
net_g = SynthesizerTrn(len(train_dataset.vocab), hps.data.n_mels if hps.data.use_mel else hps.data.n_fft // 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 = torch.optim.AdamW(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])
try:
_, _, _, epoch_str = task.load_checkpoint(task.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, optim_g)
_, _, _, epoch_str = task.load_checkpoint(task.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d, optim_d)
global_step = (epoch_str - 1) * len(train_loader)
net_g.module.mas_noise_scale = max(hps.model.mas_noise_scale - global_step * hps.model.mas_noise_scale_decay, 0.0)
except:
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) # TODO: check
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()
if rank == 0:
loader = tqdm.tqdm(train_loader, desc=f"Epoch {epoch}")
else:
loader = train_loader
for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths) in enumerate(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,
(m_p_text, logs_p_text),
(m_p_dur, logs_p_dur, z_q_dur, logs_q_dur),
(m_p_audio, logs_p_audio, m_q_audio, logs_q_audio),
) = net_g(x, x_lengths, spec, spec_lengths)
mel = spectral_norm(spec) if hps.data.use_mel else spec_to_mel(spec, hps.data.n_fft, hps.data.n_mels, hps.data.sample_rate, hps.data.f_min, hps.data.f_max)
y_hat_mel = wav_to_mel(y_hat.squeeze(1), hps.data.n_fft, hps.data.n_mels, hps.data.sample_rate, hps.data.hop_length, hps.data.win_length, hps.data.f_min, hps.data.f_max)
y_mel = slice_segments(mel, ids_slice, hps.train.segment_size // hps.data.hop_length)
y = 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 = 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_gen, losses_gen = generator_loss(y_d_hat_g)
# TODO Test gain constant
if False:
loss_kl_text = kl_loss_normal(m_q_text, logs_q_text, m_p_text, logs_p_text, x_mask) * hps.train.c_kl_text
loss_kl_dur = kl_loss(z_q_dur, logs_q_dur, m_p_dur, logs_p_dur, z_mask) * hps.train.c_kl_dur
loss_kl_audio = kl_loss_normal(m_p_audio, logs_p_audio, m_q_audio, logs_q_audio, z_mask) * hps.train.c_kl_audio
loss_fm = feature_loss(fmap_r, fmap_g)
loss_gen_all = loss_gen + loss_fm + loss_mel + loss_dur + loss_kl_dur + loss_kl_audio # TODO + loss_kl_text
optim_g.zero_grad()
scaler.scale(loss_gen_all).backward()
scaler.unscale_(optim_g)
grad_norm_g = 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_dur, loss_kl_audio] # TODO loss_kl_text
losses_str = " ".join(f"{loss.item():.3f}" for loss in losses)
loader.set_postfix_str(f"{losses_str}, {global_step}, {lr:.9f}")
# 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_dur})
# 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": task.plot_spectrogram_to_numpy(y_mel[0].data.cpu().numpy()),
# "slice/mel_gen": task.plot_spectrogram_to_numpy(y_hat_mel[0].data.cpu().numpy()),
# "all/mel": task.plot_spectrogram_to_numpy(mel[0].data.cpu().numpy()),
# "all/attn": task.plot_alignment_to_numpy(attn[0, 0].data.cpu().numpy()),
# }
# task.summarize(writer=writer, global_step=global_step, images=image_dict, scalars=scalar_dict, sample_rate=hps.data.sample_rate)
# Save checkpoint on CPU to prevent GPU OOM
if global_step % hps.train.eval_interval == 0:
# evaluate(hps, net_g, eval_loader, writer_eval)
task.save_checkpoint(net_g, optim_g, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "G_{}.pth".format(global_step)))
task.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
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 = spectral_norm(spec) if hps.data.use_mel else spec_to_mel(spec, hps.data.n_fft, hps.data.n_mels, hps.data.sample_rate, hps.data.f_min, hps.data.f_max)
y_hat_mel = wav_to_mel(y_hat.squeeze(1).float(), hps.data.n_fft, hps.data.n_mels, hps.data.sample_rate, hps.data.hop_length, hps.data.win_length, hps.data.f_min, hps.data.f_max)
image_dict = {"gen/mel": task.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": task.plot_spectrogram_to_numpy(mel[0].cpu().numpy())})
audio_dict.update({"gt/audio": y[0, :, : y_lengths[0]]})
task.summarize(writer=writer_eval, global_step=global_step, images=image_dict, audios=audio_dict, sample_rate=hps.data.sample_rate)
generator.train()
if __name__ == "__main__":
main()