train_nsf_sim_cache_sid_load_pretrain.py

import os

from lib.train import utils
import datetime

from random import shuffle

import torch

torch.backends.cudnn.deterministic = False
torch.backends.cudnn.benchmark = False
from torch.nn import functional as F
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from torch.nn.parallel import DistributedDataParallel as DDP # TODO: remove
from torch.cuda.amp import autocast, GradScaler
from lib.infer_pack import commons
from time import sleep
from time import time as ttime
from lib.train.data_utils import (
    TextAudioLoaderMultiNSFsid,
    TextAudioLoader,
    TextAudioCollateMultiNSFsid,
    TextAudioCollate,
    DistributedBucketSampler, # TODO: remove?
)

# if hps.version == "v1":
from lib.infer_pack.models import (
    SynthesizerTrnMs256NSFsid as RVC_Model_f0,
    SynthesizerTrnMs256NSFsid_nono as RVC_Model_nof0,
    MultiPeriodDiscriminator,
)
# else:
#     from lib.infer_pack.models import (
#         SynthesizerTrnMs768NSFsid as RVC_Model_f0,
#         SynthesizerTrnMs768NSFsid_nono as RVC_Model_nof0,
#         MultiPeriodDiscriminatorV2 as MultiPeriodDiscriminator,
#     )
from lib.train.losses import generator_loss, discriminator_loss, feature_loss, kl_loss
from lib.train.mel_processing import mel_spectrogram_torch, spec_to_mel_torch
from lib.train.process_ckpt import savee
from ltxcloudapi import get_logger

log = get_logger(__name__, log_level="INFO")

global_step = 0


class EpochRecorder:
    def __init__(self):
        self.last_time = ttime()

    def record(self):
        now_time = ttime()
        elapsed_time = now_time - self.last_time
        self.last_time = now_time
        elapsed_time_str = str(datetime.timedelta(seconds=elapsed_time))
        current_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
        return f"[{current_time}] | ({elapsed_time_str})"


def train_nsf_etc(arg_string, update_start_epoch):
    hps = utils.get_hparams(args_list=arg_string.split(" "))
    assert hps.version == "v1" # Import is not correct if this is not the case.
    assert torch.cuda.device_count() == 1
    run(1, hps, update_start_epoch)


def run(n_gpus, hps, update_start_epoch):
    global global_step
    log.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"))

    torch.manual_seed(hps.train.seed)

    if hps.if_f0 == 1:
        train_dataset = TextAudioLoaderMultiNSFsid(hps.data.training_files, hps.data)
    else:
        train_dataset = TextAudioLoader(hps.data.training_files, hps.data)
    train_sampler = DistributedBucketSampler(
        train_dataset,
        hps.train.batch_size * n_gpus,
        # [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1200,1400],  # 16s
        [100, 200, 300, 400, 500, 600, 700, 800, 900],  # 16s
        num_replicas=n_gpus,
        rank=0,
        shuffle=True,
    )
    # It is possible that dataloader's workers are out of shared memory. Please try to raise your shared memory limit.
    # num_workers=8 -> num_workers=4
    if hps.if_f0 == 1:
        collate_fn = TextAudioCollateMultiNSFsid()
    else:
        collate_fn = TextAudioCollate()
    train_loader = DataLoader(
        train_dataset,
        num_workers=0,
        shuffle=False,
        pin_memory=True,
        collate_fn=collate_fn,
        batch_sampler=train_sampler,
        persistent_workers=False,
        prefetch_factor=None,
    )
    if hps.if_f0 == 1:
        net_g = RVC_Model_f0(
            hps.data.filter_length // 2 + 1,
            hps.train.segment_size // hps.data.hop_length,
            **hps.model,
            is_half=hps.train.fp16_run,
            sr=hps.sample_rate,
        )
    else:
        net_g = RVC_Model_nof0(
            hps.data.filter_length // 2 + 1,
            hps.train.segment_size // hps.data.hop_length,
            **hps.model,
            is_half=hps.train.fp16_run,
        )
    if torch.cuda.is_available():
        net_g = net_g.cuda(0)
    net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm)
    if torch.cuda.is_available():
        net_d = net_d.cuda(0)
    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], find_unused_parameters=True)
    # net_d = DDP(net_d, device_ids=[rank], find_unused_parameters=True)
    if torch.cuda.is_available():
        net_g = DDP(net_g, device_ids=[0])
        net_d = DDP(net_d, device_ids=[0])
    else:
        net_g = DDP(net_g)
        net_d = DDP(net_d)

    # try:  # 如果能加载自动resume
    #     _, _, _, epoch_str = utils.load_checkpoint(
    #         utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d, optim_d
    #     )  # D多半加载没事
    #     if rank == 0:
    #         log.info("loaded D")
    #     # _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, optim_g,load_opt=0)
    #     _, _, _, epoch_str = utils.load_checkpoint(
    #         utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, optim_g
    #     )
    #     global_step = (epoch_str - 1) * len(train_loader)
    #     # epoch_str = 1
    #     # global_step = 0
    # except:  # 如果首次不能加载，加载pretrain
        
    # traceback.print_exc()
    epoch_str = 1
    global_step = 0
    if hps.pretrainG != "":
        log.info("loading pretrained %s" % (hps.pretrainG))
        result = net_g.module.load_state_dict(
            torch.load(hps.pretrainG, map_location="cpu")["model"]
        )
        log.info(result)

    if hps.pretrainD != "":
        log.info("loading pretrained %s" % (hps.pretrainD))
        result = net_d.module.load_state_dict(
            torch.load(hps.pretrainD, map_location="cpu")["model"]
        )
        log.info(result)

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

    cache = []
    for epoch in range(epoch_str, hps.total_epoch + 1):
        update_start_epoch(epoch)
        train_and_evaluate(
            epoch,
            hps,
            [net_g, net_d],
            [optim_g, optim_d],
            scaler,
            [train_loader, None],
            [writer, writer_eval],
            cache,
        )


def train_and_evaluate(
    epoch, hps, nets, optims, scaler, loaders, writers, cache
):
    net_g, net_d = nets
    optim_g, optim_d = optims
    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()

    # Prepare data iterator
    if hps.if_cache_data_in_gpu == True:
        # Use Cache
        data_iterator = cache
        if cache == []:
            # Make new cache
            for batch_idx, info in enumerate(train_loader):
                # Unpack
                if hps.if_f0 == 1:
                    (
                        phone,
                        phone_lengths,
                        pitch,
                        pitchf,
                        spec,
                        spec_lengths,
                        wave,
                        wave_lengths,
                        sid,
                    ) = info
                else:
                    (
                        phone,
                        phone_lengths,
                        spec,
                        spec_lengths,
                        wave,
                        wave_lengths,
                        sid,
                    ) = info
                # Load on CUDA
                if torch.cuda.is_available():
                    phone = phone.cuda(0, non_blocking=True)
                    phone_lengths = phone_lengths.cuda(0, non_blocking=True)
                    if hps.if_f0 == 1:
                        pitch = pitch.cuda(0, non_blocking=True)
                        pitchf = pitchf.cuda(0, non_blocking=True)
                    sid = sid.cuda(0, non_blocking=True)
                    spec = spec.cuda(0, non_blocking=True)
                    spec_lengths = spec_lengths.cuda(0, non_blocking=True)
                    wave = wave.cuda(0, non_blocking=True)
                    wave_lengths = wave_lengths.cuda(0, non_blocking=True)
                # Cache on list
                if hps.if_f0 == 1:
                    cache.append(
                        (
                            batch_idx,
                            (
                                phone,
                                phone_lengths,
                                pitch,
                                pitchf,
                                spec,
                                spec_lengths,
                                wave,
                                wave_lengths,
                                sid,
                            ),
                        )
                    )
                else:
                    cache.append(
                        (
                            batch_idx,
                            (
                                phone,
                                phone_lengths,
                                spec,
                                spec_lengths,
                                wave,
                                wave_lengths,
                                sid,
                            ),
                        )
                    )
        else:
            # Load shuffled cache
            shuffle(cache)
    else:
        # Loader
        data_iterator = enumerate(train_loader)

    # Run steps
    epoch_recorder = EpochRecorder()
    for batch_idx, info in data_iterator:
        # Data
        ## Unpack
        if hps.if_f0 == 1:
            (
                phone,
                phone_lengths,
                pitch,
                pitchf,
                spec,
                spec_lengths,
                wave,
                wave_lengths,
                sid,
            ) = info
        else:
            phone, phone_lengths, spec, spec_lengths, wave, wave_lengths, sid = info
        ## Load on CUDA
        if (hps.if_cache_data_in_gpu == False) and torch.cuda.is_available():
            phone = phone.cuda(0, non_blocking=True)
            phone_lengths = phone_lengths.cuda(0, non_blocking=True)
            if hps.if_f0 == 1:
                pitch = pitch.cuda(0, non_blocking=True)
                pitchf = pitchf.cuda(0, non_blocking=True)
            sid = sid.cuda(0, non_blocking=True)
            spec = spec.cuda(0, non_blocking=True)
            spec_lengths = spec_lengths.cuda(0, non_blocking=True)
            wave = wave.cuda(0, non_blocking=True)
            # wave_lengths = wave_lengths.cuda(rank, non_blocking=True)

        # Calculate
        with autocast(enabled=hps.train.fp16_run):
            if hps.if_f0 == 1:
                (
                    y_hat,
                    ids_slice,
                    x_mask,
                    z_mask,
                    (z, z_p, m_p, logs_p, m_q, logs_q),
                ) = net_g(phone, phone_lengths, pitch, pitchf, spec, spec_lengths, sid)
            else:
                (
                    y_hat,
                    ids_slice,
                    x_mask,
                    z_mask,
                    (z, z_p, m_p, logs_p, m_q, logs_q),
                ) = net_g(phone, phone_lengths, spec, spec_lengths, sid)
            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
            )
            with autocast(enabled=False):
                y_hat_mel = mel_spectrogram_torch(
                    y_hat.float().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,
                )
            if hps.train.fp16_run == True:
                y_hat_mel = y_hat_mel.half()
            wave = commons.slice_segments(
                wave, ids_slice * hps.data.hop_length, hps.train.segment_size
            )  # slice

            # Discriminator
            y_d_hat_r, y_d_hat_g, _, _ = net_d(wave, 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
                )
        optim_d.zero_grad()
        scaler.scale(loss_disc).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(wave, 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 + 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 global_step % hps.train.log_interval == 0:
            lr = optim_g.param_groups[0]["lr"]
            log.info(
                "Train Epoch: {} [{:.0f}%]".format(
                    epoch, 100.0 * batch_idx / len(train_loader)
                )
            )
            # Amor For Tensorboard display
            if loss_mel > 75:
                loss_mel = 75
            if loss_kl > 9:
                loss_kl = 9

            log.info([global_step, lr])
            log.info(
                f"loss_disc={loss_disc:.3f}, loss_gen={loss_gen:.3f}, loss_fm={loss_fm:.3f},loss_mel={loss_mel:.3f}, loss_kl={loss_kl:.3f}"
            )
            scalar_dict = {
                "loss/g/total": loss_gen_all,
                "loss/d/total": loss_disc,
                "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": 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()
                ),
            }
            utils.summarize(
                writer=writer,
                global_step=global_step,
                images=image_dict,
                scalars=scalar_dict,
            )
        global_step += 1
    # /Run steps

    if hps.save_every_epoch > 0 and epoch % hps.save_every_epoch == 0:
        if hps.if_latest == 0:
            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)),
            )
        else:
            utils.save_checkpoint(
                net_g,
                optim_g,
                hps.train.learning_rate,
                epoch,
                os.path.join(hps.model_dir, "G_{}.pth".format(2333333)),
            )
            utils.save_checkpoint(
                net_d,
                optim_d,
                hps.train.learning_rate,
                epoch,
                os.path.join(hps.model_dir, "D_{}.pth".format(2333333)),
            )
        if hps.save_every_weights == "1":
            if hasattr(net_g, "module"):
                ckpt = net_g.module.state_dict()
            else:
                ckpt = net_g.state_dict()
            log.info(
                "saving ckpt %s_e%s:%s"
                % (
                    hps.name,
                    epoch,
                    savee(
                        hps.export_dir,
                        ckpt,
                        hps.sample_rate,
                        hps.if_f0,
                        hps.name + "_e%s_s%s" % (epoch, global_step),
                        epoch,
                        hps.version,
                        hps,
                    ),
                )
            )

    log.info("====> Epoch: {} {}".format(epoch, epoch_recorder.record()))
    if epoch >= hps.total_epoch:
        log.info("Training is done. The program is closed.")

        if hasattr(net_g, "module"):
            ckpt = net_g.module.state_dict()
        else:
            ckpt = net_g.state_dict()
        result = savee(hps.export_dir, ckpt, hps.sample_rate, hps.if_f0, hps.name, epoch, hps.version, hps)
        log.info(f"saved final ckpt: {result}")