train_nsf_sim_cache_sid_load_pretrain.py

import sys, os

now_dir = os.getcwd()
sys.path.append(os.path.join(now_dir, "train"))
import utils

hps = utils.get_hparams()
os.environ["CUDA_VISIBLE_DEVICES"] = hps.gpus.replace("-", ",")
n_gpus = len(hps.gpus.split("-"))
from random import shuffle
import traceback, json, argparse, itertools, math, torch, pdb

torch.backends.cudnn.deterministic = False
torch.backends.cudnn.benchmark = False
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 infer_pack import commons

from time import time as ttime
from data_utils import (
    TextAudioLoaderMultiNSFsid,
    TextAudioLoader,
    TextAudioCollateMultiNSFsid,
    TextAudioCollate,
    DistributedBucketSampler,
)
from infer_pack.models import (
    SynthesizerTrnMs256NSFsid,
    SynthesizerTrnMs256NSFsid_nono,
    MultiPeriodDiscriminator,
)
from losses import generator_loss, discriminator_loss, feature_loss, kl_loss
from mel_processing import mel_spectrogram_torch, spec_to_mel_torch


global_step = 0


def main():
    # n_gpus = torch.cuda.device_count()
    os.environ["MASTER_ADDR"] = "localhost"
    os.environ["MASTER_PORT"] = "5555"

    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)
    if torch.cuda.is_available():
        torch.cuda.set_device(rank)

    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=rank,
        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=4,
        shuffle=False,
        pin_memory=True,
        collate_fn=collate_fn,
        batch_sampler=train_sampler,
        persistent_workers=True,
        prefetch_factor=8,
    )
    if hps.if_f0 == 1:
        net_g = SynthesizerTrnMs256NSFsid(
            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 = SynthesizerTrnMs256NSFsid_nono(
            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(rank)
    net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm)
    if torch.cuda.is_available():
        net_d = net_d.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], 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=[rank])
        net_d = DDP(net_d, device_ids=[rank])
    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:
            logger.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 rank == 0:
            logger.info("loaded pretrained %s %s" % (hps.pretrainG, hps.pretrainD))
        print(
            net_g.module.load_state_dict(
                torch.load(hps.pretrainG, map_location="cpu")["model"]
            )
        )  ##测试不加载优化器
        print(
            net_d.module.load_state_dict(
                torch.load(hps.pretrainD, map_location="cpu")["model"]
            )
        )

    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)

    cache = []
    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, None],
                logger,
                [writer, writer_eval],
                cache,
            )
        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,
                cache,
            )
        scheduler_g.step()
        scheduler_d.step()


def train_and_evaluate(
    rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, 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()
    if cache == [] or hps.if_cache_data_in_gpu == False:  # 第一个epoch把cache全部填满训练集
        # print("caching")
        for batch_idx, info in enumerate(train_loader):
            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
            if torch.cuda.is_available():
                phone, phone_lengths = phone.cuda(
                    rank, non_blocking=True
                ), phone_lengths.cuda(rank, non_blocking=True)
                if hps.if_f0 == 1:
                    pitch, pitchf = pitch.cuda(rank, non_blocking=True), pitchf.cuda(
                        rank, non_blocking=True
                    )
                sid = sid.cuda(rank, non_blocking=True)
                spec, spec_lengths = spec.cuda(
                    rank, non_blocking=True
                ), spec_lengths.cuda(rank, non_blocking=True)
                wave, wave_lengths = wave.cuda(
                    rank, non_blocking=True
                ), wave_lengths.cuda(rank, non_blocking=True)
            if hps.if_cache_data_in_gpu == True:
                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,
                            ),
                        )
                    )
            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 rank == 0:
                if global_step % hps.train.log_interval == 0:
                    lr = optim_g.param_groups[0]["lr"]
                    logger.info(
                        "Train Epoch: {} [{:.0f}%]".format(
                            epoch, 100.0 * batch_idx / len(train_loader)
                        )
                    )
                    # Amor For Tensorboard display
                    if loss_mel > 50:
                        loss_mel = 50
                    if loss_kl > 5:
                        loss_kl = 5

                    logger.info([global_step, lr])
                    logger.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
        # if global_step % hps.train.eval_interval == 0:
        if epoch % hps.save_every_epoch == 0 and rank == 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)),
                )

    else:  # 后续的epoch直接使用打乱的cache
        shuffle(cache)
        # print("using cache")
        for batch_idx, info in cache:
            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
            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 rank == 0:
                if global_step % hps.train.log_interval == 0:
                    lr = optim_g.param_groups[0]["lr"]
                    logger.info(
                        "Train Epoch: {} [{:.0f}%]".format(
                            epoch, 100.0 * batch_idx / len(train_loader)
                        )
                    )
                    # Amor For Tensorboard display
                    if loss_mel > 50:
                        loss_mel = 50
                    if loss_kl > 5:
                        loss_kl = 5

                    logger.info([global_step, lr])
                    logger.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
        # if global_step % hps.train.eval_interval == 0:
        if epoch % hps.save_every_epoch == 0 and rank == 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 rank == 0:
        logger.info("====> Epoch: {}".format(epoch))
    if epoch >= hps.total_epoch and rank == 0:
        logger.info("Training is done. The program is closed.")
        from process_ckpt import savee  # def savee(ckpt,sr,if_f0,name,epoch):

        if hasattr(net_g, "module"):
            ckpt = net_g.module.state_dict()
        else:
            ckpt = net_g.state_dict()
        logger.info(
            "saving final ckpt:%s"
            % (savee(ckpt, hps.sample_rate, hps.if_f0, hps.name, epoch))
        )
        os._exit(2333333)


if __name__ == "__main__":
    main()