GPT-SoVITS/GPT_SoVITS/s2_train_v3_lora.py

import warnings

warnings.filterwarnings("ignore")
import os

import utils

hps = utils.get_hparams(stage=2)
os.environ["CUDA_VISIBLE_DEVICES"] = hps.train.gpu_numbers.replace("-", ",")
import logging

import torch
import torch.distributed as dist
import torch.multiprocessing as mp
from torch.cuda.amp import GradScaler, autocast
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm

logging.getLogger("matplotlib").setLevel(logging.INFO)
logging.getLogger("h5py").setLevel(logging.INFO)
logging.getLogger("numba").setLevel(logging.INFO)
from collections import OrderedDict as od
from random import randint

from module import commons
from module.data_utils import (
    DistributedBucketSampler,
)
from module.data_utils import (
    TextAudioSpeakerCollateV3 as TextAudioSpeakerCollate,
)
from module.data_utils import (
    TextAudioSpeakerLoaderV3 as TextAudioSpeakerLoader,
)
from module.models import (
    SynthesizerTrnV3 as SynthesizerTrn,
)
from peft import LoraConfig, get_peft_model
from process_ckpt import savee

torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = False
###反正A100fp32更快，那试试tf32吧
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
torch.set_float32_matmul_precision("medium")  # 最低精度但最快（也就快一丁点），对于结果造成不了影响
# from config import pretrained_s2G,pretrained_s2D
global_step = 0

device = "cpu"  # cuda以外的设备，等mps优化后加入


def main():
    if torch.cuda.is_available():
        n_gpus = torch.cuda.device_count()
    else:
        n_gpus = 1
    os.environ["MASTER_ADDR"] = "localhost"
    os.environ["MASTER_PORT"] = str(randint(20000, 55555))

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


def run(rank, n_gpus, hps):
    global global_step, no_grad_names, save_root, lora_rank
    if rank == 0:
        logger = utils.get_logger(hps.data.exp_dir)
        logger.info(hps)
        # utils.check_git_hash(hps.s2_ckpt_dir)
        writer = SummaryWriter(log_dir=hps.s2_ckpt_dir)
        writer_eval = SummaryWriter(log_dir=os.path.join(hps.s2_ckpt_dir, "eval"))

    dist.init_process_group(
        backend="gloo" if os.name == "nt" or not torch.cuda.is_available() else "nccl",
        init_method="env://?use_libuv=False",
        world_size=n_gpus,
        rank=rank,
    )
    torch.manual_seed(hps.train.seed)
    if torch.cuda.is_available():
        torch.cuda.set_device(rank)

    train_dataset = TextAudioSpeakerLoader(hps.data)  ########
    train_sampler = DistributedBucketSampler(
        train_dataset,
        hps.train.batch_size,
        [
            32,
            300,
            400,
            500,
            600,
            700,
            800,
            900,
            1000,
            # 1100,
            # 1200,
            # 1300,
            # 1400,
            # 1500,
            # 1600,
            # 1700,
            # 1800,
            # 1900,
        ],
        num_replicas=n_gpus,
        rank=rank,
        shuffle=True,
    )
    collate_fn = TextAudioSpeakerCollate()
    train_loader = DataLoader(
        train_dataset,
        num_workers=6,
        shuffle=False,
        pin_memory=True,
        collate_fn=collate_fn,
        batch_sampler=train_sampler,
        persistent_workers=True,
        prefetch_factor=4,
    )
    save_root = "%s/logs_s2_%s_lora_%s" % (hps.data.exp_dir, hps.model.version, hps.train.lora_rank)
    os.makedirs(save_root, exist_ok=True)
    lora_rank = int(hps.train.lora_rank)
    lora_config = LoraConfig(
        target_modules=["to_k", "to_q", "to_v", "to_out.0"],
        r=lora_rank,
        lora_alpha=lora_rank,
        init_lora_weights=True,
    )

    def get_model(hps):
        return SynthesizerTrn(
            hps.data.filter_length // 2 + 1,
            hps.train.segment_size // hps.data.hop_length,
            n_speakers=hps.data.n_speakers,
            **hps.model,
        )

    def get_optim(net_g):
        return torch.optim.AdamW(
            filter(lambda p: p.requires_grad, net_g.parameters()),  ###默认所有层lr一致
            hps.train.learning_rate,
            betas=hps.train.betas,
            eps=hps.train.eps,
        )

    def model2cuda(net_g, rank):
        if torch.cuda.is_available():
            net_g = DDP(net_g.cuda(rank), device_ids=[rank], find_unused_parameters=True)
        else:
            net_g = net_g.to(device)
        return net_g

    try:  # 如果能加载自动resume
        net_g = get_model(hps)
        net_g.cfm = get_peft_model(net_g.cfm, lora_config)
        net_g = model2cuda(net_g, rank)
        optim_g = get_optim(net_g)
        # _, _, _, 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(save_root, "G_*.pth"),
            net_g,
            optim_g,
        )
        epoch_str += 1
        global_step = (epoch_str - 1) * len(train_loader)
    except:  # 如果首次不能加载，加载pretrain
        # traceback.print_exc()
        epoch_str = 1
        global_step = 0
        net_g = get_model(hps)
        if (
            hps.train.pretrained_s2G != ""
            and hps.train.pretrained_s2G != None
            and os.path.exists(hps.train.pretrained_s2G)
        ):
            if rank == 0:
                logger.info("loaded pretrained %s" % hps.train.pretrained_s2G)
            print(
                "loaded pretrained %s" % hps.train.pretrained_s2G,
                net_g.load_state_dict(
                    torch.load(hps.train.pretrained_s2G, map_location="cpu")["weight"],
                    strict=False,
                ),
            )
        net_g.cfm = get_peft_model(net_g.cfm, lora_config)
        net_g = model2cuda(net_g, rank)
        optim_g = get_optim(net_g)

    no_grad_names = set()
    for name, param in net_g.named_parameters():
        if not param.requires_grad:
            no_grad_names.add(name.replace("module.", ""))
            # print(name, "not requires_grad")
    # print(no_grad_names)
    # os._exit(233333)

    scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hps.train.lr_decay, last_epoch=-1)
    for _ in range(epoch_str):
        scheduler_g.step()

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

    net_d = optim_d = scheduler_d = None
    print("start training from epoch %s" % epoch_str)
    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])
                [train_loader, None],
                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()
    print("training done")


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()
    for batch_idx, (ssl, spec, mel, ssl_lengths, spec_lengths, text, text_lengths, mel_lengths) in enumerate(
        tqdm(train_loader)
    ):
        if torch.cuda.is_available():
            spec, spec_lengths = (
                spec.cuda(
                    rank,
                    non_blocking=True,
                ),
                spec_lengths.cuda(
                    rank,
                    non_blocking=True,
                ),
            )
            mel, mel_lengths = mel.cuda(rank, non_blocking=True), mel_lengths.cuda(rank, non_blocking=True)
            ssl = ssl.cuda(rank, non_blocking=True)
            ssl.requires_grad = False
            text, text_lengths = (
                text.cuda(
                    rank,
                    non_blocking=True,
                ),
                text_lengths.cuda(
                    rank,
                    non_blocking=True,
                ),
            )
        else:
            spec, spec_lengths = spec.to(device), spec_lengths.to(device)
            mel, mel_lengths = mel.to(device), mel_lengths.to(device)
            ssl = ssl.to(device)
            ssl.requires_grad = False
            text, text_lengths = text.to(device), text_lengths.to(device)

        with autocast(enabled=hps.train.fp16_run):
            cfm_loss = net_g(
                ssl,
                spec,
                mel,
                ssl_lengths,
                spec_lengths,
                text,
                text_lengths,
                mel_lengths,
                use_grad_ckpt=hps.train.grad_ckpt,
            )
            loss_gen_all = cfm_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 = [cfm_loss]
                logger.info("Train Epoch: {} [{:.0f}%]".format(epoch, 100.0 * batch_idx / len(train_loader)))
                logger.info([x.item() for x in losses] + [global_step, lr])

                scalar_dict = {"loss/g/total": loss_gen_all, "learning_rate": lr, "grad_norm_g": grad_norm_g}
                utils.summarize(
                    writer=writer,
                    global_step=global_step,
                    scalars=scalar_dict,
                )

        global_step += 1
    if epoch % hps.train.save_every_epoch == 0 and rank == 0:
        if hps.train.if_save_latest == 0:
            utils.save_checkpoint(
                net_g,
                optim_g,
                hps.train.learning_rate,
                epoch,
                os.path.join(save_root, "G_{}.pth".format(global_step)),
            )
        else:
            utils.save_checkpoint(
                net_g,
                optim_g,
                hps.train.learning_rate,
                epoch,
                os.path.join(save_root, "G_{}.pth".format(233333333333)),
            )
        if rank == 0 and hps.train.if_save_every_weights == True:
            if hasattr(net_g, "module"):
                ckpt = net_g.module.state_dict()
            else:
                ckpt = net_g.state_dict()
            sim_ckpt = od()
            for key in ckpt:
                # if "cfm"not in key:
                #     print(key)
                if key not in no_grad_names:
                    sim_ckpt[key] = ckpt[key].half().cpu()
            logger.info(
                "saving ckpt %s_e%s:%s"
                % (
                    hps.name,
                    epoch,
                    savee(
                        sim_ckpt,
                        hps.name + "_e%s_s%s_l%s" % (epoch, global_step, lora_rank),
                        epoch,
                        global_step,
                        hps,
                        lora_rank=lora_rank,
                    ),
                )
            )

    if rank == 0:
        logger.info("====> Epoch: {}".format(epoch))


if __name__ == "__main__":
    main()