train_ddp_dataloader_segment_submit_success_v21.py

import argparse
import os
import math
import torch
import yaml
import torch.nn as nn
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
from torch.utils.data.distributed import DistributedSampler
from torch.nn.parallel import DistributedDataParallel as DDP
from pprint import pprint
from datasets.datasets_dataloader_segment import OneshotVcDataset
from utils.model import get_model, get_vocoder, get_param_num
from utils.tools import to_device, log, synth_one_sample
# from model import FastSpeech2Loss
from model.loss_dataloader_segment import DisentangleLoss
# from dataset import Dataset
from utils.dist import ompi_rank, ompi_size, ompi_local_rank, dist_init
from utils.tools import print_rank
from evaluate_dataloader_segment import evaluate



def is_parallel_model(model):
    if isinstance(model, torch.nn.DataParallel) or \
       isinstance(model, torch.nn.parallel.DistributedDataParallel):
        return True
    else:
        return False

def prepare_directories_and_logger(output_directory, log_directory, rank):
    if rank == 0:
        if not os.path.isdir(output_directory):
            os.makedirs(output_directory)
            os.chmod(output_directory, 0o775)
        train_log_path = os.path.join(log_directory, "train")
        val_log_path = os.path.join(log_directory, "val")
        if not os.path.isdir(train_log_path):
            os.makedirs(train_log_path)
        if not os.path.isdir(val_log_path):
            os.makedirs(val_log_path)                
        # logger = Tacotron2Logger(os.path.join(output_directory, log_directory))
        train_logger = SummaryWriter(train_log_path)
        val_logger = SummaryWriter(val_log_path)
    else:
        train_logger = None
        val_logger = None
        train_log_path = ''
        val_log_path = ''
    # return logger
    return train_logger, val_logger, train_log_path, val_log_path


# def main(args, configs, log_dir):
def main(args, output_directory, log_directory, configs):
    preprocess_config, model_config, train_config = configs

    ############################# setting ddp #########################

    torch.backends.cudnn.enabled = train_config["ddp"]["cudnn_enabled"]
    torch.backends.cudnn.benchmark = train_config["ddp"]["cudnn_benchmark"]


    rank=0
    world_size=1
    local_rank='group name'

    # init distributed env
    if train_config["ddp"]["distributed_run"]:
        # dist_init(backend='nccl')
        torch.distributed.init_process_group(backend='nccl')
        rank = ompi_rank()
        # local_rank = ompi_local_rank()
        local_rank = torch.distributed.get_rank()
        # world_size = torch.cuda.device_count()
        world_size = ompi_size()
        if rank == 0:
            print('[Rank 0]: DistributedDataParallel PyTorch Method')
        torch.cuda.set_device(local_rank)
        device = torch.device("cuda", local_rank)
        print(rank, local_rank, world_size, flush=True)  
    else:
        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")  
    if rank == 0:
        # print("Dynamic Loss Scaling:", hparams.dynamic_loss_scaling)
        print("Distributed Run:", train_config["ddp"]["distributed_run"])
        print("cuDNN Enabled:", train_config["ddp"]["cudnn_enabled"])
        print("cuDNN Benchmark:", train_config["ddp"]["cudnn_benchmark"])
        print('Final parsed hparams:')
        # pprint(hps.values())
    ############################# setting ddp #########################



    print("Prepare training ...")
    torch.manual_seed(train_config["ddp"]["seed"])
    torch.cuda.manual_seed(train_config["ddp"]["seed"])    
    # Get dataset
    # dataset = Dataset(
    #     "train.txt", preprocess_config, train_config, sort=True, drop_last=True
    # )
    # batch_size = train_config["optimizer"]["batch_size"]
    # group_size = 4  # Set this larger than 1 to enable sorting in Dataset
    # assert batch_size * group_size < len(dataset)
    # loader = DataLoader(
    #     dataset,
    #     batch_size=batch_size * group_size,
    #     shuffle=True,
    #     collate_fn=dataset.collate_fn,
    # )
    # train_set = VCDataset(hps.Audio.data_dir, hps.Audio.train_meta_file)
    train_set = OneshotVcDataset(
        meta_file= preprocess_config["data"]["train_fid_list"],
        vctk_wav_dir= preprocess_config["data"]["vctk_wav_dir"],
        vctk_mel_dir= preprocess_config["data"]["vctk_mel_dir"],
        vctk_spk_dvec_dir= preprocess_config["data"]["vctk_spk_dvec_dir"],
        min_max_norm_mel = preprocess_config["data"]["min_max_norm_mel"],
        mel_min = preprocess_config["data"]["mel_min"],
        mel_max = preprocess_config["data"]["mel_max"],
        wav_file_ext = preprocess_config["data"]["wav_file_ext"],
        mel_file_ext = preprocess_config["data"]["mel_file_ext"]
    )


    if train_config["ddp"]["distributed_run"]:
        train_sampler = DistributedSampler(train_set)
        shuffle = False
    else:
        train_sampler = None
        shuffle = True

    train_loader = DataLoader(train_set, batch_size=train_config["optimizer"]["batch_size"], 
                                sampler=train_sampler, shuffle=shuffle,
                                num_workers=train_config["ddp"]["num_workers"],pin_memory=True, drop_last=False)

    # Prepare model
    model, optimizer = get_model(args, configs, device, train=True)
    print_rank("trying to move the model to GPU")
    # Move it to GPU if you can
    # model.cuda() if torch.cuda.is_available() else model.cpu()
    model.to(device)
    print_rank("moved the model to GPU")
    print_rank("model: {}".format(model))
    if train_config["ddp"]["distributed_run"]:
        model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
        model = DDP(
            model, device_ids=[local_rank], output_device=local_rank,
            find_unused_parameters=True)
    num_param = get_param_num(model)
    Loss = DisentangleLoss(preprocess_config, model_config, train_config).to(device)
    print("Number of SpeechDecompose Parameters:", num_param)

    # Init logger
    for p in train_config["path"].values():
        os.makedirs(p, exist_ok=True)
    # train_log_path = os.path.join(train_config["path"]["log_path"], "train")
    # val_log_path = os.path.join(train_config["path"]["log_path"], "val") log_directory
    # train_log_path = os.path.join(log_directory, "train")
    # val_log_path = os.path.join(log_directory, "val")    
    # os.makedirs(train_log_path, exist_ok=True)
    # os.makedirs(val_log_path, exist_ok=True)
    # train_logger = SummaryWriter(train_log_path)
    # val_logger = SummaryWriter(val_log_path)
    train_logger, val_logger, train_log_path, val_log_path = prepare_directories_and_logger(output_directory, log_directory, rank)
    # Training
    step = args.restore_step + 1
    epoch = 1
    grad_acc_step = train_config["optimizer"]["grad_acc_step"]
    grad_clip_thresh = train_config["optimizer"]["grad_clip_thresh"]
    total_step = train_config["step"]["total_step"]
    log_step = train_config["step"]["log_step"]
    save_step = train_config["step"]["save_step"]
    synth_step = train_config["step"]["synth_step"]
    val_step = train_config["step"]["val_step"]

    outer_bar = tqdm(total=total_step, desc="Training", position=0)
    outer_bar.n = args.restore_step
    outer_bar.update()
    model.train()
    while step < total_step:
        if train_config["ddp"]["distributed_run"]:
            train_loader.sampler.set_epoch(epoch)        
        inner_bar = tqdm(total=len(train_loader), desc="Epoch {}".format(epoch), position=1)
        for batch in train_loader:
            # mel = batch[0].to(device)
        # for batch in batchs:   # 每一个sample？
            # if is_parallel_model(model):
            #     batch = model.module.parse_batch(mel, batch, device)
            # else:
            #     batch = model.parse_batch(mel, batch, device)    
            # batch = model.parse_batch(batch)                       
            # Forward
            # output = model(*(batch[2:]))
            mel, speaker_embeddings, fid = batch
            mel = mel.to(device)
            speaker_embeddings = speaker_embeddings.to(device)
            # batch = (mel, speaker_embeddings, fid)
            ################# 3 mel input ###############
            mel_content = mel
            mel_spk = mel
            mel_style = mel
            mel_autoencoder = mel
            batch = (mel_content, mel_spk, mel_style, mel_autoencoder, speaker_embeddings, fid)
            ################# 3 mel input ###############

            # batch = [i.to(device) for i in batch]
            # for i in batch:
            #     print(i)
            output = model(*(batch))

            # Cal Loss
            # print("mel", mel.device, flush=True)    # cuda 0
            # print("batch", batch.device, flush=True)
            # print("output", output.device, flush=True)
            losses, lambda_kl = Loss(batch, output, step)  # step for annealing
            # print("losses", losses.device, flush=True)
            # print("losses", losses)
            # losses : total_loss, mel_loss, postnet_mel_loss, stop loss
            total_loss = losses[0]

            # Backward
            total_loss = total_loss / grad_acc_step
            # total_loss.backward()
            if train_config["ddp"]["fp16_run"]:
                with amp.scale_loss(total_loss, optimizer) as scaled_loss:
                    scaled_loss.backward()
            else:
                total_loss.backward()
            if step % grad_acc_step == 0:
                if train_config["ddp"]["fp16_run"]:
                    grad_norm = torch.nn.utils.clip_grad_norm_(
                        amp.master_params(optimizer), grad_clip_thresh)
                    is_overflow = math.isnan(grad_norm)
                else:
                    grad_norm = torch.nn.utils.clip_grad_norm_(
                        model.parameters(), grad_clip_thresh)
                # Update weights
                optimizer.step_and_update_lr()
                optimizer.zero_grad()                            
            
            # if step % grad_acc_step == 0:
            #     # Clipping gradients to avoid gradient explosion
            #     nn.utils.clip_grad_norm_(model.parameters(), grad_clip_thresh)

            #     # Update weights
            #     optimizer.step_and_update_lr()
            #     optimizer.zero_grad()

            learning_rate = optimizer._get_lr_scale()
            # print("learning_rate", learning_rate)
            if (rank == 0):
                # Load vocoder
                vocoder = get_vocoder(model_config, device)
                if step % log_step == 0:
                    losses = [l.item() for l in losses]
                    message1 = "Step {}/{}, ".format(step, total_step)
                    message2 = "Total Loss: {:.4f}, Total Mel Loss: {:.4f}, Mel Loss: {:.4f}, Mel PostNet Loss: {:.4f}, Style KL loss: {:.4f}, Content VQ Loss: {:.4f}".format(
                        *losses
                    )
                    message3 = "Content KL lambda :{:.4f}".format(lambda_kl)

                    with open(os.path.join(train_log_path, "log.txt"), "a") as f:
                        f.write(message1 + message2 + message3 + "\n")

                    outer_bar.write(message1 + message2)

                    log(train_logger, step, losses=losses, learning_rate=learning_rate, lambda_kl=lambda_kl)

                if step % synth_step == 0:
                    fig1, fig2, wav_reconstruction, wav_prediction, tag = synth_one_sample(
                        batch,
                        output,
                        vocoder,
                        model_config,
                        preprocess_config,
                        step
                    )
                    log(
                        train_logger,
                        fig=fig1,
                        tag="Training/step_{}_{}_direct_acoustic_model".format(step, tag),
                        learning_rate=learning_rate,
                    )
                    log(
                        train_logger,
                        fig=fig2,
                        tag="Training/step_{}_{}_extract_from_generated_vocoder".format(step, tag),
                        learning_rate=learning_rate,
                    )                    
                    sampling_rate = preprocess_config["preprocessing"]["audio"][
                        "sampling_rate"
                    ]
                    log(
                        train_logger,
                        audio=wav_reconstruction,
                        sampling_rate=sampling_rate,
                        tag="Training/step_{}_{}_reconstructed".format(step, tag),
                        learning_rate=learning_rate,
                    )
                    log(
                        train_logger,
                        audio=wav_prediction,
                        sampling_rate=sampling_rate,
                        tag="Training/step_{}_{}_synthesized".format(step, tag),
                        learning_rate=learning_rate,
                    )

                if step % val_step == 0:
                    model.eval()
                    message = evaluate(model, step, configs, val_logger, vocoder, learning_rate)
                    with open(os.path.join(val_log_path, "log.txt"), "a") as f:
                        f.write(message + "\n")
                    outer_bar.write(message)

                    model.train()

                if step % save_step == 0:
                    torch.save(
                        {
                            "model": model.module.state_dict(),
                            "optimizer": optimizer._optimizer.state_dict(),
                        },
                        os.path.join(
                            # train_config["path"]["ckpt_path"],
                            output_directory,
                            "{}.pth.tar".format(step),
                        ),
                    )
            step += 1
            outer_bar.update(1)
            inner_bar.update(1)
        epoch += 1

    ############ following ming's fs2 ############
    # while True:
        # if train_config["ddp"]["distributed_run"]:
        #     train_loader.sampler.set_epoch(epoch)        
        # inner_bar = tqdm(total=len(train_loader), desc="Epoch {}".format(epoch), position=1)
        # for batchs in train_loader:
        #     for batch in batchs:   # 每一个sample？
        #         if is_parallel_model(model):
        #             batch = model.module.parse_batch(batch)
        #         else:
        #             batch = model.parse_batch(batch)                 
        #         # batch = to_device(batch, device)
                
        #         # Forward
        #         # output = model(*(batch[2:]))
        #         output = model(*(batch))

        #         # Cal Loss
        #         losses = Loss(batch, output, step)  # step for annealing
        #         # losses : total_loss, mel_loss, postnet_mel_loss, stop loss
        #         total_loss = losses[0]

        #         # Backward
        #         total_loss = total_loss / grad_acc_step
        #         # total_loss.backward()
        #         if train_config["ddp"]["fp16_run"]:
        #             with amp.scale_loss(total_loss, optimizer) as scaled_loss:
        #                 scaled_loss.backward()
        #         else:
        #             total_loss.backward()
        #         if step % grad_acc_step == 0:
        #             if train_config["ddp"]["fp16_run"]:
        #                 grad_norm = torch.nn.utils.clip_grad_norm_(
        #                     amp.master_params(optimizer), grad_clip_thresh)
        #                 is_overflow = math.isnan(grad_norm)
        #             else:
        #                 grad_norm = torch.nn.utils.clip_grad_norm_(
        #                     model.parameters(), grad_clip_thresh)
        #             # Update weights
        #             optimizer.step_and_update_lr()
        #             optimizer.zero_grad()                            
                
        #         # if step % grad_acc_step == 0:
        #         #     # Clipping gradients to avoid gradient explosion
        #         #     nn.utils.clip_grad_norm_(model.parameters(), grad_clip_thresh)

        #         #     # Update weights
        #         #     optimizer.step_and_update_lr()
        #         #     optimizer.zero_grad()

        #         learning_rate = optimizer._get_lr_scale()
        #         if (rank == 0):
        #             if step % log_step == 0:
        #                 losses = [l.item() for l in losses]
        #                 message1 = "Step {}/{}, ".format(step, total_step)
        #                 message2 = "Total Loss: {:.4f}, Mel Loss: {:.4f}, Mel PostNet Loss: {:.4f}, Content KL Loss: {:.4f}, Content KL lambda :{:.4f}".format(
        #                     *losses
        #                 )

        #                 with open(os.path.join(train_log_path, "log.txt"), "a") as f:
        #                     f.write(message1 + message2 + "\n")

        #                 outer_bar.write(message1 + message2)

        #                 log(train_logger, step, losses=losses, learning_rate=learning_rate)

        #             if step % synth_step == 0:
        #                 fig, wav_reconstruction, wav_prediction, tag = synth_one_sample(
        #                     batch,
        #                     output,
        #                     vocoder,
        #                     model_config,
        #                     preprocess_config,
        #                 )
        #                 log(
        #                     train_logger,
        #                     fig=fig,
        #                     tag="Training/step_{}_{}".format(step, tag),
        #                     learning_rate=learning_rate,
        #                 )
        #                 sampling_rate = preprocess_config["preprocessing"]["audio"][
        #                     "sampling_rate"
        #                 ]
        #                 log(
        #                     train_logger,
        #                     audio=wav_reconstruction,
        #                     sampling_rate=sampling_rate,
        #                     tag="Training/step_{}_{}_reconstructed".format(step, tag),
        #                     learning_rate=learning_rate,
        #                 )
        #                 log(
        #                     train_logger,
        #                     audio=wav_prediction,
        #                     sampling_rate=sampling_rate,
        #                     tag="Training/step_{}_{}_synthesized".format(step, tag),
        #                     learning_rate=learning_rate,
        #                 )

        #             if step % val_step == 0:
        #                 model.eval()
        #                 message = evaluate(model, step, configs, val_logger, vocoder)
        #                 with open(os.path.join(val_log_path, "log.txt"), "a") as f:
        #                     f.write(message + "\n")
        #                 outer_bar.write(message)

        #                 model.train()

        #             if step % save_step == 0:
        #                 torch.save(
        #                     {
        #                         "model": model.module.state_dict(),
        #                         "optimizer": optimizer._optimizer.state_dict(),
        #                     },
        #                     os.path.join(
        #                         train_config["path"]["ckpt_path"],
        #                         "{}.pth.tar".format(step),
        #                     ),
        #                 )

        #         if step == total_step:
        #             quit()
        #         step += 1
        #         outer_bar.update(1)
        #     inner_bar.update(1)
        # epoch += 1
    ############ following ming's fs2 ############

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--restore_step", type=int, default=0)
    parser.add_argument(
        "-p",
        "--preprocess_config",
        type=str,
        default='./config/VCTK/preprocess.yaml'
    )
    # parser.add_argument(
    #     "-m", "--model_config", type=str, required=True, help="path to model.yaml"
    # )
    # parser.add_argument(
    #     "-t", "--train_config", type=str, required=True, help="path to train.yaml"
    # )
    parser.add_argument(
        "-m", "--model_config", type=str, default='./config/VCTK/model.yaml'
    )
    parser.add_argument(
        "-t", "--train_config", type=str, default='./config/VCTK/train.yaml'
    )
    ############# distributed ###############
    parser.add_argument('--model-dir',type=str, required=True,
                        help='directory to save checkpoints')
    parser.add_argument('--log-dir', type=str,default=None,
                        help='directory to save tensorboard logs')
    parser.add_argument('-c', '--checkpoint_path', type=str, default=None,
                        required=False, help='checkpoint path')
    parser.add_argument('--warm_start', action='store_true', 
                        help='load model weights only, ignore specified layers')
    parser.add_argument('--hparams', type=str,
                        required=False, help='comma separated name=value pairs')
    parser.add_argument('--hparams_json', type=str,
                        required=False, help='hparams json file')
    parser.add_argument('--local_rank', type=int, default=1,
                        required=False, help='rank of current gpu')
    parser.add_argument('--n_gpus', type=int, default=4,
                        required=False, help='number of gpus')
    ############# distributed ###############  
    # 
    ######### submitter make model and log path #########
    args = parser.parse_args()
    if args.log_dir is not None:
        log_dir = args.log_dir
    else:
        log_dir = os.path.join(args.model_dir, 'log')
    os.makedirs(log_dir, exist_ok=True)
    os.makedirs(args.model_dir, exist_ok=True)   
    ######### submitter make model and log path  #########


    # Read Config
    preprocess_config = yaml.load(
        open(args.preprocess_config, "r"), Loader=yaml.FullLoader
    )
    model_config = yaml.load(open(args.model_config, "r"), Loader=yaml.FullLoader)
    train_config = yaml.load(open(args.train_config, "r"), Loader=yaml.FullLoader)
    configs = (preprocess_config, model_config, train_config)

    # main(args, configs, args.model_dir, log_dir)
    # main(args, configs, log_dir)
    main(args, args.model_dir, args.log_dir, configs)