dan
/
tacotron2


								import os

								import time

								import argparse

								import math

								from numpy import finfo


								import torch

								from distributed import apply_gradient_allreduce

								import torch.distributed as dist

								from torch.utils.data.distributed import DistributedSampler

								from torch.utils.data import DataLoader


								from fp16_optimizer import FP16_Optimizer


								from model import Tacotron2

								from data_utils import TextMelLoader, TextMelCollate

								from loss_function import Tacotron2Loss

								from logger import Tacotron2Logger

								from hparams import create_hparams


								def batchnorm_to_float(module):

								    """Converts batch norm modules to FP32"""

								    if isinstance(module, torch.nn.modules.batchnorm._BatchNorm):

								        module.float()

								    for child in module.children():

								        batchnorm_to_float(child)

								    return module


								def reduce_tensor(tensor, num_gpus):

								    rt = tensor.clone()

								    dist.all_reduce(rt, op=dist.reduce_op.SUM)

								    rt /= num_gpus

								    return rt


								def init_distributed(hparams, n_gpus, rank, group_name):

								    assert torch.cuda.is_available(), "Distributed mode requires CUDA."

								    print("Initializing Distributed")


								    # Set cuda device so everything is done on the right GPU.

								    torch.cuda.set_device(rank % torch.cuda.device_count())


								    # Initialize distributed communication

								    dist.init_process_group(

								        backend=hparams.dist_backend, init_method=hparams.dist_url,

								        world_size=n_gpus, rank=rank, group_name=group_name)


								    print("Done initializing distributed")


								def prepare_dataloaders(hparams):

								    # Get data, data loaders and collate function ready

								    trainset = TextMelLoader(hparams.training_files, hparams)

								    valset = TextMelLoader(hparams.validation_files, hparams)

								    collate_fn = TextMelCollate(hparams.n_frames_per_step)


								    train_sampler = DistributedSampler(trainset) \

								        if hparams.distributed_run else None


								    train_loader = DataLoader(trainset, num_workers=1, shuffle=False,

								                              sampler=train_sampler,

								                              batch_size=hparams.batch_size, pin_memory=False,

								                              drop_last=True, collate_fn=collate_fn)

								    return train_loader, valset, collate_fn


								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)

								        logger = Tacotron2Logger(os.path.join(output_directory, log_directory))

								    else:

								        logger = None

								    return logger


								def load_model(hparams):

								    model = Tacotron2(hparams).cuda()

								    if hparams.fp16_run:

								        model = batchnorm_to_float(model.half())

								        model.decoder.attention_layer.score_mask_value = float(finfo('float16').min)


								    if hparams.distributed_run:

								        model = DistributedDataParallel(model)

								    elif torch.cuda.device_count() > 1:

								        model = DataParallel(model)


								    return model


								def warm_start_model(checkpoint_path, model):

								    assert os.path.isfile(checkpoint_path)

								    print("Warm starting model from checkpoint '{}'".format(checkpoint_path))

								    checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')

								    model.load_state_dict(checkpoint_dict['state_dict'])

								    return model


								def load_checkpoint(checkpoint_path, model, optimizer):

								    assert os.path.isfile(checkpoint_path)

								    print("Loading checkpoint '{}'".format(checkpoint_path))

								    checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')

								    model.load_state_dict(checkpoint_dict['state_dict'])

								    optimizer.load_state_dict(checkpoint_dict['optimizer'])

								    learning_rate = checkpoint_dict['learning_rate']

								    iteration = checkpoint_dict['iteration']

								    print("Loaded checkpoint '{}' from iteration {}" .format(

								        checkpoint_path, iteration))

								    return model, optimizer, learning_rate, iteration


								def save_checkpoint(model, optimizer, learning_rate, iteration, filepath):

								    print("Saving model and optimizer state at iteration {} to {}".format(

								        iteration, filepath))

								    torch.save({'iteration': iteration,

								                'state_dict': model.state_dict(),

								                'optimizer': optimizer.state_dict(),

								                'learning_rate': learning_rate}, filepath)


								def validate(model, criterion, valset, iteration, batch_size, n_gpus,

								             collate_fn, logger, distributed_run, rank):

								    """Handles all the validation scoring and printing"""

								    model.eval()

								    with torch.no_grad():

								        val_sampler = DistributedSampler(valset) if distributed_run else None

								        val_loader = DataLoader(valset, sampler=val_sampler, num_workers=1,

								                                shuffle=False, batch_size=batch_size,

								                                pin_memory=False, collate_fn=collate_fn)


								        val_loss = 0.0

								        for i, batch in enumerate(val_loader):

								            x, y = model.parse_batch(batch)

								            y_pred = model(x)

								            loss = criterion(y_pred, y)

								            if distributed_run:

								                reduced_val_loss = reduce_tensor(loss.data, num_gpus).item()

								            else:

								                reduced_val_loss = loss.item()

								            val_loss += reduced_val_loss

								        val_loss = val_loss / (i + 1)


								    model.train()

								    print("Validation loss {}: {:9f}  ".format(iteration, reduced_val_loss))

								    logger.log_validation(reduced_val_loss, model, y, y_pred, iteration)


								def train(output_directory, log_directory, checkpoint_path, warm_start, n_gpus,

								          rank, group_name, hparams):

								    """Training and validation logging results to tensorboard and stdout


								    Params

								    ------

								    output_directory (string): directory to save checkpoints

								    log_directory (string) directory to save tensorboard logs

								    checkpoint_path(string): checkpoint path

								    n_gpus (int): number of gpus

								    rank (int): rank of current gpu

								    hparams (object): comma separated list of "name=value" pairs.

								    """

								    if hparams.distributed_run:

								        init_distributed(hparams, n_gpus, rank, group_name)


								    torch.manual_seed(hparams.seed)

								    torch.cuda.manual_seed(hparams.seed)


								    model = load_model(hparams)

								    learning_rate = hparams.learning_rate

								    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate,

								                                 weight_decay=hparams.weight_decay)

								    if hparams.fp16_run:

								        optimizer = FP16_Optimizer(

								            optimizer, dynamic_loss_scale=hparams.dynamic_loss_scaling)


								    if hparams.distributed_run:

								        model = apply_gradient_allreduce(model)


								    criterion = Tacotron2Loss()


								    logger = prepare_directories_and_logger(

								        output_directory, log_directory, rank)


								    train_loader, valset, collate_fn = prepare_dataloaders(hparams)


								    # Load checkpoint if one exists

								    iteration = 0

								    epoch_offset = 0

								    if checkpoint_path is not None:

								        if warm_start:

								            model = warm_start_model(checkpoint_path, model)

								        else:

								            model, optimizer, _learning_rate, iteration = load_checkpoint(

								                checkpoint_path, model, optimizer)

								            if hparams.use_saved_learning_rate:

								                learning_rate = _learning_rate

								            iteration += 1  # next iteration is iteration + 1

								            epoch_offset = max(0, int(iteration / len(train_loader)))


								    model.train()

								    # ================ MAIN TRAINNIG LOOP! ===================

								    for epoch in range(epoch_offset, hparams.epochs):

								        print("Epoch: {}".format(epoch))

								        for i, batch in enumerate(train_loader):

								            start = time.perf_counter()

								            for param_group in optimizer.param_groups:

								                param_group['lr'] = learning_rate


								            model.zero_grad()

								            x, y = model.parse_batch(batch)

								            y_pred = model(x)


								            loss = criterion(y_pred, y)

								            if hparams.distributed_run:

								                reduced_loss = reduce_tensor(loss.data, num_gpus).item()

								            else:

								                reduced_loss = loss.item()


								            if hparams.fp16_run:

								                optimizer.backward(loss)

								                grad_norm = optimizer.clip_fp32_grads(hparams.grad_clip_thresh)

								            else:

								                loss.backward()

								                grad_norm = torch.nn.utils.clip_grad_norm_(

								                    model.parameters(), hparams.grad_clip_thresh)


								            optimizer.step()


								            overflow = optimizer.overflow if hparams.fp16_run else False


								            if not overflow and not math.isnan(reduced_loss) and rank == 0:

								                duration = time.perf_counter() - start

								                print("Train loss {} {:.6f} Grad Norm {:.6f} {:.2f}s/it".format(

								                    iteration, reduced_loss, grad_norm, duration))

								                logger.log_training(

								                    reduced_loss, grad_norm, learning_rate, duration, iteration)


								            if not overflow and (iteration % hparams.iters_per_checkpoint == 0):

								                validate(model, criterion, valset, iteration, hparams.batch_size,

								                         n_gpus, collate_fn, logger, hparams.distributed_run, rank)


								                if rank == 0:

								                    checkpoint_path = os.path.join(

								                        output_directory, "checkpoint_{}".format(iteration))

								                    save_checkpoint(model, optimizer, learning_rate, iteration,

								                                    checkpoint_path)


								            iteration += 1


								if __name__ == '__main__':

								    parser = argparse.ArgumentParser()

								    parser.add_argument('-o', '--output_directory', type=str,

								                        help='directory to save checkpoints')

								    parser.add_argument('-l', '--log_directory', type=str,

								                        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 the model only (warm start)')

								    parser.add_argument('--n_gpus', type=int, default=1,

								                        required=False, help='number of gpus')

								    parser.add_argument('--rank', type=int, default=0,

								                        required=False, help='rank of current gpu')

								    parser.add_argument('--group_name', type=str, default='group_name',

								                        required=False, help='Distributed group name')

								    parser.add_argument('--hparams', type=str,

								                        required=False, help='comma separated name=value pairs')


								    args = parser.parse_args()

								    hparams = create_hparams(args.hparams)


								    torch.backends.cudnn.enabled = hparams.cudnn_enabled

								    torch.backends.cudnn.benchmark = hparams.cudnn_benchmark


								    print("FP16 Run:", hparams.fp16_run)

								    print("Dynamic Loss Scaling:", hparams.dynamic_loss_scaling)

								    print("Distributed Run:", hparams.distributed_run)

								    print("cuDNN Enabled:", hparams.cudnn_enabled)

								    print("cuDNN Benchmark:", hparams.cudnn_benchmark)


								    train(args.output_directory, args.log_directory, args.checkpoint_path,

								          args.warm_start, args.n_gpus, args.rank, args.group_name, hparams)