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train.py
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train.py
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# *****************************************************************************
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of the NVIDIA CORPORATION nor the
# names of its contributors may be used to endorse or promote products
# derived from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
# *****************************************************************************
import argparse
import json
import os
import torch
#=====START: ADDED FOR DISTRIBUTED======
from distributed import init_distributed, apply_gradient_allreduce, reduce_tensor
from torch.utils.data.distributed import DistributedSampler
#=====END: ADDED FOR DISTRIBUTED======
from torch.utils.data import DataLoader
from glow import WaveGlow, WaveGlowLoss
from mel2samp import Mel2Samp
def load_checkpoint(checkpoint_path, model, optimizer):
assert os.path.isfile(checkpoint_path)
checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
iteration = checkpoint_dict['iteration']
optimizer.load_state_dict(checkpoint_dict['optimizer'])
model_for_loading = checkpoint_dict['model']
model.load_state_dict(model_for_loading.state_dict())
print("Loaded checkpoint '{}' (iteration {})" .format(
checkpoint_path, iteration))
return model, optimizer, iteration
def save_checkpoint(model, optimizer, learning_rate, iteration, filepath):
print("Saving model and optimizer state at iteration {} to {}".format(
iteration, filepath))
model_for_saving = WaveGlow(**waveglow_config).cuda()
model_for_saving.load_state_dict(model.state_dict())
torch.save({'model': model_for_saving,
'iteration': iteration,
'optimizer': optimizer.state_dict(),
'learning_rate': learning_rate}, filepath)
def train(num_gpus, rank, group_name, output_directory, epochs, learning_rate,
sigma, iters_per_checkpoint, batch_size, seed, fp16_run,
checkpoint_path, with_tensorboard):
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
#=====START: ADDED FOR DISTRIBUTED======
if num_gpus > 1:
init_distributed(rank, num_gpus, group_name, **dist_config)
#=====END: ADDED FOR DISTRIBUTED======
criterion = WaveGlowLoss(sigma)
model = WaveGlow(**waveglow_config).cuda()
#=====START: ADDED FOR DISTRIBUTED======
if num_gpus > 1:
model = apply_gradient_allreduce(model)
#=====END: ADDED FOR DISTRIBUTED======
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
if fp16_run:
from apex import amp
model, optimizer = amp.initialize(model, optimizer, opt_level='O1')
# Load checkpoint if one exists
iteration = 0
if checkpoint_path != "":
model, optimizer, iteration = load_checkpoint(checkpoint_path, model,
optimizer)
iteration += 1 # next iteration is iteration + 1
trainset = Mel2Samp(**data_config)
# =====START: ADDED FOR DISTRIBUTED======
train_sampler = DistributedSampler(trainset) if num_gpus > 1 else None
# =====END: ADDED FOR DISTRIBUTED======
train_loader = DataLoader(trainset, num_workers=1, shuffle=False,
sampler=train_sampler,
batch_size=batch_size,
pin_memory=False,
drop_last=True)
# Get shared output_directory ready
if rank == 0:
if not os.path.isdir(output_directory):
os.makedirs(output_directory)
os.chmod(output_directory, 0o775)
print("output directory", output_directory)
if with_tensorboard and rank == 0:
from tensorboardX import SummaryWriter
logger = SummaryWriter(os.path.join(output_directory, 'logs'))
model.train()
epoch_offset = max(0, int(iteration / len(train_loader)))
# ================ MAIN TRAINNIG LOOP! ===================
for epoch in range(epoch_offset, epochs):
print("Epoch: {}".format(epoch))
for i, batch in enumerate(train_loader):
model.zero_grad()
mel, audio = batch
mel = torch.autograd.Variable(mel.cuda())
audio = torch.autograd.Variable(audio.cuda())
outputs = model((mel, audio))
loss = criterion(outputs)
if num_gpus > 1:
reduced_loss = reduce_tensor(loss.data, num_gpus).item()
else:
reduced_loss = loss.item()
if fp16_run:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
optimizer.step()
print("{}:\t{:.9f}".format(iteration, reduced_loss))
if with_tensorboard and rank == 0:
logger.add_scalar('training_loss', reduced_loss, i + len(train_loader) * epoch)
if (iteration % iters_per_checkpoint == 0):
if rank == 0:
checkpoint_path = "{}/waveglow_{}".format(
output_directory, iteration)
save_checkpoint(model, optimizer, learning_rate, iteration,
checkpoint_path)
iteration += 1
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('-c', '--config', type=str,
help='JSON file for configuration')
parser.add_argument('-r', '--rank', type=int, default=0,
help='rank of process for distributed')
parser.add_argument('-g', '--group_name', type=str, default='',
help='name of group for distributed')
args = parser.parse_args()
# Parse configs. Globals nicer in this case
with open(args.config) as f:
data = f.read()
config = json.loads(data)
train_config = config["train_config"]
global data_config
data_config = config["data_config"]
global dist_config
dist_config = config["dist_config"]
global waveglow_config
waveglow_config = config["waveglow_config"]
num_gpus = torch.cuda.device_count()
if num_gpus > 1:
if args.group_name == '':
print("WARNING: Multiple GPUs detected but no distributed group set")
print("Only running 1 GPU. Use distributed.py for multiple GPUs")
num_gpus = 1
if num_gpus == 1 and args.rank != 0:
raise Exception("Doing single GPU training on rank > 0")
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = False
train(num_gpus, args.rank, args.group_name, **train_config)