def step(self, epoch=None):
current_lr = self.get_lr()[0]
# Outside of warmup epochs, we use the same learning rate for every step
# in an epoch. Don't bother updating learning rate if it hasn't changed.
if abs(current_lr - self._previous_lr) >= self._min_delta_to_update_lr:
super(WarmupAndExponentialDecayScheduler, self).step()
self._previous_lr = current_lr
else:
self._step_count += 1 # This normally happens in super().step().
# Add current learning rate to Tensorboard metrics. For warmup epochs,
# log the learning rate at every step. For non-warmup epochs, log only
# the first step since the entire epoch will use the same learning rate.
if self._summary_writer:
if self._is_warmup_epoch() or (self._step_count %
self._num_steps_per_epoch == 0):
test_utils.add_scalar_to_summary(
self._summary_writer, 'LearningRate',
self.optimizer.param_groups[0]['lr'], self._step_count)
def train_mnist():
torch.manual_seed(1)
if FLAGS.fake_data:
train_dataset_len = 60000 # Number of images in MNIST dataset.
train_loader = xu.SampleGenerator(
data=(torch.zeros(FLAGS.batch_size, 1, 28, 28),
torch.zeros(FLAGS.batch_size, dtype=torch.int64)),
sample_count=train_dataset_len // FLAGS.batch_size //
xm.xrt_world_size())
test_loader = xu.SampleGenerator(
data=(torch.zeros(FLAGS.batch_size, 1, 28, 28),
torch.zeros(FLAGS.batch_size, dtype=torch.int64)),
sample_count=10000 // FLAGS.batch_size // xm.xrt_world_size())
else:
train_dataset = datasets.MNIST(FLAGS.datadir,
train=True,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ),
(0.3081, ))
]))
train_dataset_len = len(train_dataset)
test_dataset = datasets.MNIST(FLAGS.datadir,
train=False,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ),
(0.3081, ))
]))
train_sampler = None
test_sampler = None
if xm.xrt_world_size() > 1:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset,
num_replicas=xm.xrt_world_size(),
rank=xm.get_ordinal(),
shuffle=True)
test_sampler = torch.utils.data.distributed.DistributedSampler(
test_dataset,
num_replicas=xm.xrt_world_size(),
rank=xm.get_ordinal(),
shuffle=False)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=FLAGS.batch_size,
sampler=train_sampler,
drop_last=FLAGS.drop_last,
shuffle=False if train_sampler else True,
num_workers=FLAGS.num_workers)
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=FLAGS.batch_size,
sampler=test_sampler,
drop_last=FLAGS.drop_last,
shuffle=False,
num_workers=FLAGS.num_workers)
devices = (xm.get_xla_supported_devices(
max_devices=FLAGS.num_cores) if FLAGS.num_cores != 0 else [])
# Scale learning rate to num cores
lr = FLAGS.lr * max(len(devices), 1)
# Pass [] as device_ids to run using the PyTorch/CPU engine.
model_parallel = dp.DataParallel(MNIST, device_ids=devices)
def train_loop_fn(model, loader, device, context):
loss_fn = nn.NLLLoss()
optimizer = context.getattr_or(
'optimizer', lambda: optim.SGD(
model.parameters(), lr=lr, momentum=FLAGS.momentum))
tracker = xm.RateTracker()
model.train()
for x, (data, target) in enumerate(loader):
optimizer.zero_grad()
output = model(data)
loss = loss_fn(output, target)
loss.backward()
xm.optimizer_step(optimizer)
tracker.add(FLAGS.batch_size)
if x % FLAGS.log_steps == 0:
test_utils.print_training_update(device, x, loss.item(),
tracker.rate(),
tracker.global_rate())
def test_loop_fn(model, loader, device, context):
total_samples = 0
correct = 0
model.eval()
for data, target in loader:
output = model(data)
pred = output.max(1, keepdim=True)[1]
correct += pred.eq(target.view_as(pred)).sum().item()
total_samples += data.size()[0]
accuracy = 100.0 * correct / total_samples
test_utils.print_test_update(device, accuracy)
return accuracy
accuracy = 0.0
writer = test_utils.get_summary_writer(FLAGS.logdir)
num_devices = len(
xm.xla_replication_devices(devices)) if len(devices) > 1 else 1
num_training_steps_per_epoch = train_dataset_len // (FLAGS.batch_size *
num_devices)
for epoch in range(1, FLAGS.num_epochs + 1):
model_parallel(train_loop_fn, train_loader)
accuracies = model_parallel(test_loop_fn, test_loader)
accuracy = mean(accuracies)
print('Epoch: {}, Mean Accuracy: {:.2f}%'.format(epoch, accuracy))
global_step = (epoch - 1) * num_training_steps_per_epoch
test_utils.add_scalar_to_summary(writer, 'Accuracy/test', accuracy,
global_step)
if FLAGS.metrics_debug:
print(met.metrics_report())
test_utils.close_summary_writer(writer)
return accuracy
def train_imagenet():
print('==> Preparing data..')
img_dim = get_model_property('img_dim')
if FLAGS.fake_data:
train_dataset_len = 1200000 # Roughly the size of Imagenet dataset.
train_loader = xu.SampleGenerator(
data=(torch.zeros(FLAGS.batch_size, 3, img_dim, img_dim),
torch.zeros(FLAGS.batch_size, dtype=torch.int64)),
sample_count=train_dataset_len // FLAGS.batch_size //
xm.xrt_world_size())
test_loader = xu.SampleGenerator(
data=(torch.zeros(FLAGS.test_set_batch_size, 3, img_dim, img_dim),
torch.zeros(FLAGS.test_set_batch_size, dtype=torch.int64)),
sample_count=50000 // FLAGS.batch_size // xm.xrt_world_size())
else:
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_dataset = torchvision.datasets.ImageFolder(
os.path.join(FLAGS.datadir, 'train'),
transforms.Compose([
transforms.RandomResizedCrop(img_dim),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]))
train_dataset_len = len(train_dataset.imgs)
resize_dim = max(img_dim, 256)
test_dataset = torchvision.datasets.ImageFolder(
os.path.join(FLAGS.datadir, 'val'),
# Matches Torchvision's eval transforms except Torchvision uses size
# 256 resize for all models both here and in the train loader. Their
# version crashes during training on 299x299 images, e.g. inception.
transforms.Compose([
transforms.Resize(resize_dim),
transforms.CenterCrop(img_dim),
transforms.ToTensor(),
normalize,
]))
train_sampler = None
test_sampler = None
if xm.xrt_world_size() > 1:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset,
num_replicas=xm.xrt_world_size(),
rank=xm.get_ordinal(),
shuffle=True)
test_sampler = torch.utils.data.distributed.DistributedSampler(
test_dataset,
num_replicas=xm.xrt_world_size(),
rank=xm.get_ordinal(),
shuffle=False)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=FLAGS.batch_size,
sampler=train_sampler,
drop_last=FLAGS.drop_last,
shuffle=False if train_sampler else True,
num_workers=FLAGS.num_workers)
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=FLAGS.test_set_batch_size,
sampler=test_sampler,
drop_last=FLAGS.drop_last,
shuffle=False,
num_workers=FLAGS.num_workers)
torch.manual_seed(42)
devices = (xm.get_xla_supported_devices(
max_devices=FLAGS.num_cores) if FLAGS.num_cores != 0 else [])
# Pass [] as device_ids to run using the PyTorch/CPU engine.
torchvision_model = get_model_property('model_fn')
model_parallel = dp.DataParallel(torchvision_model, device_ids=devices)
def train_loop_fn(model, loader, device, context):
loss_fn = nn.CrossEntropyLoss()
optimizer = context.getattr_or(
'optimizer', lambda: optim.SGD(model.parameters(),
lr=FLAGS.lr,
momentum=FLAGS.momentum,
weight_decay=1e-4))
lr_scheduler = context.getattr_or(
'lr_scheduler', lambda: schedulers.wrap_optimizer_with_scheduler(
optimizer,
scheduler_type=getattr(FLAGS, 'lr_scheduler_type', None),
scheduler_divisor=getattr(FLAGS, 'lr_scheduler_divisor', None),
scheduler_divide_every_n_epochs=getattr(
FLAGS, 'lr_scheduler_divide_every_n_epochs', None),
num_steps_per_epoch=num_training_steps_per_epoch,
summary_writer=writer if xm.is_master_ordinal() else None))
tracker = xm.RateTracker()
model.train()
for x, (data, target) in enumerate(loader):
optimizer.zero_grad()
output = model(data)
loss = loss_fn(output, target)
loss.backward()
xm.optimizer_step(optimizer)
tracker.add(FLAGS.batch_size)
if x % FLAGS.log_steps == 0:
test_utils.print_training_update(device, x, loss.item(),
tracker.rate(),
tracker.global_rate())
if lr_scheduler:
lr_scheduler.step()
def test_loop_fn(model, loader, device, context):
total_samples = 0
correct = 0
model.eval()
for data, target in loader:
output = model(data)
pred = output.max(1, keepdim=True)[1]
correct += pred.eq(target.view_as(pred)).sum().item()
total_samples += data.size()[0]
accuracy = 100.0 * correct / total_samples
test_utils.print_test_update(device, accuracy)
return accuracy
accuracy = 0.0
writer = test_utils.get_summary_writer(FLAGS.logdir)
num_devices = len(
xm.xla_replication_devices(devices)) if len(devices) > 1 else 1
num_training_steps_per_epoch = train_dataset_len // (FLAGS.batch_size *
num_devices)
for epoch in range(1, FLAGS.num_epochs + 1):
model_parallel(train_loop_fn, train_loader)
accuracies = model_parallel(test_loop_fn, test_loader)
accuracy = mean(accuracies)
print('Epoch: {}, Mean Accuracy: {:.2f}%'.format(epoch, accuracy))
global_step = (epoch - 1) * num_training_steps_per_epoch
test_utils.add_scalar_to_summary(writer, 'Accuracy/test', accuracy,
global_step)
if FLAGS.metrics_debug:
print(met.metrics_report())
test_utils.close_summary_writer(writer)
return accuracy
def train_mnist():
torch.manual_seed(1)
if FLAGS.fake_data:
train_loader = xu.SampleGenerator(
data=(torch.zeros(FLAGS.batch_size, 1, 28, 28),
torch.zeros(FLAGS.batch_size, dtype=torch.int64)),
sample_count=60000 // FLAGS.batch_size // xm.xrt_world_size())
test_loader = xu.SampleGenerator(
data=(torch.zeros(FLAGS.batch_size, 1, 28, 28),
torch.zeros(FLAGS.batch_size, dtype=torch.int64)),
sample_count=10000 // FLAGS.batch_size // xm.xrt_world_size())
else:
train_dataset = datasets.MNIST(os.path.join(FLAGS.datadir,
str(xm.get_ordinal())),
train=True,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ),
(0.3081, ))
]))
test_dataset = datasets.MNIST(os.path.join(FLAGS.datadir,
str(xm.get_ordinal())),
train=False,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ),
(0.3081, ))
]))
train_sampler = None
if xm.xrt_world_size() > 1:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset,
num_replicas=xm.xrt_world_size(),
rank=xm.get_ordinal(),
shuffle=True)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=FLAGS.batch_size,
sampler=train_sampler,
drop_last=FLAGS.drop_last,
shuffle=False if train_sampler else True,
num_workers=FLAGS.num_workers)
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=FLAGS.batch_size,
drop_last=FLAGS.drop_last,
shuffle=False,
num_workers=FLAGS.num_workers)
# Scale learning rate to num cores
lr = FLAGS.lr * xm.xrt_world_size()
device = xm.xla_device()
model = MNIST().to(device)
writer = None
if xm.is_master_ordinal():
writer = test_utils.get_summary_writer(FLAGS.logdir)
optimizer = optim.SGD(model.parameters(), lr=lr, momentum=FLAGS.momentum)
loss_fn = nn.NLLLoss()
def train_loop_fn(loader):
tracker = xm.RateTracker()
model.train()
for x, (data, target) in enumerate(loader):
optimizer.zero_grad()
output = model(data)
loss = loss_fn(output, target)
loss.backward()
xm.optimizer_step(optimizer)
tracker.add(FLAGS.batch_size)
if x % FLAGS.log_steps == 0:
xm.add_step_closure(_train_update,
args=(device, x, loss, tracker))
def test_loop_fn(loader):
total_samples = 0
correct = 0
model.eval()
for data, target in loader:
output = model(data)
pred = output.max(1, keepdim=True)[1]
correct += pred.eq(target.view_as(pred)).sum().item()
total_samples += data.size()[0]
accuracy = 100.0 * correct / total_samples
test_utils.print_test_update(device, accuracy)
return accuracy
accuracy = 0.0
for epoch in range(1, FLAGS.num_epochs + 1):
para_loader = pl.ParallelLoader(train_loader, [device])
train_loop_fn(para_loader.per_device_loader(device))
xm.master_print('Finished training epoch {}'.format(epoch))
para_loader = pl.ParallelLoader(test_loader, [device])
accuracy = test_loop_fn(para_loader.per_device_loader(device))
test_utils.add_scalar_to_summary(writer, 'Accuracy/test', accuracy,
epoch)
if FLAGS.metrics_debug:
print(met.metrics_report())
test_utils.close_summary_writer(writer)
return accuracy
def train_cifar():
print('==> Preparing data..')
if FLAGS.fake_data:
train_dataset_len = 50000 # Number of example in CIFAR train set.
train_loader = xu.SampleGenerator(
data=(torch.zeros(FLAGS.batch_size, 3, 32,
32), torch.zeros(FLAGS.batch_size,
dtype=torch.int64)),
sample_count=train_dataset_len // FLAGS.batch_size
// xm.xrt_world_size())
test_loader = xu.SampleGenerator(
data=(torch.zeros(FLAGS.batch_size, 3, 32,
32), torch.zeros(FLAGS.batch_size,
dtype=torch.int64)),
sample_count=10000 // FLAGS.batch_size // xm.xrt_world_size())
else:
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
train_dataset = torchvision.datasets.CIFAR10(
root=FLAGS.datadir,
train=True,
download=True,
transform=transform_train)
train_dataset_len = len(train_dataset)
test_dataset = torchvision.datasets.CIFAR10(
root=FLAGS.datadir,
train=False,
download=True,
transform=transform_test)
train_sampler = None
test_sampler = None
if xm.xrt_world_size() > 1:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset, num_replicas=xm.xrt_world_size(),
rank=xm.get_ordinal(), shuffle=True)
test_sampler = torch.utils.data.distributed.DistributedSampler(
test_dataset, num_replicas=xm.xrt_world_size(),
rank=xm.get_ordinal(), shuffle=False)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=FLAGS.batch_size,
sampler=train_sampler,
drop_last=FLAGS.drop_last,
shuffle=False if train_sampler else True,
num_workers=FLAGS.num_workers)
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=FLAGS.batch_size,
sampler=test_sampler,
drop_last=FLAGS.drop_last,
shuffle=False,
num_workers=FLAGS.num_workers)
torch.manual_seed(42)
devices = (
xm.get_xla_supported_devices(
max_devices=FLAGS.num_cores) if FLAGS.num_cores != 0 else [])
# Pass [] as device_ids to run using the PyTorch/CPU engine.
model = torchvision.models.resnet18 if FLAGS.use_torchvision else ResNet18
model_parallel = dp.DataParallel(model, device_ids=devices)
def train_loop_fn(model, loader, device, context):
loss_fn = nn.CrossEntropyLoss()
optimizer = context.getattr_or(
'optimizer', lambda: optim.SGD(
model.parameters(),
lr=FLAGS.lr,
momentum=FLAGS.momentum,
weight_decay=5e-4))
tracker = xm.RateTracker()
model.train()
for x, (data, target) in enumerate(loader):
optimizer.zero_grad()
output = model(data)
loss = loss_fn(output, target)
loss.backward()
xm.optimizer_step(optimizer)
tracker.add(FLAGS.batch_size)
if x % FLAGS.log_steps == 0:
test_utils.print_training_update(device, x, loss.item(),
tracker.rate(),
tracker.global_rate())
def test_loop_fn(model, loader, device, context):
total_samples = 0
correct = 0
model.eval()
for data, target in loader:
output = model(data)
pred = output.max(1, keepdim=True)[1]
correct += pred.eq(target.view_as(pred)).sum().item()
total_samples += data.size()[0]
accuracy = 100.0 * correct / total_samples
test_utils.print_test_update(device, accuracy)
return accuracy
accuracy = 0.0
writer = test_utils.get_summary_writer(FLAGS.logdir)
num_devices = len(
xm.xla_replication_devices(devices)) if len(devices) > 1 else 1
num_training_steps_per_epoch = train_dataset_len // (
FLAGS.batch_size * num_devices)
max_accuracy = 0.0
for epoch in range(1, FLAGS.num_epochs + 1):
model_parallel(train_loop_fn, train_loader)
accuracies = model_parallel(test_loop_fn, test_loader)
accuracy = mean(accuracies)
max_accuracy = max(accuracy, max_accuracy)
print('Epoch: {}, Mean Accuracy: {:.2f}%'.format(epoch, accuracy))
global_step = (epoch - 1) * num_training_steps_per_epoch
test_utils.add_scalar_to_summary(writer, 'Accuracy/test', accuracy,
global_step)
if FLAGS.metrics_debug:
print(met.metrics_report())
test_utils.close_summary_writer(writer)
print('Max Accuracy: {:.2f}%'.format(accuracy))
return max_accuracy
