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Fix some distributed training snafus

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This commit is contained in:
James Betker 2020-10-27 15:24:05 -06:00
parent 11a9e223a6
commit 2a3eec8fd7
4 changed files with 47 additions and 45 deletions
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1
codes/models/steps/losses.py
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@ -202,6 +202,7 @@ class DiscriminatorGanLoss(ConfigurableLoss):
# generators and discriminators by essentially having them skip steps while their counterparts "catch up".
self.min_loss = opt['min_loss'] if 'min_loss' in opt.keys() else 0
if self.min_loss != 0:
assert self.env['rank'] == 0 # distributed training does not support 'min_loss' - it can result in backward() desync by design.
self.loss_rotating_buffer = torch.zeros(10, requires_grad=False)
self.rb_ptr = 0
self.losses_computed = 0

79
codes/models/steps/steps.py
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@ -126,48 +126,49 @@ class ConfigurableStep(Module):
self.env['current_step_optimizers'] = self.optimizers
self.env['training'] = train
# Inject in any extra dependencies.
for inj in self.injectors:
# Don't do injections tagged with eval unless we are not in train mode.
if train and 'eval' in inj.opt.keys() and inj.opt['eval']:
continue
# Likewise, don't do injections tagged with train unless we are not in eval.
if not train and 'train' in inj.opt.keys() and inj.opt['train']:
continue
# Don't do injections tagged with 'after' or 'before' when we are out of spec.
if 'after' in inj.opt.keys() and self.env['step'] < inj.opt['after'] or \
'before' in inj.opt.keys() and self.env['step'] > inj.opt['before']:
continue
injected = inj(local_state)
local_state.update(injected)
new_state.update(injected)
if train and len(self.losses) > 0:
# Finally, compute the losses.
total_loss = 0
for loss_name, loss in self.losses.items():
# Some losses only activate after a set number of steps. For example, proto-discriminator losses can
# be very disruptive to a generator.
if 'after' in loss.opt.keys() and loss.opt['after'] > self.env['step']:
with self.get_network_for_name(self.get_networks_trained()[0]).join():
# Inject in any extra dependencies.
for inj in self.injectors:
# Don't do injections tagged with eval unless we are not in train mode.
if train and 'eval' in inj.opt.keys() and inj.opt['eval']:
continue
l = loss(self.training_net, local_state)
total_loss += l * self.weights[loss_name]
# Record metrics.
if isinstance(l, torch.Tensor):
self.loss_accumulator.add_loss(loss_name, l)
for n, v in loss.extra_metrics():
self.loss_accumulator.add_loss("%s_%s" % (loss_name, n), v)
loss.clear_metrics()
# Likewise, don't do injections tagged with train unless we are not in eval.
if not train and 'train' in inj.opt.keys() and inj.opt['train']:
continue
# Don't do injections tagged with 'after' or 'before' when we are out of spec.
if 'after' in inj.opt.keys() and self.env['step'] < inj.opt['after'] or \
'before' in inj.opt.keys() and self.env['step'] > inj.opt['before']:
continue
injected = inj(local_state)
local_state.update(injected)
new_state.update(injected)
# In some cases, the loss could not be set (e.g. all losses have 'after'
if isinstance(total_loss, torch.Tensor):
self.loss_accumulator.add_loss("%s_total" % (self.get_training_network_name(),), total_loss)
# Scale the loss down by the accumulation factor.
total_loss = total_loss / self.env['mega_batch_factor']
if train and len(self.losses) > 0:
# Finally, compute the losses.
total_loss = 0
for loss_name, loss in self.losses.items():
# Some losses only activate after a set number of steps. For example, proto-discriminator losses can
# be very disruptive to a generator.
if 'after' in loss.opt.keys() and loss.opt['after'] > self.env['step']:
continue
l = loss(self.training_net, local_state)
total_loss += l * self.weights[loss_name]
# Record metrics.
if isinstance(l, torch.Tensor):
self.loss_accumulator.add_loss(loss_name, l)
for n, v in loss.extra_metrics():
self.loss_accumulator.add_loss("%s_%s" % (loss_name, n), v)
loss.clear_metrics()
# Get dem grads!
self.scaler.scale(total_loss).backward()
self.grads_generated = True
# In some cases, the loss could not be set (e.g. all losses have 'after')
if isinstance(total_loss, torch.Tensor):
self.loss_accumulator.add_loss("%s_total" % (self.get_training_network_name(),), total_loss)
# Scale the loss down by the accumulation factor.
total_loss = total_loss / self.env['mega_batch_factor']
# Get dem grads!
self.scaler.scale(total_loss).backward()
self.grads_generated = True
# Detach all state variables. Within the step, gradients can flow. Once these variables leave the step
# we must release the gradients.

1
codes/requirements.txt
View File

@ -1,6 +1,5 @@
numpy
opencv-python
lmdb
pyyaml
tb-nightly
future

9
codes/train.py
View File

@ -46,7 +46,7 @@ class Trainer:
else:
opt['dist'] = True
self.init_dist()
self.init_dist('nccl')
world_size = torch.distributed.get_world_size()
self.rank = torch.distributed.get_rank()
@ -117,11 +117,11 @@ class Trainer:
total_iters = int(opt['train']['niter'])
self.total_epochs = int(math.ceil(total_iters / train_size))
if opt['dist']:
train_sampler = DistIterSampler(self.train_set, world_size, self.rank, dataset_ratio)
self.train_sampler = DistIterSampler(self.train_set, world_size, self.rank, dataset_ratio)
self.total_epochs = int(math.ceil(total_iters / (train_size * dataset_ratio)))
else:
train_sampler = None
self.train_loader = create_dataloader(self.train_set, dataset_opt, opt, train_sampler)
self.train_sampler = None
self.train_loader = create_dataloader(self.train_set, dataset_opt, opt, self.train_sampler)
if self.rank <= 0:
self.logger.info('Number of train images: {:,d}, iters: {:,d}'.format(
len(self.train_set), train_size))
@ -284,6 +284,7 @@ if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_prog_imgset_multifaceted_chained.yml')
parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
parser.add_argument('--local_rank', type=int, default=0)
args = parser.parse_args()
opt = option.parse(args.opt, is_train=True)
trainer = Trainer()