def backprop(self, state, loss):
if state.stage != Stage.TRAIN:
return
with timing.time("loss.backward"):
precision.backward(state.optimizer, loss)
state.scheduler.step_batch()
if self.config.max_clip_norm is not None:
grad_norm = precision.clip_grad_norm(state.model, self.optimizer,
self.config.max_clip_norm)
else:
grad_norm = None
with timing.time("optimizer.step"):
state.optimizer.step()
# grad_norm could be used to check grads sync in distributed training
return grad_norm
def training_backprop(loss):
with timing.time("loss.backward"):
precision.backward(self.optimizer, loss)
if world_size > 1:
# DDP fix when some parameters don't receive grads
for p in model.parameters():
if p.requires_grad and p.grad is None:
p.backward(torch.zeros_like(p.data))
if self.lr_scheduler:
self.lr_scheduler.step_batch()
if self.config.max_clip_norm is not None:
grad_norm = torch.nn.utils.clip_grad_norm_(
model.parameters(), self.config.max_clip_norm)
else:
grad_norm = None
with timing.time("optimizer.step"):
self.optimizer.step()
# grad_norm could be used to check grads sync in distributed training
return grad_norm
def backprop(self, state, loss):
if state.stage != Stage.TRAIN:
return
with timing.time("loss.backward"):
precision.backward(state.optimizer, loss)
if cuda.DISTRIBUTED_WORLD_SIZE > 1:
# DDP fix when some parameters don't receive grads
for p in state.model.parameters():
if p.requires_grad and p.grad is None:
p.backward(torch.zeros_like(p.data))
state.scheduler.step_batch()
if self.config.max_clip_norm is not None:
grad_norm = precision.clip_grad_norm(state.model, self.optimizer,
self.config.max_clip_norm)
else:
grad_norm = None
with timing.time("optimizer.step"):
state.optimizer.step()
# grad_norm could be used to check grads sync in distributed training
return grad_norm
def backprop(self, state, loss):
if state.stage != Stage.TRAIN:
return
with timing.time("loss.backward"):
precision.backward(state.optimizer, loss)
