def __call__(self, global_step, loss: AverageMeter, epoch: int, fp16: bool,
grad_scaler: torch.cuda.amp.GradScaler):
if self.rank is 0 and global_step > 0 and global_step % self.frequent == 0:
if self.init:
try:
speed: float = self.frequent * self.batch_size / (
time.time() - self.tic)
speed_total = speed * self.world_size
except ZeroDivisionError:
speed_total = float('inf')
time_now = (time.time() - self.time_start) / 3600
time_total = time_now / ((global_step + 1) / self.total_step)
time_for_end = time_total - time_now
if self.writer is not None:
self.writer.add_scalar('time_for_end', time_for_end,
global_step)
self.writer.add_scalar('loss', loss.avg, global_step)
if fp16:
msg = "Speed %.2f samples/sec Loss %.4f Epoch: %d Global Step: %d "\
"Fp16 Grad Scale: %2.f Required: %1.f hours" % (
speed_total, loss.avg, epoch, global_step, grad_scaler.get_scale(), time_for_end
)
else:
msg = "Speed %.2f samples/sec Loss %.4f Epoch: %d Global Step: %d Required: %1.f hours" % (
speed_total, loss.avg, epoch, global_step,
time_for_end)
logging.info(msg)
loss.reset()
self.tic = time.time()
else:
self.init = True
self.tic = time.time()
def process_batch(model: nn.Module, batch: Tuple, criterion: nn.modules.loss._Loss,
optimizer: torch.optim.Optimizer, scaler: torch.cuda.amp.GradScaler,
train: bool, device: torch.cuda.Device) \
-> Tuple[torch.Tensor, torch.Tensor, List[str], List[torch.Tensor]]:
"""
:param model: model to train
:param batch: batch with spectrograms, sample_rates, utterances
:param criterion: criterion to calculate loss
:param optimizer: optimizer to step
:param scaler: GradScaler for mixed precision training
:param train: perform gradient step
:param device: cuda device to work on
:return: (loss, logprobs, utterances)
"""
input_lengths, target_lengths, waveforms, spectrograms, sample_rates, utterances = batch
spectrograms = spectrograms.to(device)
utterances = utterances.to(device)
with torch.cuda.amp.autocast():
logprobs = model(spectrograms)
loss = criterion(logprobs.permute(2, 0, 1), utterances, input_lengths, target_lengths)
if train:
optimizer.zero_grad()
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
loss = loss.detach()
logprobs = logprobs.detach()
utterances = utterances.detach()
return loss, logprobs, utterances, waveforms
def after_loss_fn_new(_input: torch.Tensor, _label: torch.Tensor, _output: torch.Tensor,
loss: torch.Tensor, optimizer: Optimizer, loss_fn: Callable[..., torch.Tensor] = None,
amp: bool = False, scaler: torch.cuda.amp.GradScaler = None, **kwargs):
noise = torch.zeros_like(_input)
adv_loss_fn = functools.partial(self.adv_loss, _label=_label)
for m in range(self.pgd.iteration):
if amp:
scaler.step(optimizer)
scaler.update()
else:
optimizer.step()
self.eval()
adv_x, _ = self.pgd.optimize(_input=_input, noise=noise,
loss_fn=adv_loss_fn,
iteration=1, epsilon=adv_train_epsilon)
self.train()
loss = loss_fn(adv_x, _label)
if callable(after_loss_fn_old):
after_loss_fn_old(_input=_input, _label=_label, _output=_output,
loss=loss, optimizer=optimizer, loss_fn=loss_fn,
amp=amp, scaler=scaler, **kwargs)
if amp:
scaler.scale(loss).backward()
else:
loss.backward()
def after_loss_fn_new(_input: torch.Tensor, _label: torch.Tensor, _output: torch.Tensor,
loss: torch.Tensor, optimizer: Optimizer, loss_fn: Callable[..., torch.Tensor] = None,
amp: bool = False, scaler: torch.cuda.amp.GradScaler = None, **kwargs):
optimizer.zero_grad()
self.zero_grad()
if pre_conditioner is not None:
pre_conditioner.reset()
if self.adv_train == 'free':
noise = self.pgd.init_noise(_input.shape, pgd_eps=self.adv_train_eps,
random_init=self.adv_train_random_init,
device=_input.device)
adv_x = add_noise(x=_input, noise=noise, universal=self.pgd.universal,
clip_min=self.pgd.clip_min, clip_max=self.pgd.clip_max)
noise.data = self.pgd.valid_noise(adv_x, _input)
for m in range(self.adv_train_iter):
loss = loss_fn(adv_x, _label)
if amp:
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
else:
loss.backward()
optimizer.step()
optimizer.zero_grad()
self.zero_grad()
# self.eval()
adv_x, _ = self.pgd.optimize(_input=_input, noise=noise, target=_label, iteration=1,
pgd_alpha=self.adv_train_alpha, pgd_eps=self.adv_train_eps)
# self.train()
loss = loss_fn(adv_x, _label)
else:
loss = self.adv_loss(_input=_input, _label=_label, loss_fn=loss_fn)
if amp:
scaler.scale(loss).backward()
else:
loss.backward()
if callable(after_loss_fn_old):
after_loss_fn_old(_input=_input, _label=_label, _output=_output,
loss=loss, optimizer=optimizer, loss_fn=loss_fn,
amp=amp, scaler=scaler, **kwargs)
def train_one_epoch(
model: torch.nn.Module,
criterion: torch.nn.Module,
data_loader: Iterable,
optimizer: torch.optim.Optimizer,
scaler: torch.cuda.amp.GradScaler,
amp,
device: torch.device,
epoch: int,
max_norm: float = 0,
):
model.train()
criterion.train()
metric_logger = utils.MetricLogger(delimiter=" ")
metric_logger.add_meter(
'lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
metric_logger.add_meter(
'class_error', utils.SmoothedValue(window_size=1, fmt='{value:.2f}'))
header = 'Epoch: [{}]'.format(epoch)
print_freq = 10
for samples, targets in metric_logger.log_every(data_loader, print_freq,
header):
optimizer.zero_grad()
with autocast(enabled=amp):
samples = samples.to(device)
targets = [{k: v.to(device)
for k, v in t.items()} for t in targets]
outputs = model(samples)
loss_dict = criterion(outputs, targets)
weight_dict = criterion.weight_dict
losses = sum(loss_dict[k] * weight_dict[k]
for k in loss_dict.keys() if k in weight_dict)
# reduce losses over all GPUs for logging purposes
loss_dict_reduced = utils.reduce_dict(loss_dict)
loss_dict_reduced_unscaled = {
f'{k}_unscaled': v
for k, v in loss_dict_reduced.items()
}
loss_dict_reduced_scaled = {
k: v * weight_dict[k]
for k, v in loss_dict_reduced.items() if k in weight_dict
}
losses_reduced_scaled = sum(loss_dict_reduced_scaled.values())
loss_value = losses_reduced_scaled.item()
if not math.isfinite(loss_value):
print("Loss is {}, stopping training".format(loss_value))
print(loss_dict_reduced)
sys.exit(1)
scaler.scale(losses).backward()
if max_norm > 0:
scaler.unscale_(optimizer)
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)
scaler.step(optimizer)
scaler.update()
metric_logger.update(loss=loss_value,
**loss_dict_reduced_scaled,
**loss_dict_reduced_unscaled)
metric_logger.update(class_error=loss_dict_reduced['class_error'])
metric_logger.update(lr=optimizer.param_groups[0]["lr"])
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print("Averaged stats:", metric_logger)
return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
def train_one_epoch(model: torch.nn.Module,
criterion: torch.nn.Module,
data_loader: Iterable,
optimizer: torch.optim.Optimizer,
device: torch.device,
scaler: torch.cuda.amp.GradScaler,
epoch: int,
max_norm: float = 0,
fp16=False):
model.train()
criterion.train()
tensor_type = torch.cuda.HalfTensor if fp16 else torch.cuda.FloatTensor
metric_logger = utils.MetricLogger(delimiter=" ")
metric_logger.add_meter(
'lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
metric_logger.add_meter(
'class_error', utils.SmoothedValue(window_size=1, fmt='{value:.2f}'))
metric_logger.add_meter(
'grad_norm', utils.SmoothedValue(window_size=1, fmt='{value:.2f}'))
header = 'Epoch: [{}]'.format(epoch)
print_freq = 10
prefetcher = data_prefetcher(data_loader, device, prefetch=True)
samples, targets = prefetcher.next()
# for samples, targets in metric_logger.log_every(data_loader, print_freq, header):
for _ in metric_logger.log_every(range(len(data_loader)), print_freq,
header):
samples.tensors = samples.tensors.type(tensor_type)
samples.mask = samples.mask.type(tensor_type)
with torch.cuda.amp.autocast(enabled=fp16):
outputs, pre_outputs, pre_targets = model([samples, targets])
loss_dict = criterion(outputs, targets, pre_outputs, pre_targets)
weight_dict = criterion.weight_dict
losses = sum(loss_dict[k] * weight_dict[k]
for k in loss_dict.keys() if k in weight_dict)
# reduce losses over all GPUs for logging purposes
loss_dict_reduced = utils.reduce_dict(loss_dict)
loss_dict_reduced_unscaled = {
f'{k}_unscaled': v
for k, v in loss_dict_reduced.items()
}
loss_dict_reduced_scaled = {
k: v * weight_dict[k]
for k, v in loss_dict_reduced.items() if k in weight_dict
}
losses_reduced_scaled = sum(loss_dict_reduced_scaled.values())
loss_value = losses_reduced_scaled.item()
if not math.isfinite(loss_value):
print("Loss is {}, stopping training".format(loss_value))
print(loss_dict_reduced)
sys.exit(1)
optimizer.zero_grad()
scaler.scale(losses).backward()
scaler.unscale_(optimizer)
if max_norm > 0:
grad_total_norm = torch.nn.utils.clip_grad_norm_(
model.parameters(), max_norm)
else:
grad_total_norm = utils.get_total_grad_norm(
model.parameters(), max_norm)
scaler.step(optimizer)
scaler.update()
metric_logger.update(loss=loss_value,
**loss_dict_reduced_scaled,
**loss_dict_reduced_unscaled)
metric_logger.update(class_error=loss_dict_reduced['class_error'])
metric_logger.update(lr=optimizer.param_groups[0]["lr"])
metric_logger.update(grad_norm=grad_total_norm)
samples, targets = prefetcher.next()
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print("Averaged stats:", metric_logger)
return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
