class DDPTrainer(object):
"""Main class for data parallel training.
This class supports data parallel training, where multiple workers each
have a full model replica and gradients are accumulated synchronously via
torch.distributed.all_reduce.
"""
def __init__(self, args, model):
if not torch.cuda.is_available():
raise NotImplementedError('Training on CPU is not supported')
self.args = args
self.model = model.cuda()
self.criterion = CRITERION_REGISTRY[args.criterion](args).cuda()
self.optimizer = optim.build_optimizer(self.args, self.model.parameters())
self.lr_scheduler = lr_scheduler.build_lr_scheduler(self.args, self.optimizer)
if args.amp:
model, optimizer = amp.initialize(
self.model,
self.optimizer._optimizer,
opt_level=self.args.amp_level if self.args.amp_level else 'O2',
max_loss_scale=2**15,
cast_model_outputs=torch.float16
)
if self.args.distributed_world_size > 1:
self.model = DDP(model)
self._buffered_stats = defaultdict(lambda: [])
self._flat_grads = None
self._num_updates = 0
self._num_val_iterations = 0
self._optim_history = None
self.throughput_meter = TimeMeter()
def save_checkpoint(self, filename, extra_state):
"""Save all training state in a checkpoint file."""
if self.args.amp:
extra_state['amp_state_dict'] = amp.state_dict()
extra_state['amp_master_params'] = list(amp.master_params(self.optimizer.optimizer))
if distributed_utils.is_master(self.args): # only save one checkpoint
utils.save_state(
filename, self.args, self.get_model(), self.criterion, self.optimizer,
self.lr_scheduler, self._num_updates, self._optim_history, extra_state,
)
def load_checkpoint(self, filename, load_optim=True):
"""Load all training state from a checkpoint file."""
extra_state, optim_history, last_optim_state = \
utils.load_model_state(filename, self.get_model())
if last_optim_state is not None:
# rebuild optimizer after loading model, since params may have changed
#self.optimizer = optim.build_optimizer(self.args, self.model.parameters())
self.lr_scheduler = lr_scheduler.build_lr_scheduler(self.args, self.optimizer)
if load_optim:
self._optim_history = optim_history
# only reload optimizer and lr_scheduler if they match
last_optim = self._optim_history[-1]
if last_optim['criterion_name'] == self.criterion.__class__.__name__:
self.lr_scheduler.load_state_dict(last_optim['lr_scheduler_state'])
if last_optim['optimizer_name'] == self.optimizer.__class__.__name__:
self.optimizer.load_state_dict(last_optim_state)
self._num_updates = last_optim['num_updates']
if self.args.amp and extra_state is not None and 'amp_state_dict' in extra_state:
self.optimizer.optimizer._lazy_init_maybe_master_weights()
self.optimizer.optimizer._amp_stash.lazy_init_called = True
self.optimizer.optimizer.load_state_dict(last_optim_state)
for param, saved_param in zip(amp.master_params(self.optimizer.optimizer), extra_state['amp_master_params']):
param.data.copy_(saved_param.data)
amp.load_state_dict(extra_state['amp_state_dict'])
return extra_state
def train_step(self, sample, update_params=True, last_step=False):
"""Do forward, backward and parameter update."""
# Set seed based on args.seed and the update number so that we get
# reproducible results when resuming from checkpoints
seed = self.args.seed + self.get_num_updates()
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
self.model.train()
if isinstance(self.model, DDP):
if last_step:
self.model.disable_allreduce()
else:
self.model.enable_allreduce()
# forward and backward pass
sample, sample_size = self._prepare_sample(sample)
loss, oom_fwd = self._forward(sample)
# If this is a last batch forward pass is skipped on some workers
# Batch with sample_size 0 is not accounted for in weighted loss
logging_output = {
'ntokens': sample['ntokens'] if sample is not None else 0,
'nsentences': sample['target'].size(0) if sample is not None else 0,
'loss': utils.item(loss.data) if loss is not None else 0,
'sample_size': sample_size
}
oom_bwd = self._backward(loss)
# buffer stats and logging outputs
self._buffered_stats['sample_sizes'].append(sample_size)
self._buffered_stats['logging_outputs'].append(logging_output)
self._buffered_stats['ooms_fwd'].append(oom_fwd)
self._buffered_stats['ooms_bwd'].append(oom_bwd)
# update parameters
if update_params and not last_step:
# gather logging outputs from all replicas
sample_sizes = self._buffered_stats['sample_sizes']
logging_outputs = self._buffered_stats['logging_outputs']
ooms_fwd = self._buffered_stats['ooms_fwd']
ooms_bwd = self._buffered_stats['ooms_bwd']
if self.args.distributed_world_size > 1:
sample_sizes, logging_outputs, ooms_fwd, ooms_bwd = map(
lambda l: list(chain.from_iterable(l)),
zip(*distributed_utils.all_gather_list(
(sample_sizes, logging_outputs, ooms_fwd, ooms_bwd)
))
)
ooms_fwd = sum(ooms_fwd)
ooms_bwd = sum(ooms_bwd)
ooms = ooms_fwd + ooms_bwd #this is always <= distributed_world_size
if ooms == self.args.distributed_world_size:
print('| WARNING: OOM in all workers, skipping batch')
self.zero_grad()
return
# aggregate stats and logging outputs
grad_denom = sum(sample_sizes)
for p in self.model.parameters():
if p.requires_grad and not p.grad is None:
p.grad /= grad_denom
self._opt()
# Handle logging
sample_size = sum(log.get('sample_size', 0) for log in logging_outputs)
ntokens = sum(log.get('ntokens', 0) for log in logging_outputs)
self.throughput_meter.update(ntokens)
info_log_data = {
'tokens/s':self.throughput_meter.avg,
'tokens':ntokens,
'loss':sum(log.get('loss', 0) for log in logging_outputs) / sample_size / math.log(2)
}
debug_log_data = {
'batch_size':sum(log.get('nsentences', 0) for log in logging_outputs),
'lr':self.get_lr(),
'grad_denom':grad_denom,
'updates':1
}
DLLogger.log(step=self._num_updates, data=info_log_data, verbosity=0)
DLLogger.log(step=self._num_updates, data=debug_log_data, verbosity=1)
self.clear_buffered_stats()
def _forward(self, sample):
loss = None
oom = 0
try:
if sample is not None:
# calculate loss and sample size
logits, _ = self.model(**sample['net_input'])
target = sample['target']
if not self.args.adaptive_softmax_cutoff:
probs = F.log_softmax(logits, dim=-1, dtype=torch.float32)
else:
#TODO: trainig crashes after couple hundred iterations because of unknown
#error in the PyTorch's autograd
probs, target = self.get_model().decoder.adaptive_softmax(logits, target.view(-1))
loss = self.criterion(probs, target)
except RuntimeError as e:
if not eval and 'out of memory' in str(e):
print('| WARNING: ran out of memory in worker {}, skipping batch'.format(self.args.distributed_rank), force=True)
oom = 1
loss = None
else:
raise e
return loss, oom
def _backward(self, loss):
oom = 0
if loss is not None:
try:
if self.args.amp:
with amp.scale_loss(loss, self.optimizer._optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
except RuntimeError as e:
if 'out of memory' in str(e):
print('| WARNING: ran out of memory in worker {}, skipping batch'.format(self.args.distributed_rank), force=True)
oom = 1
self.zero_grad()
else:
raise e
return oom
def _opt(self):
# take an optimization step
self.optimizer.step()
self.zero_grad()
self._num_updates += 1
# update learning rate
self.lr_scheduler.step_update(self._num_updates)
def valid_step(self, sample):
"""Do forward pass in evaluation mode."""
self.model.eval()
self._num_val_iterations += 1
# forward pass
sample, sample_size = self._prepare_sample(sample)
with torch.no_grad():
loss, oom_fwd = self._forward(sample)
logging_output = {
'ntokens': sample['ntokens'] if sample is not None else 0,
'nsentences': sample['target'].size(0) if sample is not None else 0,
'sample_size': sample_size
}
loss = loss.item() if loss is not None else 0
assert not oom_fwd, 'Ran out of memory during validation'
# gather logging outputs from all GPUs
if self.args.distributed_world_size > 1:
losses, sample_sizes, logging_outputs = zip(*distributed_utils.all_gather_list(
(loss, sample_size, logging_output)
))
else:
losses = [loss]
sample_sizes = [sample_size]
logging_outputs = [logging_output]
# TODO: check when ntokens != sample_size
ntokens = sum(log.get('ntokens', 0) for log in logging_outputs)
weight = sum(log.get('sample_size', 0) for log in logging_outputs)
scaled_loss = sum(losses) / weight / math.log(2)
return scaled_loss
def dummy_train_step(self, dummy_batch):
"""Dummy training step for warming caching allocator."""
self.train_step(dummy_batch, update_params=False)
self.zero_grad()
self.clear_buffered_stats()
def zero_grad(self):
self.optimizer.zero_grad()
def clear_buffered_stats(self):
self._buffered_stats.clear()
def lr_step(self, epoch, val_loss=None):
"""Adjust the learning rate based on the validation loss."""
return self.lr_scheduler.step(epoch, val_loss)
def lr_step_update(self, num_updates):
"""Update the learning rate after each update."""
return self.lr_scheduler.step_update(num_updates)
def get_lr(self):
"""Get the current learning rate."""
return self.optimizer.get_lr()
def get_throughput_meter(self):
"""Get the throughput meter"""
return self.throughput_meter
def get_model(self):
"""Get the model replica."""
return self.model.module if isinstance(self.model, DDP) else self.model
def get_num_updates(self):
"""Get the number of parameters updates."""
return self._num_updates
def _prepare_sample(self, sample):
if sample is None or len(sample) == 0:
return None, 0
return utils.move_to_cuda(sample), sample['ntokens']
class FlowNMT(nn.Module):
"""
NMT model with Generative Flow.
"""
def __init__(self, core: FlowNMTCore):
super(FlowNMT, self).__init__()
self.core = core
self.length_unit = self.core.prior.length_unit
self.distribured_enabled = False
def _get_core(self):
return self.core.module if self.distribured_enabled else self.core
def sync(self):
core = self._get_core()
core.prior.sync()
def init(self, src_sents, tgt_sents, src_masks, tgt_masks, init_scale=1.0):
core = self._get_core()
core.init(src_sents, tgt_sents, src_masks, tgt_masks, init_scale=init_scale)
def init_posterior(self, src_sents, tgt_sents, src_masks, tgt_masks, init_scale=1.0):
core = self._get_core()
core.init_posterior(src_sents, tgt_sents, src_masks, tgt_masks, init_scale=init_scale)
def init_prior(self, src_sents, tgt_sents, src_masks, tgt_masks, init_scale=1.0):
core = self._get_core()
core.init_prior(src_sents, tgt_sents, src_masks, tgt_masks, init_scale=init_scale)
def reconstruct(self, src_sents: torch.Tensor, tgt_sents: torch.Tensor,
src_masks: torch.Tensor, tgt_masks: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
return self._get_core().reconstruct(src_sents, tgt_sents, src_masks, tgt_masks)
def translate_argmax(self, src_sents: torch.Tensor, src_masks: torch.Tensor,
n_tr: int = 1, tau: float = 0.0) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Args:
src_sents: Tensor [batch, src_length]
tensor for source sentences
src_masks: Tensor [batch, src_length] or None
tensor for source masks
n_tr: int (default 1)
number of translations per sentence per length candidate
tau: float (default 0.0)
temperature
Returns: Tensor1, Tensor2
Tensor1: tensor for translations [batch, tgt_length]
Tensor2: lengths [batch]
"""
return self._get_core().translate_argmax(src_sents, src_masks, n_tr=n_tr, tau=tau)
def translate_iw(self, src_sents: torch.Tensor, src_masks: torch.Tensor,
n_len: int = 1, n_tr: int = 1,
tau: float = 0.0, k: int = 1) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Args:
src_sents: Tensor [batch, src_length]
tensor for source sentences
src_masks: Tensor [batch, src_length] or None
tensor for source masks
n_len: int (default 1)
number of length candidates
n_tr: int (default 1)
number of translations per sentence per length candidate
tau: float (default 0.0)
temperature
k: int (default 1)
number of samples for importance weighted sampling
Returns: Tensor1, Tensor2
Tensor1: tensor for translations [batch, tgt_length]
Tensor2: lengths [batch]
"""
return self._get_core().translate_iw(src_sents, src_masks, n_len=n_len, n_tr=n_tr,
tau=tau, k=k)
def translate_sample(self, src_sents: torch.Tensor, src_masks: torch.Tensor,
n_len: int = 1, n_tr: int = 1, tau: float = 0.0) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Args:
src_sents: Tensor [batch, src_length]
tensor for source sentences
src_masks: Tensor [batch, src_length] or None
tensor for source masks
n_len: int (default 1)
number of length candidates
n_tr: int (default 1)
number of translations per sentence per length candidate
tau: float (default 0.0)
temperature
Returns: Tensor1, Tensor2
Tensor1: tensor for translations [batch * n_len * n_tr, tgt_length]
Tensor2: lengths [batch * n_len * n_tr]
"""
return self._get_core().translate_sample(src_sents, src_masks, n_len=n_len, n_tr=n_tr, tau=tau)
def reconstruct_error(self, src_sents: torch.Tensor, tgt_sents: torch,
src_masks: torch.Tensor, tgt_masks: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Args:
src_sents: Tensor [batch, src_length]
tensor for source sentences
tgt_sents: Tensor [batch, tgt_length]
tensor for target sentences
src_masks: Tensor [batch, src_length] or None
tensor for source masks
tgt_masks: Tensor [batch, tgt_length] or None
tensor for target masks
Returns: Tensor1, Tensor2
Tensor1: reconstruction error [batch]
Tensor2: length loss [batch]
"""
return self.core(src_sents, tgt_sents, src_masks, tgt_masks, only_recon_loss=True)
def loss(self, src_sents: torch.Tensor, tgt_sents: torch,
src_masks: torch.Tensor, tgt_masks: torch.Tensor,
nsamples: int = 1, eval=False) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Args:
src_sents: Tensor [batch, src_length]
tensor for source sentences
tgt_sents: Tensor [batch, tgt_length]
tensor for target sentences
src_masks: Tensor [batch, src_length] or None
tensor for source masks
tgt_masks: Tensor [batch, tgt_length] or None
tensor for target masks
nsamples: int
number of samples
eval: bool
if eval, turn off distributed mode
Returns: Tensor1, Tensor2, Tensor3
Tensor1: reconstruction error [batch]
Tensor2: KL [batch]
Tensor3: length loss [batch]
"""
core = self._get_core() if eval else self.core
return core(src_sents, tgt_sents, src_masks, tgt_masks, nsamples=nsamples)
def init_distributed(self, rank, local_rank):
assert not self.distribured_enabled
self.distribured_enabled = True
print("Initializing Distributed, rank {}, local rank {}".format(rank, local_rank))
dist.init_process_group(backend='nccl', rank=rank)
torch.cuda.set_device(local_rank)
self.core = DistributedDataParallel(self.core)
def sync_params(self):
assert self.distribured_enabled
core = self._get_core()
flat_dist_call([param.data for param in core.parameters()], dist.all_reduce)
self.core.needs_refresh = True
def enable_allreduce(self):
assert self.distribured_enabled
self.core.enable_allreduce()
def disable_allreduce(self):
assert self.distribured_enabled
self.core.disable_allreduce()
def save(self, model_path):
model = {'core': self._get_core().state_dict()}
model_name = os.path.join(model_path, 'model.pt')
torch.save(model, model_name)
def save_core(self, path):
core = self._get_core()
model = {'prior': core.prior.state_dict(),
'encoder': core.encoder.state_dict(),
'decoder': core.decoder.state_dict(),
'posterior': core.posterior.state_dict()}
torch.save(model, path)
def load_core(self, path, device, load_prior=True):
model = torch.load(path, map_location=device)
core = self._get_core()
core.posterior.load_state_dict(model['posterior'])
core.encoder.load_state_dict(model['encoder'])
core.decoder.load_state_dict(model['decoder'])
if load_prior:
core.prior.load_state_dict(model['prior'])
@classmethod
def load(cls, model_path, device):
params = json.load(open(os.path.join(model_path, 'config.json'), 'r'))
flownmt = FlowNMT.from_params(params).to(device)
model_name = os.path.join(model_path, 'model.pt')
model = torch.load(model_name, map_location=device)
flownmt.core.load_state_dict(model['core'])
return flownmt
@classmethod
def from_params(cls, params: Dict) -> "FlowNMT":
src_vocab_size = params.pop('src_vocab_size')
tgt_vocab_size = params.pop('tgt_vocab_size')
embed_dim = params.pop('embed_dim')
latent_dim = params.pop('latent_dim')
hidden_size = params.pop('hidden_size')
max_src_length = params.pop('max_src_length')
max_tgt_length = params.pop('max_tgt_length')
src_pad_idx = params.pop('src_pad_idx')
tgt_pad_idx = params.pop('tgt_pad_idx')
share_embed = params.pop('share_embed')
tie_weights = params.pop('tie_weights')
# prior
prior_params = params.pop('prior')
prior_params['flow']['features'] = latent_dim
prior_params['flow']['src_features'] = latent_dim
prior_params['length_predictor']['features'] = latent_dim
prior_params['length_predictor']['max_src_length'] = max_src_length
prior = Prior.by_name(prior_params.pop('type')).from_params(prior_params)
# eocoder
encoder_params = params.pop('encoder')
encoder_params['vocab_size'] = src_vocab_size
encoder_params['embed_dim'] = embed_dim
encoder_params['padding_idx'] = src_pad_idx
encoder_params['latent_dim'] = latent_dim
encoder_params['hidden_size'] = hidden_size
encoder = Encoder.by_name(encoder_params.pop('type')).from_params(encoder_params)
# posterior
posterior_params = params.pop('posterior')
posterior_params['vocab_size'] = tgt_vocab_size
posterior_params['embed_dim'] = embed_dim
posterior_params['padding_idx'] = tgt_pad_idx
posterior_params['latent_dim'] = latent_dim
posterior_params['hidden_size'] = hidden_size
_shared_embed = encoder.embed if share_embed else None
posterior_params['_shared_embed'] = _shared_embed
posterior = Posterior.by_name(posterior_params.pop('type')).from_params(posterior_params)
# decoder
decoder_params = params.pop('decoder')
decoder_params['vocab_size'] = tgt_vocab_size
decoder_params['latent_dim'] = latent_dim
decoder_params['hidden_size'] = hidden_size
_shared_weight = posterior.tgt_embed.weight if tie_weights else None
decoder_params['_shared_weight'] = _shared_weight
decoder = Decoder.by_name(decoder_params.pop('type')).from_params(decoder_params)
return FlowNMT(FlowNMTCore(encoder, prior, posterior, decoder))
class WolfModel(nn.Module):
"""
Variational Auto-Encoding Generative Flow
"""
def __init__(self, core: WolfCore):
super(WolfModel, self).__init__()
self.core = core
self.distribured_enabled = False
def _get_core(self):
return self.core.module if self.distribured_enabled else self.core
def sync(self):
core = self._get_core()
core.sync()
def init(self, x, y=None, init_scale=1.0):
core = self._get_core()
core.init(x, y=y, init_scale=init_scale)
def init_distributed(self, rank, local_rank):
assert not self.distribured_enabled
self.distribured_enabled = True
print("Initializing Distributed, rank {}, local rank {}".format(
rank, local_rank))
dist.init_process_group(backend='nccl', rank=rank)
torch.cuda.set_device(local_rank)
self.core = DistributedDataParallel(convert_syncbn_model(self.core))
def enable_allreduce(self):
assert self.distribured_enabled
self.core.enable_allreduce()
def disable_allreduce(self):
assert self.distribured_enabled
self.core.disable_allreduce()
def encode_global(self, data, y=None, n_bits=8, nsamples=1, random=False):
"""
Args:
data: Tensor [batch, channels, height, width]
input data
y: Tensor or None
class labels for x or None.
n_bits: int (default 8)
number of bits for image data.
nsamples: int (default 1)
number of samples for each image.
random: bool (default False)
incorporating randomness.
Returns: Tensor
tensor for global encoding [batch, nsamples, dim] or None
"""
return self._get_core().encode_global(data,
y=y,
n_bits=n_bits,
nsamples=nsamples,
random=random)
def encode(self,
data,
y=None,
n_bits=8,
nsamples=1,
random=False) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Args:
data: Tensor [batch, channels, height, width]
input data
y: Tensor or None
class labels for x or None.
n_bits: int (default 8)
number of bits for image data.
nsamples: int (default 1)
number of samples for each image.
random: bool (default False)
incorporating randomness.
Returns: Tensor1, Tensor2
Tensor1: epsilon [batch, channels, height width]
Tensor2: z [batch, dim] or None
"""
return self._get_core().encode(data,
y=y,
n_bits=n_bits,
nsamples=nsamples,
random=random)
def decode(self, epsilon, z=None, n_bits=8) -> torch.Tensor:
"""
Args:
epsilon: Tensor [batch, channels, height, width]
epslion for generation
z: Tensor or None [batch, dim]
conditional input
n_bits: int (default 8)
number of bits for image data.
Returns: generated tensor [nums, channels, height, width]
"""
return self._get_core().decode(epsilon, z=z, n_bits=n_bits)
def decode_with_attn(self, epsilon, z=None, n_bits=8):
return self._get_core().decode_with_attn(epsilon, z=z, n_bits=n_bits)
def synthesize(self,
nums,
image_size,
tau=1.0,
n_bits=8,
device=torch.device('cpu')) -> torch.Tensor:
"""
Args:
nums: int
number of synthesis
image_size: size of tuple
the size of the synthesized images with shape [channels, height, width]
tau: float (default 1.0)
temperature
n_bits: int (default 8)
number of bits for image data.
device: torch.device
device to store the synthesis
Returns: generated tensor [nums, channels, height, width]
"""
return self._get_core().synthesize(nums,
image_size,
tau=tau,
n_bits=n_bits,
device=device)
def loss(self, data, y=None, n_bits=8, nsamples=1) -> \
Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Args:
data: Tensor [batch, channels, height, width]
input data.
y: Tensor or None
class labels for x or None.
n_bits: int (default 8)
number of bits for image data.
nsamples: int (default 1)
number of samples for compute the loss.
Returns: Tensor1, Tensor2, Tensor3
Tensor1: generation loss [batch]
Tensor2: KL [batch]
Tensor3: dequantization loss [batch]
"""
core = self._get_core() if not self.training else self.core
return core(data, y=y, n_bits=n_bits, nsamples=nsamples)
def loss_attn(self, data, y=None, n_bits=8, nsamples=1) -> \
Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Args:
data: Tensor [batch, channels, height, width]
input data.
y: Tensor or None
class labels for x or None.
n_bits: int (default 8)
number of bits for image data.
nsamples: int (default 1)
number of samples for compute the loss.
Returns: Tensor1, Tensor2, Tensor3
Tensor1: generation loss [batch]
Tensor2: KL [batch]
Tensor3: dequantization loss [batch]
"""
core = self._get_core() if not self.training else self.core
return core.forward_attn(data, y=y, n_bits=n_bits, nsamples=nsamples)
def to_device(self, device):
assert not self.distribured_enabled
self.core.discriminator.to_device(device)
return self.to(device)
def save(self, model_path, version=None):
model = {'core': self._get_core().state_dict()}
if version is not None:
model_name = os.path.join(model_path, 'model{}.pt'.format(version))
else:
model_name = os.path.join(model_path, 'model.pt')
torch.save(model, model_name)
@classmethod
def load(cls, model_path, device, version=None):
params = json.load(open(os.path.join(model_path, 'config.json'), 'r'))
flowae = WolfModel.from_params(params).to_device(device)
if version != None:
model_name = os.path.join(model_path, 'model{}.pt'.format(version))
else:
model_name = os.path.join(model_path, 'model.pt')
model = torch.load(model_name, map_location=device)
flowae.core.load_state_dict(model['core'])
return flowae
@classmethod
def from_params(cls, params: Dict) -> "WolfModel":
# discriminator
disc_params = params.pop('discriminator')
discriminator = Discriminator.by_name(
disc_params.pop('type')).from_params(disc_params)
# dequantizer
dequant_params = params.pop('dequantizer')
dequantizer = DeQuantizer.by_name(
dequant_params.pop('type')).from_params(dequant_params)
# generator
generator_params = params.pop('generator')
generator = Generator.from_params(generator_params)
return WolfModel(WolfCore(generator, discriminator, dequantizer))
