def __init__(self, search_space=None, **kwargs):
super(SpNasCodec, self).__init__(search_space, **kwargs)
config_template_file = search_space.config_template_file
assert config_template_file is not None
self.config_template = Config(config_template_file)
if 'epoch' in search_space.keys():
self.config_template['total_epochs'] = search_space.epoch
def __init__(self, hps=None, **kwargs):
"""Construct method."""
default_config = {
'batch_size': 1,
'num_workers': 0,
'shuffle': False,
'distributed': False,
'imgs_per_gpu': 1,
'pin_memory': True,
'drop_last': True
}
self.mode = "train"
if "mode" in kwargs.keys():
self.mode = kwargs["mode"]
if hps is not None:
self._init_hps(hps)
if 'common' in self.cfg.keys() and self.cfg['common'] is not None:
common = deepcopy(self.cfg['common'])
self.args = update_dict(common, deepcopy(self.cfg[self.mode]))
else:
self.args = deepcopy(self.cfg[self.mode])
self.args = update_dict(self.args, Config(default_config))
for key in kwargs.keys():
if key in self.args:
self.args[key] = kwargs[key]
self.train = self.mode in ["train", "val"]
transforms_list = self._init_transforms()
self._transforms = Transforms(transforms_list)
if "transforms" in kwargs.keys():
self._transforms.__transform__ = kwargs["transforms"]
self.sampler = self._init_sampler()
def _init_hps(self, hps=None):
"""Load hps from file."""
if hps is not None:
self.hps = hps
elif self.config.hps_file is not None:
desc_file = self.config.hps_file.replace("{local_base_path}",
self.local_base_path)
self.hps = Config(desc_file)
elif self.config.hps_folder is not None:
folder = self.config.hps_folder.replace("{local_base_path}",
self.local_base_path)
pattern = FileOps.join_path(folder, "desc_*.json")
desc_file = glob.glob(pattern)[0]
self.hps = Config(desc_file)
if self.hps and self.hps.get('trainer'):
load_conf_from_desc(self.config, self.hps.get('trainer'))
def import_torch_operators():
"""Import search space operators from torch."""
ops = Config()
for _name in dir(nn):
if _name.startswith("_"):
continue
_cls = getattr(nn, _name)
if not isclass(_cls):
continue
ops[_name] = ClassFactory.register_cls(_cls, ClassType.SEARCH_SPACE)
return ops
def __init__(self, hps=None, mode='train', **kwargs):
"""Construct method."""
super(Dataset, self).__init__()
self.args = dict()
self.mode = mode
if mode == "val" and not hasattr(self.config, "val"):
self.mode = "test"
# modify config from kwargs, `Cifar10(mode='test', data_path='/cache/datasets')`
if kwargs:
self.args = Config(kwargs)
if hasattr(self, 'config'):
config = obj2config(getattr(self.config, self.mode))
config.update(self.args)
self.args = config
self._init_hps(hps)
self.train = self.mode in ["train", "val"]
transforms_list = self._init_transforms()
self._transforms = Transforms(transforms_list)
if "transforms" in kwargs.keys():
self._transforms.__transform__ = kwargs["transforms"]
self.dataset_init()
self.sampler = self._init_sampler()
def _init_model(self, model=None):
"""Load model desc from save path and parse to model."""
if model is not None:
if vega.is_torch_backend() and self.use_cuda:
model = model.cuda()
return model
model_cfg = Config(ClassFactory.__configs__.get('model'))
if "model_desc_file" in model_cfg and model_cfg.model_desc_file is not None:
desc_file = model_cfg.model_desc_file
desc_file = desc_file.replace("{local_base_path}",
self.local_base_path)
if ":" not in desc_file:
desc_file = os.path.abspath(desc_file)
if ":" in desc_file:
local_desc_file = FileOps.join_path(
self.local_output_path, os.path.basename(desc_file))
FileOps.copy_file(desc_file, local_desc_file)
desc_file = local_desc_file
model_desc = Config(desc_file)
logging.info("net_desc:{}".format(model_desc))
elif "model_desc" in model_cfg and model_cfg.model_desc is not None:
model_desc = model_cfg.model_desc
elif "models_folder" in model_cfg and model_cfg.models_folder is not None:
folder = model_cfg.models_folder.replace("{local_base_path}",
self.local_base_path)
pattern = FileOps.join_path(folder, "desc_*.json")
desc_file = glob.glob(pattern)[0]
model_desc = Config(desc_file)
else:
return None
if model_desc is not None:
self.model_desc = model_desc
net_desc = NetworkDesc(model_desc)
model = net_desc.to_model()
if vega.is_torch_backend() and self.use_cuda:
model = model.cuda()
return model
else:
return None
def _decode_hps(hps):
"""Decode hps: `trainer.optim.lr : 0.1` to dict format.
And convert to `vega.core.common.config import Config` object
This Config will be override in Trainer or Datasets class
The override priority is: input hps > user configuration > default configuration
:param hps: hyper params
:return: dict
"""
hps_dict = {}
for hp_name, value in hps.items():
hp_dict = {}
for key in list(reversed(hp_name.split('.'))):
if hp_dict:
hp_dict = {key: hp_dict}
else:
hp_dict = {key: value}
# update cfg with hps
hps_dict = update_dict(hps_dict, hp_dict, [])
return Config(hps_dict)
def _init_hps(self, hps):
"""Convert trainer values in hps to cfg."""
if hps.get("dataset") is not None:
self.cfg.train = Config(update_dict(hps.dataset, self.cfg.train))
self.cfg = Config(update_dict(hps.trainer, self.cfg))
def _init_hps(self, hps):
"""Convert trainer values in hps to cfg."""
if hps is not None:
self.args = Config(update_dict(hps, self.args))
class Dataset(TaskOps):
"""This is the base class of the dataset, which is a subclass of `TaskOps`.
The Dataset provide several basic attribute like dataloader, transform and sampler.
"""
def __new__(cls, *args, **kwargs):
"""Create a subclass instance of dataset."""
if Dataset in cls.__bases__:
return super().__new__(cls)
if kwargs.get('type'):
t_cls = ClassFactory.get_cls(ClassType.DATASET, kwargs.pop('type'))
else:
t_cls = ClassFactory.get_cls(ClassType.DATASET)
return super().__new__(t_cls)
def __init__(self, hps=None, mode='train', **kwargs):
"""Construct method."""
super(Dataset, self).__init__()
self.args = dict()
self.mode = mode
if mode == "val" and not hasattr(self.config, "val"):
self.mode = "test"
# modify config from kwargs, `Cifar10(mode='test', data_path='/cache/datasets')`
if kwargs:
self.args = Config(kwargs)
if hasattr(self, 'config'):
config = obj2config(getattr(self.config, self.mode))
config.update(self.args)
self.args = config
self._init_hps(hps)
self.train = self.mode in ["train", "val"]
transforms_list = self._init_transforms()
self._transforms = Transforms(transforms_list)
if "transforms" in kwargs.keys():
self._transforms.__transform__ = kwargs["transforms"]
self.dataset_init()
self.sampler = self._init_sampler()
def dataset_init(self):
"""Init Dataset before sampler."""
pass
def _init_hps(self, hps):
"""Convert trainer values in hps to cfg."""
if hps is not None:
self.args = Config(update_dict(hps, self.args))
@property
def dataloader(self):
"""Dataloader arrtribute which is a unified interface to generate the data.
:return: a batch data
:rtype: dict, list, optional
"""
data_loader = torch_data.DataLoader(self,
batch_size=self.args.batch_size,
shuffle=self.args.shuffle,
num_workers=self.args.num_workers,
pin_memory=self.args.pin_memory,
sampler=self.sampler,
drop_last=self.args.drop_last)
return data_loader
@property
def transforms(self):
"""Transform function which can replace transforms."""
return self._transforms
@transforms.setter
def transforms(self, value):
"""Set function of transforms."""
if isinstance(value, list):
self.transforms.__transform__ = value
def _init_transforms(self):
"""Initialize transforms method.
:return: a list of object
:rtype: list
"""
if "transforms" in self.args.keys():
transforms = list()
if not isinstance(self.args.transforms, list):
self.args.transforms = [self.args.transforms]
for i in range(len(self.args.transforms)):
transform_name = self.args.transforms[i].pop("type")
kwargs = self.args.transforms[i]
if ClassFactory.is_exists(ClassType.TRANSFORM, transform_name):
transforms.append(ClassFactory.get_cls(ClassType.TRANSFORM, transform_name)(**kwargs))
else:
transforms.append(getattr(importlib.import_module('torchvision.transforms'),
transform_name)(**kwargs))
return transforms
else:
return list()
@property
def sampler(self):
"""Sampler function which can replace sampler."""
return self._sampler
@sampler.setter
def sampler(self, value):
"""Set function of sampler."""
self._sampler = value
def _init_sampler(self):
"""Initialize sampler method.
:return: if the distributed is True, return a sampler object, else return None
:rtype: an object or None
"""
if self.args.distributed:
rank, world_size = get_dist_info()
self.args.shuffle = False
sampler = DistributedSampler(self,
num_replicas=world_size,
rank=rank,
shuffle=self.args.shuffle)
else:
sampler = None
return sampler
def __len__(self):
"""Get the length of the dataset."""
raise NotImplementedError
def __getitem__(self, index):
"""Get an item of the dataset according to the index."""
raise NotImplementedError
class Trainer(DistributedWorker):
"""Trainer class.
:param model: input model, defaults to None
:type model: tf model, optional
:param id: id of the model, defaults to None
:type id: int, optional
:param hps: hyperparameters, defaults to None
:type hps: dict, optional
"""
# __worker_id__ = 0
config = TrainerConfig()
def __init__(self,
model=None,
id=None,
hps=None,
load_ckpt_flag=False,
**kwargs):
super(Trainer, self).__init__()
self.worker_type = WorkerTypes.TRAINER
Trainer.__worker_id__ += 1
if id is not None:
self._worker_id = id
else:
self._worker_id = Trainer.__worker_id__
# Data Memeber list of Trainer
self.is_chief = True
self.use_cuda = self.config.cuda
self.epochs = self.config.epochs
self.do_validation = True
self.auto_save_ckpt = True
self.auto_save_perf = True
self.skip_train = False
self.valid_interval = self.config.valid_interval
self.hps = hps
self.model = model
self.optimizer = None
self.lr_scheduler = None
self.loss = None
self.use_syncbn = self.config.syncbn
self.use_amp = self.config.amp
self.train_metrics = None
self.valid_metrics = None
self.call_metrics_on_train = self.config.call_metrics_on_train
self.train_loader = None
self.valid_loader = None
self.train_step = None
self.valid_step = None
self.make_batch = None
self.model_fn = None
self.train_input_fn = None
self.valid_input_fn = None
self.callbacks = None
self.performance = None
self.model_desc = {}
self.visual_data = {}
self.load_ckpt_flag = load_ckpt_flag
self.checkpoint_file_name = 'checkpoint.pth'
self.model_pickle_file_name = 'model.pkl'
self.model_path = FileOps.join_path(self.get_local_worker_path(),
self.model_pickle_file_name)
self.checkpoint_file = FileOps.join_path(self.get_local_worker_path(),
self.checkpoint_file_name)
self.weights_file = FileOps.join_path(
self.get_local_worker_path(),
"model_{}.pth".format(self.worker_id))
self.distributed = self.config.distributed
# Used by TimmTrainerCallbacks since it builds its trainer in
# the before_train callback
self.lazy_built = self.config.lazy_built
# Indicate whether the necessary components of a trainer
# has been built for running
self.has_built = False
self._world_size = 1
self._rank_id = 0
self._local_rank_id = 0
self.config.kwargs = kwargs
def train_process(self):
"""Whole train process of the TrainWorker specified in config.
After training, the model and validation results are saved to local_worker_path and s3_path.
"""
init_log(log_file="worker_{}.txt".format(self.worker_id))
logging.debug("Use the unified Trainer")
if not self.lazy_built:
self.build(model=self.model,
hps=self.hps,
load_ckpt_flag=self.load_ckpt_flag)
self._init_callbacks(self.callbacks)
self._train_loop()
def build(self,
model=None,
optimizer=None,
loss=None,
lr_scheduler=None,
metrics=None,
hps=None,
callbacks=None,
train_loader=None,
valid_loader=None,
make_batch=None,
train_step=None,
valid_step=None,
model_fn=None,
train_input_fn=None,
valid_input_fn=None,
load_ckpt_flag=False,
checkpoint_file_name="checkpoint.pth",
model_pickle_file_name="model.pkl"):
"""Build the trainer by assembling the necessary components."""
# Intitialize hyperparameters by parameters or configurations
self._init_hps(hps)
logging.debug("Trainer Config: {}".format(obj2config(self.config)))
self.checkpoint_file_name = checkpoint_file_name
self.model_pickle_file_name = model_pickle_file_name
if vega.is_torch_backend():
self._init_step_functions(make_batch, train_step, valid_step)
elif vega.is_tf_backend():
self._init_estimator_fn(model_fn, train_input_fn, valid_input_fn)
self._init_tf_session()
self._init_distributed_setting()
self._init_cuda_setting()
self._init_tf_estimator()
self.do_validation = self.config.with_valid
self.model = self._init_model(model)
self.load_ckpt_flag = load_ckpt_flag
if self.load_ckpt_flag:
self.load_checkpoint()
else:
self._load_pretrained_model()
self.use_syncbn = self.config.syncbn
if self.use_syncbn and vega.is_torch_backend():
self.model = apex.parallel.convert_syncbn_model(self.model)
self.train_loader = self._init_dataloader(mode='train',
loader=train_loader)
self.valid_loader = self._init_dataloader(mode='val',
loader=valid_loader)
if vega.is_torch_backend():
self.optimizer = Optimizer()(model=self.model, distributed=self.distributed) \
if optimizer is None else optimizer
self.loss = Loss()() if loss is None else loss
self.lr_scheduler = LrScheduler()(
self.optimizer) if lr_scheduler is None else lr_scheduler
# Some trainer has different train batch size from valid batch
self.train_metrics = self._init_metrics(
metrics) if vega.is_torch_backend() else None
self.valid_metrics = self._init_metrics(metrics)
self._init_horovod_setting()
if self.use_amp and vega.is_torch_backend():
self.model, self.optimizer = amp.initialize(self.model,
self.optimizer,
opt_level='O1')
if self.callbacks is None:
self.callbacks = callbacks
# self.output_model_desc()
cur_working_dir = FileOps.join_path(self.local_output_path,
self.step_name)
FileOps.make_dir(cur_working_dir)
# Make sure Trainer has been built for training
self.has_built = True
def _init_cuda_setting(self):
"""Init CUDA setting."""
if not vega.is_torch_backend():
return
if not self.config.cuda:
self.config.device = -1
return
self.config.device = self.config.cuda if self.config.cuda is not True else 0
self.use_cuda = True
if self.distributed:
torch.cuda.set_device(self._local_rank_id)
torch.cuda.manual_seed(self.config.seed)
def _init_distributed_setting(self):
if not self.distributed:
return
if vega.is_npu_device():
self.npu_init = npu_ops.initialize_system()
self.npu_shutdown = npu_ops.shutdown_system()
self.sess.run(self.npu_init)
self._world_size = hvd.size() if vega.is_gpu_device(
) else get_rank_size()
self._rank_id = hvd.rank() if vega.is_gpu_device() else get_rank_id()
self._local_rank_id = hvd.local_rank() if vega.is_gpu_device(
) else get_local_rank_id()
def _init_horovod_setting(self):
"""Init horovod setting."""
self.is_chief = True
if self.distributed and vega.is_torch_backend():
hvd.broadcast_parameters(self.model.state_dict(), root_rank=0)
hvd.broadcast_optimizer_state(self.optimizer, root_rank=0)
if hvd.rank() != 0:
self.is_chief = False
else:
self.is_chief = True
def _init_hps(self, hps=None):
"""Load hps from file."""
if hps is not None:
self.hps = hps
elif self.config.hps_file is not None:
desc_file = self.config.hps_file.replace("{local_base_path}",
self.local_base_path)
self.hps = Config(desc_file)
elif self.config.hps_folder is not None:
folder = self.config.hps_folder.replace("{local_base_path}",
self.local_base_path)
pattern = FileOps.join_path(folder, "desc_*.json")
desc_file = glob.glob(pattern)[0]
self.hps = Config(desc_file)
if self.hps and self.hps.get('trainer'):
load_conf_from_desc(self.config, self.hps.get('trainer'))
def _init_model(self, model=None):
"""Load model desc from save path and parse to model."""
if model is not None:
if vega.is_torch_backend() and self.use_cuda:
model = model.cuda()
return model
model_cfg = Config(ClassFactory.__configs__.get('model'))
if "model_desc_file" in model_cfg and model_cfg.model_desc_file is not None:
desc_file = model_cfg.model_desc_file
desc_file = desc_file.replace("{local_base_path}",
self.local_base_path)
if ":" not in desc_file:
desc_file = os.path.abspath(desc_file)
if ":" in desc_file:
local_desc_file = FileOps.join_path(
self.local_output_path, os.path.basename(desc_file))
FileOps.copy_file(desc_file, local_desc_file)
desc_file = local_desc_file
model_desc = Config(desc_file)
logging.info("net_desc:{}".format(model_desc))
elif "model_desc" in model_cfg and model_cfg.model_desc is not None:
model_desc = model_cfg.model_desc
elif "models_folder" in model_cfg and model_cfg.models_folder is not None:
folder = model_cfg.models_folder.replace("{local_base_path}",
self.local_base_path)
pattern = FileOps.join_path(folder, "desc_*.json")
desc_file = glob.glob(pattern)[0]
model_desc = Config(desc_file)
else:
return None
if model_desc is not None:
self.model_desc = model_desc
net_desc = NetworkDesc(model_desc)
model = net_desc.to_model()
if vega.is_torch_backend() and self.use_cuda:
model = model.cuda()
return model
else:
return None
def _load_pretrained_model(self):
if self.model is None:
return
if self.config.pretrained_model_file is not None:
model_file = self.config.pretrained_model_file
model_file = os.path.abspath(model_file)
if vega.is_torch_backend():
ckpt = torch.load(model_file)
self.model.load_state_dict(ckpt)
elif vega.is_tf_backend():
model_folder = os.path.dirname(model_file)
FileOps.copy_folder(model_folder, self.get_local_worker_path())
return
def load_checkpoint(self,
worker_id=None,
step_name=None,
saved_folder=None):
"""Load checkpoint."""
if saved_folder is None:
if worker_id is None:
worker_id = self.worker_id
if step_name is None:
step_name = self.step_name
saved_folder = self.get_local_worker_path(step_name, worker_id)
checkpoint_file = FileOps.join_path(saved_folder,
self.checkpoint_file_name)
model_pickle_file = FileOps.join_path(saved_folder,
self.model_pickle_file_name)
try:
with open(model_pickle_file, 'rb') as f:
model = pickle.load(f)
if vega.is_torch_backend():
ckpt = torch.load(checkpoint_file,
map_location=torch.device('cpu'))
model.load_state_dict(ckpt['weight'])
if self.config.cuda:
model = model.cuda()
elif vega.is_tf_backend():
FileOps.copy_folder(saved_folder,
self.get_local_worker_path())
self.model = model
except Exception:
logging.info(
'Checkpoint file is not existed, use default model now.')
return
def _init_metrics(self, metrics=None):
"""Init metrics."""
if metrics is not None:
return metrics
else:
return Metrics()
def _init_dataloader(self, mode, loader=None):
"""Init dataloader."""
if loader is not None:
return loader
if mode == "train" and self.hps is not None and self.hps.get(
"dataset") is not None:
dataset_cls = ClassFactory.get_cls(ClassType.DATASET)
dataset = dataset_cls(mode=mode, hps=self.hps.get("dataset"))
else:
dataset_cls = ClassFactory.get_cls(ClassType.DATASET)
dataset = dataset_cls(mode=mode)
if vega.is_torch_backend():
if self.distributed:
sampler = torch.utils.data.distributed.DistributedSampler(
dataset, num_replicas=hvd.size(), rank=hvd.rank())
dataset.sampler = sampler
return dataset.dataloader
elif vega.is_tf_backend():
if self.distributed:
dataset.set_distributed(self._world_size, self._rank_id)
return dataset
def _train_loop(self):
"""Do the training with data, callbacks and step functions etc."""
# Allow user to build trainer in before_train() callback, but they
# should set lazy_built in configuration file to True
self.callbacks.before_train()
if self.skip_train:
return
for epoch in range(self.epochs):
epoch_logs = {'train_num_batches': len(self.train_loader)}
if self.do_validation:
epoch_logs.update(
{'valid_num_batches': len(self.valid_loader)})
self.callbacks.before_epoch(epoch, epoch_logs)
self._train_epoch()
if self.do_validation and self._should_run_validation(epoch):
self._valid_epoch()
self.callbacks.after_epoch(epoch)
self.callbacks.after_train()
if self.distributed:
self._shutdown_distributed()
def _train_epoch(self):
if vega.is_torch_backend():
self.model.train()
for batch_index, batch in enumerate(self.train_loader):
batch = self.make_batch(batch)
batch_logs = {'train_batch': batch}
self.callbacks.before_train_step(batch_index, batch_logs)
train_batch_output = self.train_step(batch)
batch_logs.update(train_batch_output)
if self.config.is_detection_trainer:
batch_logs.update({'is_detection_trainer': True})
self.callbacks.after_train_step(batch_index, batch_logs)
elif vega.is_tf_backend():
self.estimator.train(input_fn=self.train_input_fn,
steps=len(self.train_loader),
hooks=self._init_logging_hook())
def _valid_epoch(self):
self.callbacks.before_valid()
valid_logs = None
if vega.is_torch_backend():
self.model.eval()
with torch.no_grad():
for batch_index, batch in enumerate(self.valid_loader):
batch = self.make_batch(batch)
batch_logs = {'valid_batch': batch}
self.callbacks.before_valid_step(batch_index, batch_logs)
valid_batch_output = self.valid_step(batch)
self.callbacks.after_valid_step(batch_index,
valid_batch_output)
elif vega.is_tf_backend():
eval_metrics = self.estimator.evaluate(
input_fn=self.valid_input_fn, steps=len(self.valid_loader))
self.valid_metrics.update(eval_metrics)
valid_logs = dict()
valid_logs['cur_valid_perfs'] = self.valid_metrics.results
self.callbacks.after_valid(valid_logs)
def _init_step_functions(self,
make_batch=None,
train_step=None,
valid_step=None):
# Init make_batch function by user or using the default one
if self.make_batch is None:
if make_batch is not None:
self.make_batch = make_batch
else:
self.make_batch = self._default_make_batch
# Init train_step function by user or using the default one
if self.train_step is None:
if train_step is not None:
self.train_step = train_step
else:
self.train_step = self._default_train_step
# Init valid_step function by user or using the default one
if self.valid_step is None:
if valid_step is not None:
self.valid_step = valid_step
else:
self.valid_step = self._default_valid_step
def _default_make_batch(self, batch):
"""Unpack batch to get input and target."""
input, target = batch
if self.use_cuda and not self.config.is_detection_trainer:
input, target = input.cuda(), target.cuda()
return (input, target)
def _default_train_step(self, batch):
input, target = batch
self.optimizer.zero_grad()
output = self.model(input)
loss = self.loss(output, target)
if self.use_amp:
with amp.scale_loss(loss, self.optimizer) as scaled_loss:
scaled_loss.backward()
self.optimizer.synchronize()
with self.optimizer.skip_synchronize():
self.optimizer.step()
else:
loss.backward()
if self.config.grad_clip:
torch.nn.utils.clip_grad_norm_(self.model.parameters(),
self.config.grad_clip)
self.optimizer.step()
return {'loss': loss.item(), 'train_batch_output': output}
def _default_valid_step(self, batch):
input, target = batch
if self.config.is_detection_trainer:
output = self.model(input, forward_train=False)
else:
output = self.model(input)
return {'valid_batch_output': output}
def _init_estimator_fn(self, model_fn, train_input_fn, valid_input_fn):
if self.model_fn is None:
if model_fn is not None:
self.model_fn = model_fn
else:
self.model_fn = self._default_model_fn
if self.train_input_fn is None:
if train_input_fn is not None:
self.train_input_fn = train_input_fn
else:
self.train_input_fn = self._default_train_input_fn
if self.valid_input_fn is None:
if valid_input_fn is not None:
self.valid_input_fn = valid_input_fn
else:
self.valid_input_fn = self._default_valid_input_fn
def _init_minimize_op(self, loss, global_step, var_list=None):
"""Init loss minimize operation, include loss scale method."""
loss_scale = self.config.loss_scale if self.use_amp else 1.
if loss_scale != 1:
scaled_grad_vars = self.optimizer.compute_gradients(
loss * loss_scale, var_list=var_list)
unscaled_grad_vars = [(grad / loss_scale, var)
for grad, var in scaled_grad_vars]
minimize_op = self.optimizer.apply_gradients(
unscaled_grad_vars, global_step)
else:
grad_vars = self.optimizer.compute_gradients(loss,
var_list=var_list)
minimize_op = self.optimizer.apply_gradients(
grad_vars, global_step)
return minimize_op
def _default_train_input_fn(self):
return self.train_loader.input_fn()
def _default_valid_input_fn(self):
return self.valid_loader.input_fn()
def _default_model_fn(self, features, labels, mode):
"""Define model_fn used by TensorFlow Estimator.
:params features: input features
:type features: tensorflow tensors
:params labels: label data
:type labels: tensorflow tensors
:params mode: mode of estimator
:type mode: tf.estimator.ModeKeys
:return: tensorflow EstimatorSpec
:rtype: tf.estimator.EstimatorSpec
"""
logging.info('model function action')
logits = self.model(features, mode == tf.estimator.ModeKeys.TRAIN)
logits = tf.cast(logits, tf.float32)
self.loss = Loss()()
loss = self.loss(logits=logits, labels=labels)
train_op = None
if mode == tf.estimator.ModeKeys.TRAIN:
global_step = tf.train.get_or_create_global_step()
epoch = tf.cast(global_step, tf.float32) / tf.cast(
len(self.train_loader), tf.float32)
self.lr_scheduler = LrScheduler()()
self.optimizer = Optimizer()(lr_scheduler=self.lr_scheduler,
epoch=epoch,
distributed=self.distributed)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
minimize_op = self._init_minimize_op(loss, global_step)
train_op = tf.group(minimize_op, update_ops)
eval_metric_ops = None
if mode == tf.estimator.ModeKeys.EVAL:
eval_metric_ops = self.valid_metrics(logits, labels)
return tf.estimator.EstimatorSpec(mode=mode,
loss=loss,
train_op=train_op,
eval_metric_ops=eval_metric_ops)
def _should_run_validation(self, epoch):
# Zero valid_interval means doesn't run _valid_loop of the trainer
# and user may provide _valid_loop in other callbacks
if self.valid_interval == 0:
return False
else:
return epoch % self.valid_interval == 0 or (epoch +
1) == self.epochs
def _init_callbacks(self, callbacks):
# Initialize custom callbacks by configuration or parameters
if callbacks is not None:
return callbacks
disables = []
if not self.config.model_statistics:
disables.append('ModelStatistics')
self.callbacks = CallbackList(self.config.callbacks, disables)
self.callbacks.set_trainer(self)
def _metric_average(self, val, name):
"""Do metric average.
:param val: input value
:param name: metric name
:return:
"""
tensor = torch.tensor(val)
avg_tensor = hvd.allreduce(tensor, name=name)
return avg_tensor.item()
@property
def _first_rank(self):
"""Check if the first rank."""
if self.distributed and hvd.rank() != 0:
return False
else:
return True
def _backup(self):
"""Backup result worker folder."""
if self.need_backup is True and self.backup_base_path is not None:
backup_worker_path = FileOps.join_path(self.backup_base_path,
self.get_worker_subpath())
FileOps.copy_folder(
self.get_local_worker_path(self.step_name, self.worker_id),
backup_worker_path)
def _save_visual_data(self, is_train=True, pfms=None, loss=None, lr=None):
# TODO Will move to metric base class later.
for _name, value in pfms.items():
if is_train:
_name = "{}_{}".format("t", _name)
else:
_name = "{}_{}".format("v", _name)
if isinstance(value, list):
for i, _item in enumerate(value):
_name = "{}_{}".format(_name, i)
self.visual_data[_name] = _item.data.item()
elif isinstance(value, dict):
for k, v in value.keys():
_name = "{}_{}".format(k, i)
self.visual_data[_name] = v
elif value is not None:
self.visual_data[_name] = value.data.item()
if loss is not None:
self.visual_data["loss"] = loss
if lr is not None:
self.visual_data["lr"] = lr
def _init_tf_estimator(self):
"""Init tensorflow estimator."""
if not vega.is_tf_backend():
return
sess_config = self._init_session_config()
if vega.is_gpu_device():
self._init_gpu_estimator(sess_config)
elif vega.is_npu_device():
self._init_npu_estimator(sess_config)
def _init_tf_session(self):
if not vega.is_tf_backend():
return
sess_config = self._init_session_config()
self.sess = tf.Session(config=sess_config)
def _init_session_config(self):
sess_config = self._init_gpu_session_config() if vega.is_gpu_device() else \
self._init_npu_session_config()
return sess_config
def _init_logging_hook(self):
logging_hook = []
if vega.is_gpu_device() and self.distributed:
logging_hook += [hvd.BroadcastGlobalVariablesHook(0)]
return logging_hook
def _init_gpu_estimator(self, sess_config):
"""Init tensorflow estimator."""
config = tf.estimator.RunConfig(
model_dir=self.get_local_worker_path(),
save_checkpoints_steps=self.config.save_steps,
log_step_count_steps=self.config.report_freq,
session_config=sess_config)
self.estimator = tf.estimator.Estimator(model_fn=self.model_fn,
config=config)
def _init_npu_estimator(self, sess_config):
model_dir = self.get_local_worker_path()
if self.distributed:
model_dir = FileOps.join_path(model_dir, str(self._local_rank_id))
config = NPURunConfig(model_dir=model_dir,
save_checkpoints_steps=self.config.save_steps,
log_step_count_steps=self.config.report_freq,
session_config=sess_config,
enable_data_pre_proc=True,
iterations_per_loop=1)
self.estimator = NPUEstimator(model_fn=self.model_fn, config=config)
def _init_gpu_session_config(self):
sess_config = tf.ConfigProto()
sess_config.gpu_options.allow_growth = True
if self.distributed:
sess_config.gpu_options.visible_device_list = str(hvd.local_rank())
return sess_config
def _init_npu_session_config(self):
sess_config = tf.ConfigProto()
sess_config.graph_options.rewrite_options.remapping = RewriterConfig.OFF
custom_op = sess_config.graph_options.rewrite_options.custom_optimizers.add(
)
custom_op.name = "NpuOptimizer"
if self.use_amp:
custom_op.parameter_map["precision_mode"].s = tf.compat.as_bytes(
"allow_mix_precision")
custom_op.parameter_map["use_off_line"].b = True
# custom_op.parameter_map['hcom_parallel'].b = True
# custom_op.parameter_map["enable_data_pre_proc"].b = True
# custom_op.parameter_map["mix_compile_mode"].b = True # mixed calculation
# custom_op.parameter_map["min_group_size"].b = 1
return sess_config
def _shutdown_distributed(self):
if vega.is_npu_device() and self.distributed:
self.sess.run(self.npu_shutdown)
self.sess.close()
