def bert_withlossscale_manager_train_feed():
class ModelBert(nn.Cell):
def __init__(self, network, optimizer=None):
super(ModelBert, self).__init__()
self.optimizer = optimizer
manager = DynamicLossScaleManager()
update_cell = LossScaleUpdateCell(manager)
self.train_network = BertTrainOneStepWithLossScaleCell(
network, self.optimizer, scale_update_cell=update_cell)
self.train_network.set_train()
def construct(self, arg0, arg1, arg2, arg3, arg4, arg5, arg6, arg7):
return self.train_network(arg0, arg1, arg2, arg3, arg4, arg5, arg6,
arg7)
version = os.getenv('VERSION', 'base')
batch_size = int(os.getenv('BATCH_SIZE', '1'))
scaling_sens = Tensor(np.ones([1]).astype(np.float32))
inputs = load_test_data(batch_size) + (scaling_sens, )
config = get_config(version=version, batch_size=batch_size)
netwithloss = BertNetworkWithLoss(config, True)
optimizer = AdamWeightDecayDynamicLR(netwithloss.trainable_params(), 10)
net = ModelBert(netwithloss, optimizer=optimizer)
net.set_train()
build_construct_graph(net, *inputs, execute=True)
def test_bert_train():
"""
the main function
"""
class ModelBert(nn.Cell):
"""
ModelBert definition
"""
def __init__(self, network, optimizer=None):
super(ModelBert, self).__init__()
self.optimizer = optimizer
self.train_network = BertTrainOneStepCell(network, self.optimizer)
self.train_network.set_train()
def construct(self, arg0, arg1, arg2, arg3, arg4, arg5, arg6):
return self.train_network(arg0, arg1, arg2, arg3, arg4, arg5, arg6)
version = os.getenv('VERSION', 'large')
batch_size = int(os.getenv('BATCH_SIZE', '1'))
inputs = load_test_data(batch_size)
config = get_config(version=version, batch_size=batch_size)
netwithloss = BertNetworkWithLoss(config, True)
optimizer = AdamWeightDecayDynamicLR(netwithloss.trainable_params(), 10)
net = ModelBert(netwithloss, optimizer=optimizer)
net.set_train()
build_construct_graph(net, *inputs, execute=False)
def test_AdamWeightDecayDynamicLR():
""" test_AdamWeightDecayDynamicLR """
inputs = Tensor(np.ones([1, 64]).astype(np.float32))
label = Tensor(np.zeros([1, 10]).astype(np.float32))
net = Net()
net.set_train()
loss = nn.SoftmaxCrossEntropyWithLogits()
optimizer = AdamWeightDecayDynamicLR(net.trainable_params(),
decay_steps=20,
learning_rate=0.1)
net_with_loss = WithLossCell(net, loss)
train_network = TrainOneStepCell(net_with_loss, optimizer)
_executor.compile(train_network, inputs, label)
def test_AdamWeightDecayDynamicLR():
""" test_AdamWeightDecayDynamicLR """
context.set_auto_parallel_context(parallel_mode="data_parallel",
device_num=2,
enable_parallel_optimizer=True)
inputs = Tensor(np.ones([32, 128]).astype(np.float32))
label = Tensor(np.zeros([32, 768]).astype(np.float32))
net = Net()
net.set_train()
loss = nn.SoftmaxCrossEntropyWithLogits()
optimizer = AdamWeightDecayDynamicLR(net.trainable_params(),
decay_steps=20,
learning_rate=0.1)
net_with_loss = WithLossCell(net, loss)
train_network = TrainOneStepCell(net_with_loss, optimizer)
_executor.compile(train_network, inputs, label)
def do_train(dataset=None, network=None, load_checkpoint_path="", save_checkpoint_path=""):
""" do train """
if load_checkpoint_path == "":
raise ValueError("Pretrain model missed, finetune task must load pretrain model!")
steps_per_epoch = dataset.get_dataset_size()
epoch_num = dataset.get_repeat_count()
# optimizer
if optimizer_cfg.optimizer == 'AdamWeightDecayDynamicLR':
optimizer = AdamWeightDecayDynamicLR(network.trainable_params(),
decay_steps=steps_per_epoch * epoch_num,
learning_rate=optimizer_cfg.AdamWeightDecayDynamicLR.learning_rate,
end_learning_rate=optimizer_cfg.AdamWeightDecayDynamicLR.end_learning_rate,
power=optimizer_cfg.AdamWeightDecayDynamicLR.power,
warmup_steps=int(steps_per_epoch * epoch_num * 0.1),
weight_decay=optimizer_cfg.AdamWeightDecayDynamicLR.weight_decay,
eps=optimizer_cfg.AdamWeightDecayDynamicLR.eps)
elif optimizer_cfg.optimizer == 'Lamb':
optimizer = Lamb(network.trainable_params(), decay_steps=steps_per_epoch * epoch_num,
start_learning_rate=optimizer_cfg.Lamb.start_learning_rate,
end_learning_rate=optimizer_cfg.Lamb.end_learning_rate,
power=optimizer_cfg.Lamb.power, weight_decay=optimizer_cfg.Lamb.weight_decay,
warmup_steps=int(steps_per_epoch * epoch_num * 0.1),
decay_filter=optimizer_cfg.Lamb.decay_filter)
elif optimizer_cfg.optimizer == 'Momentum':
optimizer = Momentum(network.trainable_params(), learning_rate=optimizer_cfg.Momentum.learning_rate,
momentum=optimizer_cfg.Momentum.momentum)
else:
raise Exception("Optimizer not supported. support: [AdamWeightDecayDynamicLR, Lamb, Momentum]")
# load checkpoint into network
ckpt_config = CheckpointConfig(save_checkpoint_steps=steps_per_epoch, keep_checkpoint_max=1)
ckpoint_cb = ModelCheckpoint(prefix="classifier", directory=save_checkpoint_path, config=ckpt_config)
param_dict = load_checkpoint(load_checkpoint_path)
load_param_into_net(network, param_dict)
update_cell = DynamicLossScaleUpdateCell(loss_scale_value=2**32, scale_factor=2, scale_window=1000)
netwithgrads = BertFinetuneCell(network, optimizer=optimizer, scale_update_cell=update_cell)
model = Model(netwithgrads)
callbacks = [TimeMonitor(dataset.get_dataset_size()), LossCallBack(), ckpoint_cb]
model.train(epoch_num, dataset, callbacks=callbacks)
def test_train():
'''
finetune function
'''
target = args_opt.device_target
if target == "Ascend":
devid = int(os.getenv('DEVICE_ID'))
context.set_context(mode=context.GRAPH_MODE,
device_target="Ascend",
device_id=devid)
elif target == "GPU":
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
else:
raise Exception("Target error, GPU or Ascend is supported.")
#BertCLSTrain for classification
#BertNERTrain for sequence labeling
if cfg.task == 'NER':
if cfg.use_crf:
netwithloss = BertNER(bert_net_cfg,
True,
num_labels=len(tag_to_index),
use_crf=True,
tag_to_index=tag_to_index,
dropout_prob=0.1)
else:
netwithloss = BertNER(bert_net_cfg,
True,
num_labels=cfg.num_labels,
dropout_prob=0.1)
elif cfg.task == 'SQUAD':
netwithloss = BertSquad(bert_net_cfg, True, 2, dropout_prob=0.1)
else:
netwithloss = BertCLS(bert_net_cfg,
True,
num_labels=cfg.num_labels,
dropout_prob=0.1)
if cfg.task == 'SQUAD':
dataset = get_squad_dataset(bert_net_cfg.batch_size, cfg.epoch_num)
else:
dataset = get_dataset(bert_net_cfg.batch_size, cfg.epoch_num)
# optimizer
steps_per_epoch = dataset.get_dataset_size()
if cfg.optimizer == 'AdamWeightDecayDynamicLR':
optimizer = AdamWeightDecayDynamicLR(
netwithloss.trainable_params(),
decay_steps=steps_per_epoch * cfg.epoch_num,
learning_rate=cfg.AdamWeightDecayDynamicLR.learning_rate,
end_learning_rate=cfg.AdamWeightDecayDynamicLR.end_learning_rate,
power=cfg.AdamWeightDecayDynamicLR.power,
warmup_steps=int(steps_per_epoch * cfg.epoch_num * 0.1),
weight_decay=cfg.AdamWeightDecayDynamicLR.weight_decay,
eps=cfg.AdamWeightDecayDynamicLR.eps)
elif cfg.optimizer == 'Lamb':
optimizer = Lamb(netwithloss.trainable_params(),
decay_steps=steps_per_epoch * cfg.epoch_num,
start_learning_rate=cfg.Lamb.start_learning_rate,
end_learning_rate=cfg.Lamb.end_learning_rate,
power=cfg.Lamb.power,
weight_decay=cfg.Lamb.weight_decay,
warmup_steps=int(steps_per_epoch * cfg.epoch_num *
0.1),
decay_filter=cfg.Lamb.decay_filter)
elif cfg.optimizer == 'Momentum':
optimizer = Momentum(netwithloss.trainable_params(),
learning_rate=cfg.Momentum.learning_rate,
momentum=cfg.Momentum.momentum)
else:
raise Exception("Optimizer not supported.")
# load checkpoint into network
ckpt_config = CheckpointConfig(save_checkpoint_steps=steps_per_epoch,
keep_checkpoint_max=1)
ckpoint_cb = ModelCheckpoint(prefix=cfg.ckpt_prefix,
directory=cfg.ckpt_dir,
config=ckpt_config)
param_dict = load_checkpoint(cfg.pre_training_ckpt)
load_param_into_net(netwithloss, param_dict)
update_cell = DynamicLossScaleUpdateCell(loss_scale_value=2**32,
scale_factor=2,
scale_window=1000)
if cfg.task == 'SQUAD':
netwithgrads = BertSquadCell(netwithloss,
optimizer=optimizer,
scale_update_cell=update_cell)
else:
netwithgrads = BertFinetuneCell(netwithloss,
optimizer=optimizer,
scale_update_cell=update_cell)
model = Model(netwithgrads)
model.train(cfg.epoch_num, dataset, callbacks=[LossCallBack(), ckpoint_cb])
def run_pretrain(args_opt):
"""pre-train bert"""
global device_id
global device_num
global rank_id
global job_id
args_opt.device_id = device_id
args_opt.device_num = device_num
sync_dataset(args_opt.data_url)
context.set_context(mode=context.GRAPH_MODE,
device_target=args_opt.device_target,
device_id=args_opt.device_id)
context.set_context(reserve_class_name_in_scope=False)
context.set_context(variable_memory_max_size="30GB")
ckpt_save_dir = args_opt.save_checkpoint_path
if args_opt.distribute == "true":
if args_opt.device_target == 'Ascend':
D.init('hccl')
device_num = args_opt.device_num
rank = args_opt.device_id % device_num
else:
D.init('nccl')
device_num = D.get_group_size()
rank = D.get_rank()
ckpt_save_dir = args_opt.save_checkpoint_path + 'ckpt_' + str(
rank) + '/'
context.reset_auto_parallel_context()
context.set_auto_parallel_context(
parallel_mode=ParallelMode.DATA_PARALLEL,
mirror_mean=True,
device_num=device_num)
from mindspore.parallel._auto_parallel_context import auto_parallel_context
if bert_net_cfg.num_hidden_layers == 12:
if bert_net_cfg.use_relative_positions:
auto_parallel_context().set_all_reduce_fusion_split_indices(
[29, 58, 87, 116, 145, 174, 203, 217])
else:
auto_parallel_context().set_all_reduce_fusion_split_indices(
[28, 55, 82, 109, 136, 163, 190, 205])
elif bert_net_cfg.num_hidden_layers == 24:
if bert_net_cfg.use_relative_positions:
auto_parallel_context().set_all_reduce_fusion_split_indices(
[30, 90, 150, 210, 270, 330, 390, 421])
else:
auto_parallel_context().set_all_reduce_fusion_split_indices(
[38, 93, 148, 203, 258, 313, 368, 397])
else:
rank = 0
device_num = 1
if args_opt.device_target == 'GPU' and bert_net_cfg.compute_type != mstype.float32:
logger.warning('Gpu only support fp32 temporarily, run with fp32.')
bert_net_cfg.compute_type = mstype.float32
ds, new_repeat_count = create_bert_dataset(args_opt.epoch_size, device_num,
rank, args_opt.do_shuffle,
args_opt.enable_data_sink,
args_opt.data_sink_steps,
args_opt.data_dir,
args_opt.schema_dir)
if args_opt.train_steps > 0:
new_repeat_count = min(
new_repeat_count, args_opt.train_steps // args_opt.data_sink_steps)
netwithloss = BertNetworkWithLoss(bert_net_cfg, True)
if cfg.optimizer == 'Lamb':
optimizer = Lamb(netwithloss.trainable_params(),
decay_steps=ds.get_dataset_size() * new_repeat_count,
start_learning_rate=cfg.Lamb.start_learning_rate,
end_learning_rate=cfg.Lamb.end_learning_rate,
power=cfg.Lamb.power,
warmup_steps=cfg.Lamb.warmup_steps,
weight_decay=cfg.Lamb.weight_decay,
eps=cfg.Lamb.eps)
elif cfg.optimizer == 'Momentum':
optimizer = Momentum(netwithloss.trainable_params(),
learning_rate=cfg.Momentum.learning_rate,
momentum=cfg.Momentum.momentum)
elif cfg.optimizer == 'AdamWeightDecayDynamicLR':
optimizer = AdamWeightDecayDynamicLR(
netwithloss.trainable_params(),
decay_steps=ds.get_dataset_size() * new_repeat_count,
learning_rate=cfg.AdamWeightDecayDynamicLR.learning_rate,
end_learning_rate=cfg.AdamWeightDecayDynamicLR.end_learning_rate,
power=cfg.AdamWeightDecayDynamicLR.power,
weight_decay=cfg.AdamWeightDecayDynamicLR.weight_decay,
eps=cfg.AdamWeightDecayDynamicLR.eps,
warmup_steps=cfg.AdamWeightDecayDynamicLR.warmup_steps)
else:
raise ValueError(
"Don't support optimizer {}, only support [Lamb, Momentum, AdamWeightDecayDynamicLR]"
.format(cfg.optimizer))
callback = [TimeMonitor(ds.get_dataset_size()), LossCallBack()]
print("Enable save checkpoint: ", args_opt.enable_save_ckpt)
print("Rank ID: ", rank_id)
if args_opt.enable_save_ckpt == "true" and rank_id % device_num == 0:
print("Enable save checkpoint")
config_ck = CheckpointConfig(
save_checkpoint_steps=args_opt.save_checkpoint_steps,
keep_checkpoint_max=args_opt.save_checkpoint_num)
ckpoint_cb = ModelCheckpoint(prefix='checkpoint_bert',
directory=ckpt_save_dir,
config=config_ck)
callback.append(ckpoint_cb)
if args_opt.load_checkpoint_path:
param_dict = load_checkpoint(args_opt.load_checkpoint_path)
load_param_into_net(netwithloss, param_dict)
if args_opt.enable_lossscale == "true":
update_cell = DynamicLossScaleUpdateCell(
loss_scale_value=cfg.loss_scale_value,
scale_factor=cfg.scale_factor,
scale_window=cfg.scale_window)
netwithgrads = BertTrainOneStepWithLossScaleCell(
netwithloss, optimizer=optimizer, scale_update_cell=update_cell)
else:
netwithgrads = BertTrainOneStepCell(netwithloss, optimizer=optimizer)
model = Model(netwithgrads)
model.train(new_repeat_count,
ds,
callbacks=callback,
dataset_sink_mode=(args_opt.enable_data_sink == "true"))
def run_pretrain():
"""pre-train bert_clue"""
parser = argparse.ArgumentParser(description='bert pre_training')
parser.add_argument("--distribute",
type=str,
default="false",
help="Run distribute, default is false.")
parser.add_argument("--epoch_size",
type=int,
default="1",
help="Epoch size, default is 1.")
parser.add_argument("--device_id",
type=int,
default=0,
help="Device id, default is 0.")
parser.add_argument("--device_num",
type=int,
default=1,
help="Use device nums, default is 1.")
parser.add_argument("--enable_task_sink",
type=str,
default="true",
help="Enable task sink, default is true.")
parser.add_argument("--enable_loop_sink",
type=str,
default="true",
help="Enable loop sink, default is true.")
parser.add_argument("--enable_mem_reuse",
type=str,
default="true",
help="Enable mem reuse, default is true.")
parser.add_argument("--enable_save_ckpt",
type=str,
default="true",
help="Enable save checkpoint, default is true.")
parser.add_argument("--enable_lossscale",
type=str,
default="true",
help="Use lossscale or not, default is not.")
parser.add_argument("--do_shuffle",
type=str,
default="true",
help="Enable shuffle for dataset, default is true.")
parser.add_argument("--enable_data_sink",
type=str,
default="true",
help="Enable data sink, default is true.")
parser.add_argument("--data_sink_steps",
type=int,
default="1",
help="Sink steps for each epoch, default is 1.")
parser.add_argument("--checkpoint_path",
type=str,
default="",
help="Checkpoint file path")
parser.add_argument("--save_checkpoint_steps",
type=int,
default=1000,
help="Save checkpoint steps, "
"default is 1000.")
parser.add_argument("--save_checkpoint_num",
type=int,
default=1,
help="Save checkpoint numbers, default is 1.")
parser.add_argument("--data_dir",
type=str,
default="",
help="Data path, it is better to use absolute path")
parser.add_argument("--schema_dir",
type=str,
default="",
help="Schema path, it is better to use absolute path")
args_opt = parser.parse_args()
context.set_context(mode=context.GRAPH_MODE,
device_target="Ascend",
device_id=args_opt.device_id)
context.set_context(enable_task_sink=(args_opt.enable_task_sink == "true"),
enable_loop_sink=(args_opt.enable_loop_sink == "true"),
enable_mem_reuse=(args_opt.enable_mem_reuse == "true"))
context.set_context(reserve_class_name_in_scope=False)
if args_opt.distribute == "true":
device_num = args_opt.device_num
context.reset_auto_parallel_context()
context.set_auto_parallel_context(
parallel_mode=ParallelMode.DATA_PARALLEL,
mirror_mean=True,
device_num=device_num)
D.init()
rank = args_opt.device_id % device_num
else:
rank = 0
device_num = 1
ds = create_bert_dataset(args_opt.epoch_size, device_num, rank,
args_opt.do_shuffle, args_opt.enable_data_sink,
args_opt.data_sink_steps, args_opt.data_dir,
args_opt.schema_dir)
netwithloss = BertNetworkWithLoss(bert_net_cfg, True)
if cfg.optimizer == 'Lamb':
optimizer = Lamb(netwithloss.trainable_params(),
decay_steps=ds.get_dataset_size() *
ds.get_repeat_count(),
start_learning_rate=cfg.Lamb.start_learning_rate,
end_learning_rate=cfg.Lamb.end_learning_rate,
power=cfg.Lamb.power,
warmup_steps=cfg.Lamb.warmup_steps,
weight_decay=cfg.Lamb.weight_decay,
eps=cfg.Lamb.eps)
elif cfg.optimizer == 'Momentum':
optimizer = Momentum(netwithloss.trainable_params(),
learning_rate=cfg.Momentum.learning_rate,
momentum=cfg.Momentum.momentum)
elif cfg.optimizer == 'AdamWeightDecayDynamicLR':
optimizer = AdamWeightDecayDynamicLR(
netwithloss.trainable_params(),
decay_steps=ds.get_dataset_size() * ds.get_repeat_count(),
learning_rate=cfg.AdamWeightDecayDynamicLR.learning_rate,
end_learning_rate=cfg.AdamWeightDecayDynamicLR.end_learning_rate,
power=cfg.AdamWeightDecayDynamicLR.power,
weight_decay=cfg.AdamWeightDecayDynamicLR.weight_decay,
eps=cfg.AdamWeightDecayDynamicLR.eps)
else:
raise ValueError(
"Don't support optimizer {}, only support [Lamb, Momentum, AdamWeightDecayDynamicLR]"
.format(cfg.optimizer))
callback = [LossCallBack()]
if args_opt.enable_save_ckpt == "true":
config_ck = CheckpointConfig(
save_checkpoint_steps=args_opt.save_checkpoint_steps,
keep_checkpoint_max=args_opt.save_checkpoint_num)
ckpoint_cb = ModelCheckpoint(prefix='checkpoint_bert',
config=config_ck)
callback.append(ckpoint_cb)
if args_opt.checkpoint_path:
param_dict = load_checkpoint(args_opt.checkpoint_path)
load_param_into_net(netwithloss, param_dict)
if args_opt.enable_lossscale == "true":
update_cell = DynamicLossScaleUpdateCell(
loss_scale_value=cfg.loss_scale_value,
scale_factor=cfg.scale_factor,
scale_window=cfg.scale_window)
netwithgrads = BertTrainOneStepWithLossScaleCell(
netwithloss, optimizer=optimizer, scale_update_cell=update_cell)
else:
netwithgrads = BertTrainOneStepCell(netwithloss, optimizer=optimizer)
model = Model(netwithgrads)
model.train(ds.get_repeat_count(),
ds,
callbacks=callback,
dataset_sink_mode=(args_opt.enable_data_sink == "true"))
def run_pretrain():
"""pre-train bert_clue"""
parser = argparse.ArgumentParser(description='bert pre_training')
parser.add_argument(
'--device_target',
type=str,
default='Ascend',
choices=['Ascend', 'GPU'],
help='device where the code will be implemented. (Default: Ascend)')
parser.add_argument("--distribute",
type=str,
default="false",
help="Run distribute, default is false.")
parser.add_argument("--epoch_size",
type=int,
default="1",
help="Epoch size, default is 1.")
parser.add_argument("--device_id",
type=int,
default=0,
help="Device id, default is 0.")
parser.add_argument("--device_num",
type=int,
default=1,
help="Use device nums, default is 1.")
parser.add_argument("--enable_save_ckpt",
type=str,
default="true",
help="Enable save checkpoint, default is true.")
parser.add_argument("--enable_lossscale",
type=str,
default="true",
help="Use lossscale or not, default is not.")
parser.add_argument("--do_shuffle",
type=str,
default="true",
help="Enable shuffle for dataset, default is true.")
parser.add_argument("--enable_data_sink",
type=str,
default="true",
help="Enable data sink, default is true.")
parser.add_argument("--data_sink_steps",
type=int,
default="1",
help="Sink steps for each epoch, default is 1.")
parser.add_argument("--save_checkpoint_path",
type=str,
default="",
help="Save checkpoint path")
parser.add_argument("--load_checkpoint_path",
type=str,
default="",
help="Load checkpoint file path")
parser.add_argument("--save_checkpoint_steps",
type=int,
default=1000,
help="Save checkpoint steps, "
"default is 1000.")
parser.add_argument("--train_steps",
type=int,
default=-1,
help="Training Steps, default is -1, "
"meaning run all steps according to epoch number.")
parser.add_argument("--save_checkpoint_num",
type=int,
default=1,
help="Save checkpoint numbers, default is 1.")
parser.add_argument("--data_dir",
type=str,
default="",
help="Data path, it is better to use absolute path")
parser.add_argument("--schema_dir",
type=str,
default="",
help="Schema path, it is better to use absolute path")
args_opt = parser.parse_args()
context.set_context(mode=context.GRAPH_MODE,
device_target=args_opt.device_target,
device_id=args_opt.device_id)
context.set_context(reserve_class_name_in_scope=False)
context.set_context(variable_memory_max_size="30GB")
ckpt_save_dir = args_opt.save_checkpoint_path
if args_opt.distribute == "true":
if args_opt.device_target == 'Ascend':
D.init('hccl')
device_num = args_opt.device_num
rank = args_opt.device_id % device_num
else:
D.init('nccl')
device_num = D.get_group_size()
rank = D.get_rank()
ckpt_save_dir = args_opt.save_checkpoint_path + 'ckpt_' + str(
rank) + '/'
context.reset_auto_parallel_context()
context.set_auto_parallel_context(
parallel_mode=ParallelMode.DATA_PARALLEL,
mirror_mean=True,
device_num=device_num)
from mindspore.parallel._auto_parallel_context import auto_parallel_context
if bert_net_cfg.num_hidden_layers == 12:
if bert_net_cfg.use_relative_positions:
auto_parallel_context().set_all_reduce_fusion_split_indices(
[29, 58, 87, 116, 145, 174, 203, 217])
else:
auto_parallel_context().set_all_reduce_fusion_split_indices(
[28, 55, 82, 109, 136, 163, 190, 205])
elif bert_net_cfg.num_hidden_layers == 24:
if bert_net_cfg.use_relative_positions:
auto_parallel_context().set_all_reduce_fusion_split_indices(
[30, 90, 150, 210, 270, 330, 390, 421])
else:
auto_parallel_context().set_all_reduce_fusion_split_indices(
[38, 93, 148, 203, 258, 313, 368, 397])
else:
rank = 0
device_num = 1
if args_opt.device_target == 'GPU' and bert_net_cfg.compute_type != mstype.float32:
logger.warning('Gpu only support fp32 temporarily, run with fp32.')
bert_net_cfg.compute_type = mstype.float32
ds, new_repeat_count = create_bert_dataset(args_opt.epoch_size, device_num,
rank, args_opt.do_shuffle,
args_opt.enable_data_sink,
args_opt.data_sink_steps,
args_opt.data_dir,
args_opt.schema_dir)
data_epoch_size = new_repeat_count // args_opt.epoch_size # Epoch nums in one dataset.
if args_opt.train_steps > 0:
new_repeat_count = min(
new_repeat_count, args_opt.train_steps // args_opt.data_sink_steps)
netwithloss = BertNetworkWithLoss(bert_net_cfg, True)
if cfg.optimizer == 'Lamb':
optimizer = Lamb(netwithloss.trainable_params(),
decay_steps=ds.get_dataset_size() * new_repeat_count,
start_learning_rate=cfg.Lamb.start_learning_rate,
end_learning_rate=cfg.Lamb.end_learning_rate,
power=cfg.Lamb.power,
warmup_steps=cfg.Lamb.warmup_steps,
weight_decay=cfg.Lamb.weight_decay,
eps=cfg.Lamb.eps)
elif cfg.optimizer == 'Momentum':
optimizer = Momentum(netwithloss.trainable_params(),
learning_rate=cfg.Momentum.learning_rate,
momentum=cfg.Momentum.momentum)
elif cfg.optimizer == 'AdamWeightDecayDynamicLR':
optimizer = AdamWeightDecayDynamicLR(
netwithloss.trainable_params(),
decay_steps=ds.get_dataset_size() * new_repeat_count,
learning_rate=cfg.AdamWeightDecayDynamicLR.learning_rate,
end_learning_rate=cfg.AdamWeightDecayDynamicLR.end_learning_rate,
power=cfg.AdamWeightDecayDynamicLR.power,
weight_decay=cfg.AdamWeightDecayDynamicLR.weight_decay,
eps=cfg.AdamWeightDecayDynamicLR.eps,
warmup_steps=cfg.AdamWeightDecayDynamicLR.warmup_steps)
else:
raise ValueError(
"Don't support optimizer {}, only support [Lamb, Momentum, AdamWeightDecayDynamicLR]"
.format(cfg.optimizer))
callback = [
TimeMonitor(ds.get_dataset_size()),
LossCallBack(data_epoch_size)
]
if args_opt.enable_save_ckpt == "true":
config_ck = CheckpointConfig(
save_checkpoint_steps=args_opt.save_checkpoint_steps,
keep_checkpoint_max=args_opt.save_checkpoint_num)
ckpoint_cb = ModelCheckpoint(prefix='checkpoint_bert',
directory=ckpt_save_dir,
config=config_ck)
callback.append(ckpoint_cb)
if args_opt.load_checkpoint_path:
param_dict = load_checkpoint(args_opt.load_checkpoint_path)
load_param_into_net(netwithloss, param_dict)
if args_opt.enable_lossscale == "true":
update_cell = DynamicLossScaleUpdateCell(
loss_scale_value=cfg.loss_scale_value,
scale_factor=cfg.scale_factor,
scale_window=cfg.scale_window)
netwithgrads = BertTrainOneStepWithLossScaleCell(
netwithloss, optimizer=optimizer, scale_update_cell=update_cell)
else:
netwithgrads = BertTrainOneStepCell(netwithloss, optimizer=optimizer)
model = Model(netwithgrads)
model.train(new_repeat_count,
ds,
callbacks=callback,
dataset_sink_mode=(args_opt.enable_data_sink == "true"))
def test_AdamWeightDecayDynamicLR_init(self):
with pytest.raises(TypeError):
AdamWeightDecayDynamicLR(0.5, 10)
def test_AdamWeightDecayDynamicLR_init(self):
with pytest.raises(ValueError):
AdamWeightDecayDynamicLR(None, 10)
def __init__(self, network):
super(TrainStepWrapForAdamDynamicLr, self).__init__()
self.network = network
self.weights = ParameterTuple(network.get_parameters())
self.optimizer = AdamWeightDecayDynamicLR(self.weights, 10)
self.sens = Tensor(np.ones(shape=(1, 10)).astype(np.float32))