def test_bert_tdt():
"""test bert tdt"""
context.set_context(mode=context.GRAPH_MODE,
device_target="Ascend",
reserve_class_name_in_scope=False)
context.set_context(enable_task_sink=True)
context.set_context(enable_loop_sink=True)
context.set_context(enable_mem_reuse=True)
parallel_callback = ModelCallback()
ds = me_de_train_dataset()
version = os.getenv('VERSION', 'large')
batch_size = int(os.getenv('BATCH_SIZE', '16'))
config = get_config(version=version, batch_size=batch_size)
netwithloss = BertNetworkWithLoss(config, True)
optimizer = Momentum(netwithloss.trainable_params(),
learning_rate=2e-5,
momentum=0.9)
netwithgrads = BertTrainOneStepCell(netwithloss, optimizer=optimizer)
netwithgrads.set_train(True)
model = Model(netwithgrads)
params = netwithloss.trainable_params()
for param in params:
value = param.default_input
name = param.name
if isinstance(value, Tensor):
if name.split('.')[-1] in ['weight']:
if name.split('.')[-3] in ['cls2']:
logger.info(
"***************** BERT param name is 1 {}".format(
name))
param.default_input = weight_variable(
value.asnumpy().shape)
else:
logger.info(
"***************** BERT param name is 2 {}".format(
name))
tempshape = value.asnumpy().shape
shape = (tempshape[1], tempshape[0])
weight_value = weight_variable(shape).asnumpy()
param.default_input = Tensor(
np.transpose(weight_value, [1, 0]))
else:
logger.info(
"***************** BERT param name is 3 {}".format(name))
param.default_input = weight_variable(value.asnumpy().shape)
model.train(ds.get_repeat_count(),
ds,
callbacks=parallel_callback,
dataset_sink_mode=False)
loss_value = np.array(parallel_callback.loss_list)
expect_out = [
12.19179, 11.965041, 11.969687, 11.97815, 11.969171, 12.603289,
12.165594, 12.824818, 12.38842, 12.604046
]
logger.info("expected loss value output: {}".format(expect_out))
assert allclose(loss_value, expect_out, 0.00001, 0.00001)
def test_bert_tdt():
"""test bert tdt"""
context.set_context(mode=context.GRAPH_MODE)
context.set_context(device_target="Ascend")
context.set_context(enable_task_sink=True)
context.set_context(enable_loop_sink=True)
context.set_context(enable_mem_reuse=True)
parallel_callback = ModelCallback()
ds = me_de_train_dataset(cfg.bert_config.batch_size)
config = cfg.bert_config
netwithloss = BertNetworkWithLoss(config, True)
optimizer = Lamb(netwithloss.trainable_params(),
decay_steps=cfg.decay_steps,
start_learning_rate=cfg.start_learning_rate,
end_learning_rate=cfg.end_learning_rate,
power=cfg.power,
warmup_steps=cfg.num_warmup_steps,
decay_filter=lambda x: False)
netwithgrads = BertTrainOneStepCell(netwithloss, optimizer=optimizer)
netwithgrads.set_train(True)
model = Model(netwithgrads)
config_ck = CheckpointConfig(
save_checkpoint_steps=cfg.save_checkpoint_steps,
keep_checkpoint_max=cfg.keep_checkpoint_max)
ckpoint_cb = ModelCheckpoint(prefix=cfg.checkpoint_prefix,
config=config_ck)
model.train(ds.get_repeat_count(),
ds,
callbacks=[parallel_callback, ckpoint_cb],
dataset_sink_mode=False)
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_bert_tdt():
"""test bert tdt"""
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", reserve_class_name_in_scope=False)
context.set_context(enable_task_sink=True)
context.set_context(enable_loop_sink=True)
context.set_context(enable_mem_reuse=True)
parallel_callback = ModelCallback()
ds = me_de_train_dataset()
version = os.getenv('VERSION', 'large')
batch_size = int(os.getenv('BATCH_SIZE', '16'))
config = get_config(version=version, batch_size=batch_size)
netwithloss = BertNetworkWithLoss(config, True)
optimizer = Lamb(netwithloss.trainable_params(), decay_steps=10000, start_learning_rate=1e-4,
end_learning_rate=0.0, power=10.0, warmup_steps=0, decay_filter=lambda x: False)
netwithgrads = BertTrainOneStepCell(netwithloss, optimizer=optimizer)
netwithgrads.set_train(True)
model = Model(netwithgrads)
params = netwithloss.trainable_params()
for param in params:
value = param.default_input
name = param.name
if isinstance(value, Tensor):
if name.split('.')[-1] in ['weight']:
if name.split('.')[-3] in ['cls2']:
logger.info("***************** BERT param name is 1 {}".format(name))
param.default_input = weight_variable(value.asnumpy().shape)
else:
logger.info("***************** BERT param name is 2 {}".format(name))
tempshape = value.asnumpy().shape
shape = (tempshape[1], tempshape[0])
weight_value = weight_variable(shape).asnumpy()
param.default_input = Tensor(np.transpose(weight_value, [1, 0]))
else:
logger.info("***************** BERT param name is 3 {}".format(name))
param.default_input = weight_variable(value.asnumpy().shape)
model.train(ds.get_repeat_count(), ds, callbacks=parallel_callback, dataset_sink_mode=False)
loss_value = np.array(parallel_callback.loss_list)
expect_out = [12.191790, 11.739655, 11.523477, 11.320723, 11.113152, 11.203759, 10.841681, 10.826849,
10.616718, 10.486609]
logger.info("expected loss value output: {}".format(expect_out))
assert allclose(loss_value, expect_out, 0.001, 0.001)
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 test_bert_tdt():
"""test bert tdt"""
context.set_context(mode=context.GRAPH_MODE,
device_target="Ascend",
reserve_class_name_in_scope=False)
context.set_context(enable_loop_sink=True)
context.set_context(enable_mem_reuse=True)
ds = me_de_train_dataset()
version = os.getenv('VERSION', 'large')
batch_size = int(os.getenv('BATCH_SIZE', '16'))
config = get_config(version=version, batch_size=batch_size)
netwithloss = BertNetworkWithLoss(config, True)
optimizer = Momentum(netwithloss.trainable_params(),
learning_rate=2e-5,
momentum=0.9)
scale_window = 3
scale_manager = DynamicLossScaleManager(2**16, 2, scale_window)
netwithgrads = BertTrainOneStepWithLossScaleCell(
netwithloss,
optimizer=optimizer,
scale_update_cell=scale_manager.get_update_cell())
netwithgrads.set_train(True)
model = Model(netwithgrads)
callback = ModelCallback()
params = netwithloss.trainable_params()
for param in params:
param.init_data()
value = param.default_input
name = param.name
if isinstance(value, Tensor):
if name.split('.')[-1] in ['weight']:
if name.split('.')[-3] in ['cls2']:
logger.info(
"***************** BERT param name is 1 {}".format(
name))
param.default_input = weight_variable(
value.asnumpy().shape)
else:
logger.info(
"***************** BERT param name is 2 {}".format(
name))
tempshape = value.asnumpy().shape
shape = (tempshape[1], tempshape[0])
weight_value = weight_variable(shape).asnumpy()
param.default_input = Tensor(
np.transpose(weight_value, [1, 0]))
else:
logger.info(
"***************** BERT param name is 3 {}".format(name))
param.default_input = weight_variable(value.asnumpy().shape)
model.train(ds.get_repeat_count(),
ds,
callbacks=callback,
dataset_sink_mode=False)
# assertion occurs while the loss value, overflow state or loss_scale value is wrong
loss_value = np.array(callback.loss_list)
expect_loss_value = [
12.1918125, 11.966035, 11.972114, 11.982188, 11.974092, 12.610916,
12.17565, 12.840416, 12.40291, 12.621661
]
print("loss value: {}".format(loss_value))
assert np.allclose(loss_value, expect_loss_value, 0.00001, 0.00001)
overflow = np.array(callback.overflow_list)
expect_overflow = [
True, True, False, False, False, True, False, False, False, True
]
print("overflow: {}".format(overflow))
assert (overflow == expect_overflow).all()
loss_scale = np.array(callback.lossscale_list)
expect_loss_scale = [
32768.0, 16384.0, 16384.0, 16384.0, 32768.0, 16384.0, 16384.0, 16384.0,
32768.0, 16384.0
]
print("loss scale: {}".format(loss_scale))
assert np.allclose(loss_scale, expect_loss_scale, 0.00001, 0.00001)
def test_bert_tdt():
"""test bert tdt"""
context.set_context(mode=context.GRAPH_MODE,
device_target="Ascend",
reserve_class_name_in_scope=False)
context.set_context(enable_task_sink=True)
context.set_context(enable_loop_sink=True)
context.set_context(enable_mem_reuse=True)
ds = me_de_train_dataset()
version = os.getenv('VERSION', 'large')
batch_size = int(os.getenv('BATCH_SIZE', '16'))
config = get_config(version=version, batch_size=batch_size)
netwithloss = BertNetworkWithLoss(config, True)
optimizer = Momentum(netwithloss.trainable_params(),
learning_rate=2e-5,
momentum=0.9)
scale_window = 3
scale_manager = DynamicLossScaleManager(2**32, 2, scale_window)
netwithgrads = BertTrainOneStepWithLossScaleCell(
netwithloss,
optimizer=optimizer,
scale_update_cell=scale_manager.get_update_cell())
netwithgrads.set_train(True)
model = Model(netwithgrads)
callback = ModelCallback()
params = netwithloss.trainable_params()
for param in params:
value = param.default_input
name = param.name
if isinstance(value, Tensor):
if name.split('.')[-1] in ['weight']:
if name.split('.')[-3] in ['cls2']:
logger.info(
"***************** BERT param name is 1 {}".format(
name))
param.default_input = weight_variable(
value.asnumpy().shape)
else:
logger.info(
"***************** BERT param name is 2 {}".format(
name))
tempshape = value.asnumpy().shape
shape = (tempshape[1], tempshape[0])
weight_value = weight_variable(shape).asnumpy()
param.default_input = Tensor(
np.transpose(weight_value, [1, 0]))
else:
logger.info(
"***************** BERT param name is 3 {}".format(name))
param.default_input = weight_variable(value.asnumpy().shape)
model.train(ds.get_repeat_count(),
ds,
callbacks=callback,
dataset_sink_mode=False)
# assertion occurs while the loss_scale value is wrong
count = 0
for i in range(len(callback.overflow_list)):
if callback.overflow_list[i] == Tensor(True, mstype.bool_) and i > 0:
count = 0
assert callback.lossscale_list[i] == callback.lossscale_list[
i - 1] * Tensor(0.5, mstype.float32)
if callback.overflow_list[i] == Tensor(False, mstype.bool_):
count = count + 1
if count == scale_window:
count = 0
assert callback.lossscale_list[i] == callback.lossscale_list[
i - 1] * Tensor(2.0, mstype.float32)