def _get_optimizer(args_opt, network):
"""get thor optimizer."""
if cfg.optimizer == "Thor":
if args_opt.distribute == "true":
from src.thor_for_bert_arg import THOR
else:
from src.thor_for_bert import THOR
lr = get_bert_lr()
damping = get_bert_damping()
optimizer = THOR(filter(lambda x: x.requires_grad, network.get_parameters()), lr, cfg.Thor.momentum,
filter(lambda x: 'matrix_A' in x.name, network.get_parameters()),
filter(lambda x: 'matrix_G' in x.name, network.get_parameters()),
cfg.Thor.weight_decay, cfg.Thor.loss_scale, bert_net_cfg.num_hidden_layers,
bert_net_cfg.batch_size, damping)
else:
raise ValueError("Don't support optimizer {}, only support [Thor]".format(cfg.optimizer))
return optimizer
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=4,
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="false",
help="Use lossscale or not, default is not.")
parser.add_argument("--do_shuffle",
type=str,
default="false",
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="100",
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,
save_graphs=False)
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()
device_num = args_opt.device_num
rank = args_opt.device_id % device_num
else:
D.init()
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],
"hccl_world_groupsum1")
auto_parallel_context().set_all_reduce_fusion_split_indices(
[29, 58, 87, 116, 145, 174, 203, 217],
"hccl_world_groupsum3")
else:
auto_parallel_context().set_all_reduce_fusion_split_indices(
[28, 55, 82, 109, 136, 163, 190, 205],
"hccl_world_groupsum1")
auto_parallel_context().set_all_reduce_fusion_split_indices(
[28, 55, 82, 109, 136, 163, 190, 205],
"hccl_world_groupsum3")
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],
"hccl_world_groupsum1")
auto_parallel_context().set_all_reduce_fusion_split_indices(
[30, 90, 150, 210, 270, 330, 390, 421],
"hccl_world_groupsum3")
else:
auto_parallel_context().set_all_reduce_fusion_split_indices(
[38, 93, 148, 203, 258, 313, 368, 397],
"hccl_world_groupsum1")
auto_parallel_context().set_all_reduce_fusion_split_indices(
[38, 93, 148, 203, 258, 313, 368, 397],
"hccl_world_groupsum3")
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 = create_bert_dataset(device_num, rank, args_opt.do_shuffle,
args_opt.data_dir, args_opt.schema_dir)
net_with_loss = BertNetworkWithLoss(bert_net_cfg, True)
new_repeat_count = args_opt.epoch_size * ds.get_dataset_size(
) // args_opt.data_sink_steps
if args_opt.train_steps > 0:
new_repeat_count = min(
new_repeat_count, args_opt.train_steps // args_opt.data_sink_steps)
else:
args_opt.train_steps = args_opt.epoch_size * ds.get_dataset_size()
logger.info("train steps: {}".format(args_opt.train_steps))
if cfg.optimizer == 'Lamb':
lr_schedule = BertLearningRate(
learning_rate=cfg.Lamb.learning_rate,
end_learning_rate=cfg.Lamb.end_learning_rate,
warmup_steps=cfg.Lamb.warmup_steps,
decay_steps=args_opt.train_steps,
power=cfg.Lamb.power)
params = net_with_loss.trainable_params()
decay_params = list(filter(cfg.Lamb.decay_filter, params))
other_params = list(
filter(lambda x: not cfg.Lamb.decay_filter(x), params))
group_params = [{
'params': decay_params,
'weight_decay': cfg.Lamb.weight_decay
}, {
'params': other_params
}, {
'order_params': params
}]
optimizer = Lamb(group_params,
learning_rate=lr_schedule,
eps=cfg.Lamb.eps)
elif cfg.optimizer == 'Momentum':
optimizer = Momentum(net_with_loss.trainable_params(),
learning_rate=cfg.Momentum.learning_rate,
momentum=cfg.Momentum.momentum)
elif cfg.optimizer == 'AdamWeightDecay':
lr_schedule = BertLearningRate(
learning_rate=cfg.AdamWeightDecay.learning_rate,
end_learning_rate=cfg.AdamWeightDecay.end_learning_rate,
warmup_steps=cfg.AdamWeightDecay.warmup_steps,
decay_steps=args_opt.train_steps,
power=cfg.AdamWeightDecay.power)
params = net_with_loss.trainable_params()
decay_params = list(filter(cfg.AdamWeightDecay.decay_filter, params))
other_params = list(
filter(lambda x: not cfg.AdamWeightDecay.decay_filter(x), params))
group_params = [{
'params': decay_params,
'weight_decay': cfg.AdamWeightDecay.weight_decay
}, {
'params': other_params,
'weight_decay': 0.0
}, {
'order_params': params
}]
optimizer = AdamWeightDecay(group_params,
learning_rate=lr_schedule,
eps=cfg.AdamWeightDecay.eps)
elif cfg.optimizer == "Thor":
lr = get_bert_lr()
damping = get_bert_damping()
optimizer = THOR(
filter(lambda x: x.requires_grad,
net_with_loss.get_parameters()), lr, cfg.Thor.momentum,
filter(lambda x: 'matrix_A' in x.name,
net_with_loss.get_parameters()),
filter(lambda x: 'matrix_G' in x.name,
net_with_loss.get_parameters()), cfg.Thor.weight_decay,
cfg.Thor.loss_scale, bert_net_cfg.num_hidden_layers,
bert_net_cfg.batch_size, damping)
else:
raise ValueError(
"Don't support optimizer {}, only support [Lamb, Momentum, AdamWeightDecay, Thor]"
.format(cfg.optimizer))
callback = [TimeMonitor(args_opt.data_sink_steps), LossCallBack()]
if args_opt.enable_save_ckpt == "true" and rank == 0:
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(net_with_loss, 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)
net_with_grads = BertTrainOneStepWithLossScaleCell(
net_with_loss, optimizer=optimizer, scale_update_cell=update_cell)
else:
net_with_grads = BertTrainOneStepCell(net_with_loss,
optimizer=optimizer)
model = Model(net_with_grads, frequency=cfg.Thor.frequency)
model.train(new_repeat_count,
ds,
callbacks=callback,
dataset_sink_mode=(args_opt.enable_data_sink == "true"),
sink_size=args_opt.data_sink_steps)
def _get_optimizer(args_opt, network):
"""get bert optimizer, support Lamb, Momentum, AdamWeightDecay and Thor."""
if cfg.optimizer == 'Lamb':
lr_schedule = BertLearningRate(
learning_rate=cfg.Lamb.learning_rate,
end_learning_rate=cfg.Lamb.end_learning_rate,
warmup_steps=cfg.Lamb.warmup_steps,
decay_steps=args_opt.train_steps,
power=cfg.Lamb.power)
params = network.trainable_params()
decay_params = list(filter(cfg.Lamb.decay_filter, params))
other_params = list(
filter(lambda x: not cfg.Lamb.decay_filter(x), params))
group_params = [{
'params': decay_params,
'weight_decay': cfg.Lamb.weight_decay
}, {
'params': other_params
}, {
'order_params': params
}]
optimizer = Lamb(group_params,
learning_rate=lr_schedule,
eps=cfg.Lamb.eps)
elif cfg.optimizer == 'Momentum':
optimizer = Momentum(network.trainable_params(),
learning_rate=cfg.Momentum.learning_rate,
momentum=cfg.Momentum.momentum)
elif cfg.optimizer == 'AdamWeightDecay':
lr_schedule = BertLearningRate(
learning_rate=cfg.AdamWeightDecay.learning_rate,
end_learning_rate=cfg.AdamWeightDecay.end_learning_rate,
warmup_steps=cfg.AdamWeightDecay.warmup_steps,
decay_steps=args_opt.train_steps,
power=cfg.AdamWeightDecay.power)
params = network.trainable_params()
decay_params = list(filter(cfg.AdamWeightDecay.decay_filter, params))
other_params = list(
filter(lambda x: not cfg.AdamWeightDecay.decay_filter(x), params))
group_params = [{
'params': decay_params,
'weight_decay': cfg.AdamWeightDecay.weight_decay
}, {
'params': other_params,
'weight_decay': 0.0
}, {
'order_params': params
}]
optimizer = AdamWeightDecay(group_params,
learning_rate=lr_schedule,
eps=cfg.AdamWeightDecay.eps)
elif cfg.optimizer == "Thor":
if args_opt.distribute == "true":
from src.thor_for_bert_arg import THOR
else:
from src.thor_for_bert import THOR
lr = get_bert_lr()
damping = get_bert_damping()
optimizer = THOR(
filter(lambda x: x.requires_grad,
network.get_parameters()), lr, cfg.Thor.momentum,
filter(lambda x: 'matrix_A' in x.name, network.get_parameters()),
filter(lambda x: 'matrix_G' in x.name, network.get_parameters()),
cfg.Thor.weight_decay, cfg.Thor.loss_scale,
bert_net_cfg.num_hidden_layers, bert_net_cfg.batch_size, damping)
else:
raise ValueError(
"Don't support optimizer {}, only support [Lamb, Momentum, AdamWeightDecay, Thor]"
.format(cfg.optimizer))
return optimizer