def __init__(self,
size=256,
batch_size=16,
image_size=(96, ),
num_classes=16,
random_offset=0):
"""init"""
self.size = size
self.rank_batch_size = batch_size
self.total_batch_size = self.rank_batch_size
self.random_offset = random_offset
self.image_size = image_size
self.num_classes = num_classes
self.num_epochs = -1
self.rank_size = 1
self.rank_id = 0
self.batch_index = 0
self.image_data_type = np.float32
self.label_data_type = np.float32
self.is_onehot = True
init(backend_name='hccl')
self.rank_size = get_group_size()
self.rank_id = get_rank()
self.total_batch_size = self.rank_batch_size * self.rank_size
self.total_batch_data_size = (self.rank_size,
self.rank_batch_size) + image_size
self.do_copy = False
def test_inference():
"""distributed inference after distributed training"""
context.set_context(mode=context.GRAPH_MODE)
init(backend_name="hccl")
context.set_auto_parallel_context(full_batch=True, parallel_mode="semi_auto_parallel",
strategy_ckpt_load_file="./train_strategy.ckpt", device_num=8)
predict_data = create_predict_data()
network = Net(matmul_size=(96, 16))
model = Model(network)
predict_layout = model.infer_predict_layout(Tensor(predict_data))
ckpt_file_list = create_ckpt_file_list()
load_distributed_checkpoint(network, ckpt_file_list, predict_layout)
predict_result = model.predict(predict_data)
print(predict_result)
def __run_standalone(self):
# import
from mindspore import context
from mindspore.communication import init
from mindspore.context import ParallelMode
# set context: device_target
context.set_context(device_target=self.__device_target)
# set context: mode
if self.__graph_mode:
context.set_context(mode=context.GRAPH_MODE)
# set context: save_graphs
context.set_context(save_graphs=self.__save_graphs)
# set context: device_id
device_id = int(os.environ.get("DEVICE_ID", 0))
context.set_context(device_id=device_id)
# init
device_num = int(os.environ.get("DEVICE_NUM", 1))
if device_num > 1 and "win32" not in sys.platform:
context.reset_auto_parallel_context()
context.set_auto_parallel_context(
device_num=device_num,
parallel_mode=ParallelMode.DATA_PARALLEL,
gradients_mean=True)
init()
if self.__dataset is None:
print(
"Warning: `dataset` is None. Please call func: `set_dataset($dataset)`."
)
if self.__network is None:
print(
"Warning: `network` is None. Please call func: `set_network($network)`."
)
if self.__dataset is None or self.__network is None:
return
if self.__do_eval:
self.__eval()
else:
self.__train()
def inception_v4_train():
"""
Train Inceptionv4 in data parallelism
"""
print('epoch_size: {} batch_size: {} class_num {}'.format(config.epoch_size, config.batch_size, config.num_classes))
context.set_context(mode=context.GRAPH_MODE, device_target=args.platform)
if args.platform == "Ascend":
context.set_context(device_id=args.device_id)
context.set_context(enable_graph_kernel=False)
rank = 0
if device_num > 1:
if args.platform == "Ascend":
init(backend_name='hccl')
elif args.platform == "GPU":
init()
else:
raise ValueError("Unsupported device target.")
rank = get_rank()
context.set_auto_parallel_context(device_num=device_num,
parallel_mode=ParallelMode.DATA_PARALLEL,
gradients_mean=True,
all_reduce_fusion_config=[200, 400])
# create dataset
train_dataset = create_dataset(dataset_path=args.dataset_path, do_train=True,
repeat_num=1, batch_size=config.batch_size, shard_id=rank)
train_step_size = train_dataset.get_dataset_size()
# create model
net = Inceptionv4(classes=config.num_classes)
# loss
loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
# learning rate
lr = Tensor(generate_cosine_lr(steps_per_epoch=train_step_size, total_epochs=config.epoch_size))
decayed_params = []
no_decayed_params = []
for param in net.trainable_params():
if 'beta' not in param.name and 'gamma' not in param.name and 'bias' not in param.name:
decayed_params.append(param)
else:
no_decayed_params.append(param)
for param in net.trainable_params():
if 'beta' not in param.name and 'gamma' not in param.name and 'bias' not in param.name:
param.set_data(initializer(XavierUniform(), param.data.shape, param.data.dtype))
group_params = [{'params': decayed_params, 'weight_decay': config.weight_decay},
{'params': no_decayed_params},
{'order_params': net.trainable_params()}]
opt = RMSProp(group_params, lr, decay=config.decay, epsilon=config.epsilon, weight_decay=config.weight_decay,
momentum=config.momentum, loss_scale=config.loss_scale)
if args.device_id == 0:
print(lr)
print(train_step_size)
if args.resume:
ckpt = load_checkpoint(args.resume)
load_param_into_net(net, ckpt)
loss_scale_manager = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
if args.platform == "Ascend":
model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc', 'top_1_accuracy', 'top_5_accuracy'},
loss_scale_manager=loss_scale_manager, amp_level=config.amp_level)
elif args.platform == "GPU":
model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc', 'top_1_accuracy', 'top_5_accuracy'},
loss_scale_manager=loss_scale_manager, amp_level='O0')
else:
raise ValueError("Unsupported device target.")
# define callbacks
performance_cb = TimeMonitor(data_size=train_step_size)
loss_cb = LossMonitor(per_print_times=train_step_size)
ckp_save_step = config.save_checkpoint_epochs * train_step_size
config_ck = CheckpointConfig(save_checkpoint_steps=ckp_save_step, keep_checkpoint_max=config.keep_checkpoint_max)
ckpoint_cb = ModelCheckpoint(prefix=f"inceptionV4-train-rank{rank}",
directory='ckpts_rank_' + str(rank), config=config_ck)
callbacks = [performance_cb, loss_cb]
if device_num > 1 and config.is_save_on_master:
if args.device_id == 0:
callbacks.append(ckpoint_cb)
else:
callbacks.append(ckpoint_cb)
# train model
model.train(config.epoch_size, train_dataset, callbacks=callbacks, dataset_sink_mode=True)
from mindspore import dtype as mstype
import mindspore.ops as ops
import mindspore.dataset as ds
import mindspore.dataset.vision.c_transforms as vision
import mindspore.dataset.transforms.c_transforms as C
from mindspore.communication import init, get_rank, get_group_size
from mindspore import Tensor, Model, context
from mindspore.nn import Momentum
from mindspore.context import ParallelMode
from mindspore.train.callback import LossMonitor
from resnet import resnet50
device_id = int(os.getenv('DEVICE_ID'))
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
context.set_context(device_id=device_id) # set device_id
init()
def create_dataset(data_path,
repeat_num=1,
batch_size=32,
rank_id=0,
rank_size=1): # pylint: disable=missing-docstring
resize_height = 224
resize_width = 224
rescale = 1.0 / 255.0
shift = 0.0
# get rank_id and rank_size
rank_id = get_rank()
rank_size = get_group_size()
import mindspore.nn as nn
from mindspore import dtype as mstype
import mindspore.ops as ops
import mindspore.dataset as ds
import mindspore.dataset.vision.c_transforms as vision
import mindspore.dataset.transforms.c_transforms as C
from mindspore.communication import init, get_rank, get_group_size
from mindspore import Tensor, Model
from mindspore.nn import Momentum
from mindspore.context import ParallelMode
from mindspore import context
from mindspore.train.callback import LossMonitor
from resnet import resnet50
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
init("nccl")
def create_dataset(data_path,
repeat_num=1,
batch_size=32,
rank_id=0,
rank_size=1): # pylint: disable=missing-docstring
resize_height = 224
resize_width = 224
rescale = 1.0 / 255.0
shift = 0.0
# get rank_id and rank_size
rank_id = get_rank()
rank_size = get_group_size()