def __init__(self, network, optimizer, sens=1.0):
super(TrainingWrapper, self).__init__(auto_prefix=False)
self.network = network
self.network.set_grad()
self.weights = ms.ParameterTuple(network.trainable_params())
self.optimizer = optimizer
self.grad = C.GradOperation(get_by_list=True, sens_param=True)
self.sens = sens
self.reducer_flag = False
self.grad_reducer = None
self.parallel_mode = context.get_auto_parallel_context("parallel_mode")
if self.parallel_mode in [
ParallelMode.DATA_PARALLEL, ParallelMode.HYBRID_PARALLEL
]:
self.reducer_flag = True
if self.reducer_flag:
mean = context.get_auto_parallel_context("gradients_mean")
if auto_parallel_context().get_device_num_is_set():
degree = context.get_auto_parallel_context("device_num")
else:
degree = get_group_size()
self.grad_reducer = nn.DistributedGradReducer(
optimizer.parameters, mean, degree)
def __init__(self, net):
super(GradNetWrap, self).__init__()
self.net = net
self.weights = mindspore.ParameterTuple(net.get_parameters())
