def _get_parameter_broadcast():
"""Get the parameter broadcast."""
parallel_mode = auto_parallel_context().get_parallel_mode()
parameter_broadcast = auto_parallel_context().get_parameter_broadcast()
if parallel_mode in (
"data_parallel", "hybrid_parallel"
) and parameter_broadcast is False and get_seed() is None:
logger.warning(
"You are suggested to use mindspore.context.set_auto_parallel_context(parameter_broadcast=True)"
" or mindspore.common.set_seed() to share parameters among multi-devices."
)
return parameter_broadcast
def __init__(self, keep_prob=0.5, dtype=mstype.float32):
super(Dropout, self).__init__()
if keep_prob <= 0 or keep_prob > 1:
raise ValueError(
"dropout probability should be a number in range (0, 1], but got {}"
.format(keep_prob))
Validator.check_subclass("dtype", dtype, mstype.number_type,
self.cls_name)
Validator.check_value_type('keep_prob', keep_prob, [float],
self.cls_name)
self.keep_prob = keep_prob
seed0 = get_seed()
self.seed0 = seed0 if seed0 is not None else 0
self.seed1 = 0
self.dtype = dtype
self.get_shape = P.Shape()
self.dropout_gen_mask = P.DropoutGenMask(Seed0=self.seed0,
Seed1=self.seed1)
self.dropout_do_mask = P.DropoutDoMask()
self.cast = P.Cast()
self.is_gpu = context.get_context('device_target') in ["GPU"]
self.dropout = P.Dropout(keep_prob)