mindspore.FixedLossScaleManager
- class mindspore.FixedLossScaleManager(loss_scale=128.0, drop_overflow_update=True)[source]
Loss scale(Magnification factor of gradients when mix precision is used) manager with a fixed loss scale value, inherits from
mindspore.LossScaleManager.- Parameters
loss_scale (float) – Magnification factor of gradients. Note that if drop_overflow_update is set to False, the value of loss_scale in optimizer should be set to the same as here. Default: 128.0.
drop_overflow_update (bool) – Whether to execute optimizer if there is an overflow. If True, the optimizer will not executed when overflow occurs. Default: True.
Examples
>>> from mindspore import Model, nn, FixedLossScaleManager >>> >>> net = Net() >>> #1) Drop the parameter update if there is an overflow >>> loss_scale_manager = FixedLossScaleManager() >>> optim = nn.Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9) >>> model = Model(net, loss_scale_manager=loss_scale_manager, optimizer=optim) >>> >>> #2) Execute parameter update even if overflow occurs >>> loss_scale = 1024.0 >>> loss_scale_manager = FixedLossScaleManager(loss_scale, False) >>> optim = nn.Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9, loss_scale=loss_scale) >>> model = Model(net, loss_scale_manager=loss_scale_manager, optimizer=optim)
- get_drop_overflow_update()[source]
Get drop_overflow_update, whether to drop optimizer update for current step when there is an overflow.
- Returns
bool, drop_overflow_update value.
- get_update_cell()[source]
Returns the instance of
mindspore.nn.Cellthat used to update the loss scale which will be called atmindspore.nn.TrainOneStepWithLossScaleCell. As the loss scale is fixed in this class, the instance will do nothing.- Returns
None or
mindspore.FixedLossScaleUpdateCell. Instance ofmindspore.FixedLossScaleUpdateCellwhen drop_overflow_update is True. None when drop_overflow_update is False.