mindspore.amp.FixedLossScaleManager
- class mindspore.amp.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.amp.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
>>> import mindspore as ms >>> from mindspore import amp, nn >>> >>> # Define the network structure of LeNet5. Refer to >>> # https://gitee.com/mindspore/docs/blob/master/docs/mindspore/code/lenet.py >>> net = LeNet5() >>> loss_scale = 1024.0 >>> loss_scale_manager = amp.FixedLossScaleManager(loss_scale, False) >>> optim = nn.Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9, loss_scale=loss_scale) >>> model = ms.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.Cell
that 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.nn.FixedLossScaleUpdateCell
. Instance ofmindspore.nn.FixedLossScaleUpdateCell
when drop_overflow_update is True. None when drop_overflow_update is False.
- update_loss_scale(overflow)[source]
Update loss scale value. The interface at
mindspore.amp.FixedLossScaleManager
will do nothing.- Parameters
overflow (bool) – Whether it overflows.