mindspore.nn.DynamicLossScaleUpdateCell

class mindspore.nn.DynamicLossScaleUpdateCell(loss_scale_value, scale_factor, scale_window)[source]

Dynamic Loss scale update cell.

For loss scaling training, the initial loss scaling value will be set to be loss_scale_value. In each training step, the loss scaling value will be updated by loss scaling value/scale_factor when there is an overflow. And it will be increased by loss scaling value * scale_factor if there is no overflow for a continuous scale_window steps. This cell is used for Graph mode training in which all logic will be executed on device side(Another training mode is normal(non-sink) mode in which some logic will be executed on host).

Parameters

  • loss_scale_value (float) – Initializes loss scale.

  • scale_factor (int) – Coefficient of increase and decrease.

  • scale_window (int) – Maximum continuous training steps that do not have overflow.

Inputs:

  • loss_scale (Tensor) - The loss scale value during training with shape \(()\).

  • overflow (bool) - Whether the overflow occurs or not.

Outputs:

bool, the input overflow.

Raises

TypeError – If dtype of inputs or label is neither float16 nor float32.

Supported Platforms:

Ascend GPU

Examples

>>> import numpy as np
>>> from mindspore import Tensor, Parameter, nn
>>> import mindspore.ops as ops
>>>
>>> class Net(nn.Cell):
...     def __init__(self, in_features, out_features):
...         super(Net, self).__init__()
...         self.weight = Parameter(Tensor(np.ones([in_features, out_features]).astype(np.float32)),
...                                 name='weight')
...         self.matmul = ops.MatMul()
...
...     def construct(self, x):
...         output = self.matmul(x, self.weight)
...         return output
...
>>> in_features, out_features = 16, 10
>>> net = Net(in_features, out_features)
>>> loss = nn.MSELoss()
>>> optimizer = nn.Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
>>> net_with_loss = nn.WithLossCell(net, loss)
>>> manager = nn.DynamicLossScaleUpdateCell(loss_scale_value=2**12, scale_factor=2, scale_window=1000)
>>> train_network = nn.TrainOneStepWithLossScaleCell(net_with_loss, optimizer, scale_sense=manager)
>>> input = Tensor(np.ones([out_features, in_features]), mindspore.float32)
>>> labels = Tensor(np.ones([out_features,]), mindspore.float32)
>>> output = train_network(input, labels)
get_loss_scale()[source]

Get Loss Scale value.