mindspore.nn.thor

mindspore.nn.thor(net, learning_rate, damping, momentum, weight_decay=0.0, loss_scale=1.0, batch_size=32, use_nesterov=False, decay_filter=lambda x: ..., split_indices=None, enable_clip_grad=False, frequency=100)[source]

Updates gradients by second-order algorithm–THOR.

The updating formulas are as follows,

\[\begin{split}\begin{array}{ll} & \textbf{Parameter:} \: \text{the learning rate } \gamma\text{, the damping parameter }\lambda \\ & \textbf{Init:} \: \lambda \leftarrow 0 \\ & A_{i-1}=\mathbb{E}\left[a_{i-1} a_{i-1}^{T}\right] \\ & G_{i}=\mathbb{E}\left[D_{s_i} D_{s_i}^{T}\right] \\ & w_{i}^{(k+1)} \leftarrow w_{i}^{(k)}-\gamma\left(\left(A_{i-1}^{(k)}+\lambda I\right)^{-1} \otimes\left(G_{i}^{(k)}+\lambda I\right)^{-1}\right) \nabla_{w_{i}} J^{(k)} \end{array}\end{split}\]

\(a_{i-1}\) represents the input of i-th layer,and which is the activations of previous layer. \(D_{s_i}\) represents the derivative of the loss function of the output of the i-th layer. \(I\) represents the identity matrix. \(\lambda\) represents \(damping\), \(g_i\) represents gradients of the i-th layer. \(\otimes\) represents Kronecker product, \(\gamma\) represents ‘learning rate’.

Note

When a parameter group is separated, ‘weight_decay’ of each group is applied to the corresponding parameter. ‘weight_decay’ in the optimizer is applied to arguments that do not have ‘beta’ or ‘gamma’ in their name when the argument group is not separated. When separating parameter groups, set grad_centralization to True if you want to concentrate gradients, but concentration gradients can only be applied to parameters of the convolution layer. If the parameter for the unconvolutional layer is set to True, an error will be reported. To improve the performance of parameter groups, you can customize the order of parameters.

Parameters

  • net (Cell) – The training network.

  • learning_rate (Tensor) – A value for the learning rate.

  • damping (Tensor) – A value for the damping.

  • momentum (float) – Hyper-parameter of type float, means momentum for the moving average. It must be at least 0.0.

  • weight_decay (int, float) – Weight decay (L2 penalty). It must be equal to or greater than 0.0. Default: 0.0.

  • loss_scale (float) – A value for the loss scale. It must be greater than 0.0. In general, use the default value. Default: 1.0.

  • batch_size (int) – The size of a batch. Default: 32

  • use_nesterov (bool) – Enable Nesterov momentum. Default: False.

  • decay_filter (function) – A function to determine which layers the weight decay applied to. And it only works when the weight_decay > 0. Default: lambda x: x.name not in []

  • split_indices (list) – Set allreduce fusion strategy by A/G layer indices . Only works when distributed computing. ResNet50 as an example, there are 54 layers of A/G respectively, when split_indices is set to [26, 53], it means A/G is divided into two groups to allreduce, one is 0~26 layer, and the other is 27~53. Default: None

  • enable_clip_grad (bool) – Whether to clip the gradients. Default: False

  • frequency (int) – The update interval of A/G and \(A^{-1}/G^{-1}\). When frequency equals N (N is greater than 1), A/G and \(A^{-1}/G^{-1}\) will be updated every N steps, and other steps will use the stale A/G and \(A^{-1}/G^{-1}\) to update weights. Default: 100.

Inputs:

  • gradients (tuple[Tensor]) - The gradients of params, the shape is the same as params.

Outputs:

tuple[bool], all elements are True.

Raises

  • TypeError – If learning_rate is not Tensor.

  • TypeError – If loss_scale, momentum or frequency is not a float.

  • TypeError – If weight_decay is neither float nor int.

  • TypeError – If use_nesterov is not a bool.

  • TypeError – If frequency is not int.

  • ValueError – If loss_scale is less than or equal to 0.

  • ValueError – If weight_decay or momentum is less than 0.

  • ValueError – If frequency is less than 2.

Supported Platforms:

Ascend GPU

Examples

>>> import mindspore as ms
>>> from mindspore.nn import thor
>>> from mindspore import nn
>>> from mindspore import Tensor
>>>
>>> # Define the network structure of LeNet5. Refer to
>>> # https://gitee.com/mindspore/docs/blob/r2.0/docs/mindspore/code/lenet.py
>>> net = LeNet5()
>>> # Create the dataset taking MNIST as an example. Refer to
>>> # https://gitee.com/mindspore/docs/blob/r2.0/docs/mindspore/code/mnist.py
>>> dataset = create_dataset()
>>> temp = Tensor([4e-4, 1e-4, 1e-5, 1e-5], mstype.float32)
>>> optim = thor(net, learning_rate=temp, damping=temp, momentum=0.9, loss_scale=128, frequency=4)
>>> loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
>>> loss_scale = ms.FixedLossScaleManager(128, drop_overflow_update=False)
>>> model = ms.Model(net, loss_fn=loss, optimizer=optim, loss_scale_manager=loss_scale, metrics={'acc'},
...               amp_level="O2", keep_batchnorm_fp32=False)
>>> model = ms.ConvertModelUtils.convert_to_thor_model(model=model, network=net, loss_fn=loss, optimizer=optim,
...                                                 loss_scale_manager=loss_scale, metrics={'acc'},
...                                                 amp_level="O2", keep_batchnorm_fp32=False)
>>> loss_cb = ms.LossMonitor()
>>> model.train(1, dataset, callbacks=loss_cb, sink_size=4, dataset_sink_mode=True)