mindspore.ops.LayerNorm

class mindspore.ops.LayerNorm(begin_norm_axis=1, begin_params_axis=1, epsilon=1e-7)[source]

Applies the Layer Normalization to the input tensor.

This operator will normalize the input tensor on given axis. LayerNorm is described in the paper Layer Normalization.

\[y = \frac{x - mean}{\sqrt{variance + \epsilon}} * \gamma + \beta\]

where \(\gamma\) is scale, \(\beta\) is bias, \(\epsilon\) is epsilon.

Parameters

  • begin_norm_axis (int) – The begin axis of the input_x to apply LayerNorm, the value must be in [-1, rank(input)). Default: 1.

  • begin_params_axis (int) – The begin axis of the parameter input (gamma, beta) to apply LayerNorm, the value must be in [-1, rank(input)). Default: 1.

  • epsilon (float) – A value added to the denominator for numerical stability. Default: 1e-7.

Inputs:

  • input_x (Tensor) - Tensor of shape \((N, \ldots)\). The input of LayerNorm.

  • gamma (Tensor) - Tensor of shape \((P_0, \ldots, P_\text{begin_params_axis})\). The learnable parameter gamma as the scale on norm.

  • beta (Tensor) - Tensor of shape \((P_0, \ldots, P_\text{begin_params_axis})\). The learnable parameter beta as the scale on norm.

Outputs:

tuple[Tensor], tuple of 3 tensors, the normalized input and the updated parameters.

  • output_x (Tensor) - The normalized input, has the same type and shape as the input_x. The shape is \((N, C)\).

  • mean (Tensor) - Tensor of shape \((C,)\).

  • variance (Tensor) - Tensor of shape \((C,)\).

Raises

  • TypeError – If begin_norm_axis or begin_params_axis is not an int.

  • TypeError – If epsilon is not a float.

  • TypeError – If input_x, gamma or beta is not a Tensor.

Supported Platforms:

Ascend GPU CPU

Examples

>>> input_x = Tensor(np.array([[1, 2, 3], [1, 2, 3]]), mindspore.float32)
>>> gamma = Tensor(np.ones([3]), mindspore.float32)
>>> beta = Tensor(np.ones([3]), mindspore.float32)
>>> layer_norm = ops.LayerNorm()
>>> output, mean, variance = layer_norm(input_x, gamma, beta)
>>> print(output)
[[-0.2247448  1.         2.2247448]
 [-0.2247448  1.         2.2247448]]
>>> print(mean)
[[2.]
 [2.]]
>>> print(variance)
[[0.6666667]
 [0.6666667]]