oneflow.nn.LayerNorm¶

class oneflow.nn.LayerNorm(normalized_shape: Union[int, Tuple[int], oneflow.Size], eps: float = 1e-05, elementwise_affine: bool = True)¶

Applies Layer Normalization over a mini-batch of inputs as described in the paper Layer Normalization

\[y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta\]

The mean and standard-deviation are calculated separately over the last certain number dimensions which have to be of the shape specified by normalized_shape. \(\gamma\) and \(\beta\) are learnable affine transform parameters of normalized_shape if elementwise_affine is True. The standard-deviation is calculated via the biased estimator.

Note

Unlike Batch Normalization and Instance Normalization, which applies scalar scale and bias for each entire channel/plane with the affine option, Layer Normalization applies per-element scale and bias with elementwise_affine.

This layer uses statistics computed from input data in both training and evaluation modes.

Parameters

normalized_shape (int or list or oneflow.Size) –
input shape from an expected input of size

\[[* \times \text{normalized_shape}[0] \times \text{normalized_shape}[1] \times \ldots \times \text{normalized_shape}[-1]]\]

If a single integer is used, it is treated as a singleton list, and this module will

normalize over the last dimension which is expected to be of that specific size.
eps – a value added to the denominator for numerical stability. Default: 1e-5
elementwise_affine – a boolean value that when set to True, this module has learnable per-element affine parameters initialized to ones (for weights) and zeros (for biases). Default: True.

Shape:

Input: \((N, *)\)
Output: \((N, *)\) (same shape as input)

For example:

>>> import numpy as np
>>> import oneflow as flow

>>> input_arr = np.array(
...     [
...         [
...             [[-0.16046895, -1.03667831], [-0.34974465, 0.26505867]],
...             [[-1.24111986, -0.53806001], [1.72426331, 0.43572459]],
...         ],
...         [
...             [[-0.77390957, -0.42610624], [0.16398858, -1.35760343]],
...             [[1.07541728, 0.11008703], [0.26361224, -0.48663723]],
...         ],
...     ],
...     dtype=np.float32,
... )

>>> x = flow.Tensor(input_arr)
>>> m = flow.nn.LayerNorm(2)
>>> y = m(x).numpy()
>>> y
array([[[[ 0.99997395, -0.99997395],
         [-0.999947  ,  0.999947  ]],

        [[-0.99995965,  0.9999595 ],
         [ 0.99998784, -0.99998784]]],


       [[[-0.9998348 ,  0.99983466],
         [ 0.9999914 , -0.9999914 ]],

        [[ 0.9999785 , -0.9999785 ],
         [ 0.9999646 , -0.9999646 ]]]], dtype=float32)

__init__(normalized_shape: Union[int, Tuple[int], oneflow.Size], eps: float = 1e-05, elementwise_affine: bool = True) → None ¶: Initialize self. See help(type(self)) for accurate signature.

Methods

`__call__`(args, *kwargs)	Call self as a function.
`__delattr__`(name, /)	Implement delattr(self, name).
`__dir__`()	Default dir() implementation.
`__eq__`(value, /)	Return self==value.
`__format__`(format_spec, /)	Default object formatter.
`__ge__`(value, /)	Return self>=value.
`__getattr__`(name)
`__getattribute__`(name, /)	Return getattr(self, name).
`__gt__`(value, /)	Return self>value.
`__hash__`()	Return hash(self).
`__init__`(normalized_shape[, eps, …])	Initialize self.
`__init_subclass__`	This method is called when a class is subclassed.
`__le__`(value, /)	Return self<=value.
`__lt__`(value, /)	Return self<value.
`__ne__`(value, /)	Return self!=value.
`__new__`(**kwargs)	Create and return a new object.
`__reduce__`()	Helper for pickle.
`__reduce_ex__`(protocol, /)	Helper for pickle.
`__repr__`()	Return repr(self).
`__setattr__`(name, value)	Implement setattr(self, name, value).
`__sizeof__`()	Size of object in memory, in bytes.
`__str__`()	Return str(self).
`__subclasshook__`	Abstract classes can override this to customize issubclass().
`_apply`(fn[, applied_dict])
`_get_name`()
`_load_from_state_dict`(state_dict, prefix, …)
`_named_members`(get_members_fn[, prefix, recurse])
`_save_to_state_dict`(destination, prefix, …)
`_shallow_repr`()
`add_module`(name, module)	Adds a child module to the current module.
`apply`(fn)	Applies `fn` recursively to every submodule (as returned by `.children()`) as well as self.
`buffers`([recurse])	Returns an iterator over module buffers.
`children`()	Returns an iterator over immediate children modules.
`cpu`()	Moves all model parameters and buffers to the CPU.
`cuda`([device])	Moves all model parameters and buffers to the GPU.
`double`()	Casts all floating point parameters and buffers to `double` datatype.
`eval`()	Sets the module in evaluation mode.
`extra_repr`()	Set the extra representation of the module
`float`()	Casts all floating point parameters and buffers to `float` datatype.
`forward`(x)
`half`()	Casts all floating point parameters and buffers to `half` datatype.
`load_state_dict`(state_dict[, strict])	Copies parameters and buffers from `state_dict` into this module and its descendants.
`modules`()	Returns an iterator over all modules in the network.
`named_buffers`([prefix, recurse])	Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
`named_children`()	Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
`named_modules`([memo, prefix])	Returns an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
`named_parameters`([prefix, recurse])	Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
`parameters`([recurse])	Returns an iterator over module parameters.
`register_buffer`(name, tensor[, persistent])	Adds a buffer to the module.
`register_forward_hook`(hook)	Registers a forward hook on the module.
`register_forward_pre_hook`(hook)	Registers a forward pre-hook on the module.
`register_parameter`(name, param)	Adds a parameter to the module.
`reset_parameters`()
`state_dict`([destination, prefix, keep_vars])	Returns a dictionary containing a whole state of the module.
`to`([device])	Moves the parameters and buffers.
`to_consistent`(args, *kwargs)	This interface is no longer available, please use `oneflow.nn.Module.to_global()` instead.
`to_global`([placement, sbp])	Convert the parameters and buffers to global.
`train`([mode])	Sets the module in training mode.
`zero_grad`([set_to_none])	Sets gradients of all model parameters to zero.

Attributes

`__annotations__`
`__constants__`