haste_pytorch.LayerNormLSTM

Class `LayerNormLSTM`

Layer Normalized Long Short-Term Memory layer.

This LSTM layer applies layer normalization to the input, recurrent, and output activations of a standard LSTM. The implementation is fused and GPU-accelerated. DropConnect and Zoneout regularization are built-in, and this layer allows setting a non-zero initial forget gate bias.

Details about the exact function this layer implements can be found at #1.

See __init__ and forward for usage.

`init`

__init__(
    input_size,
    hidden_size,
    batch_first=False,
    forget_bias=1.0,
    dropout=0.0,
    zoneout=0.0
)

Initialize the parameters of the LSTM layer.

Arguments:

input_size: int, the feature dimension of the input.
hidden_size: int, the feature dimension of the output.
batch_first: (optional) bool, if True, then the input and output tensors are provided as (batch, seq, feature).
forget_bias: (optional) float, sets the initial bias of the forget gate for this LSTM cell.
dropout: (optional) float, sets the dropout rate for DropConnect regularization on the recurrent matrix.
zoneout: (optional) float, sets the zoneout rate for Zoneout regularization.

Variables:

kernel: the input projection weight matrix. Dimensions (input_size, hidden_size * 4) with i,g,f,o gate layout. Initialized with Xavier uniform initialization.
recurrent_kernel: the recurrent projection weight matrix. Dimensions (hidden_size, hidden_size * 4) with i,g,f,o gate layout. Initialized with orthogonal initialization.
bias: the projection bias vector. Dimensions (hidden_size * 4) with i,g,f,o gate layout. The forget gate biases are initialized to forget_bias and the rest are zeros.
gamma: the input and recurrent normalization gain. Dimensions (2, hidden_size * 4) with gamma[0] specifying the input gain and gamma[1] specifying the recurrent gain. Initialized to ones.
gamma_h: the output normalization gain. Dimensions (hidden_size). Initialized to ones.
beta_h: the output normalization bias. Dimensions (hidden_size). Initialized to zeros.

Methods

`call`

__call__(
    *input,
    **kwargs
)

Call self as a function.

`add_module`

add_module(
    name,
    module
)

Adds a child module to the current module.

The module can be accessed as an attribute using the given name.

Args:

name (string): name of the child module. The child module can be accessed from this module using the given name module (Module): child module to be added to the module.

`apply`

apply(fn)

Applies fn recursively to every submodule (as returned by .children()) as well as self. Typical use includes initializing the parameters of a model (see also :ref:nn-init-doc).

Args:

fn (:class:Module -> None): function to be applied to each submodule

Returns:

Module: self

Example::

```
>>> def init_weights(m):
>>>     print(m)
>>>     if type(m) == nn.Linear:
>>>         m.weight.data.fill_(1.0)
>>>         print(m.weight)
>>> net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2))
>>> net.apply(init_weights)
Linear(in_features=2, out_features=2, bias=True)
Parameter containing:
tensor([[ 1.,  1.],
        [ 1.,  1.]])
Linear(in_features=2, out_features=2, bias=True)
Parameter containing:
tensor([[ 1.,  1.],
        [ 1.,  1.]])
Sequential(
  (0): Linear(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
)
Sequential(
  (0): Linear(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
)
```

`buffers`

buffers(recurse=True)

Returns an iterator over module buffers.

Args:

recurse (bool): if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module.

Yields:

torch.Tensor: module buffer

Example::

```
>>> for buf in model.buffers():
>>>     print(type(buf.data), buf.size())
<class 'torch.FloatTensor'> (20L,)
<class 'torch.FloatTensor'> (20L, 1L, 5L, 5L)
```

`children`

children()

Returns an iterator over immediate children modules.

Yields:

Module: a child module

`cpu`

cpu()

Moves all model parameters and buffers to the CPU.

Returns:

Module: self

`cuda`

cuda(device=None)

Moves all model parameters and buffers to the GPU.

This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on GPU while being optimized.

Arguments:

device (int, optional): if specified, all parameters will be copied to that device

Returns:

Module: self

`double`

double()

Casts all floating point parameters and buffers to double datatype.

Returns:

Module: self

`eval`

eval()

Sets the module in evaluation mode.

This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. :class:Dropout, :class:BatchNorm, etc.

This is equivalent with :meth:self.train(False) <torch.nn.Module.train>.

Returns:

Module: self

`extra_repr`

extra_repr()

Set the extra representation of the module

To print customized extra information, you should reimplement this method in your own modules. Both single-line and multi-line strings are acceptable.

`float`

float()

Casts all floating point parameters and buffers to float datatype.

Returns:

Module: self

`forward`

forward(
    input,
    state=None,
    lengths=None
)

Runs a forward pass of the LSTM layer.

Arguments:

input: Tensor, a batch of input sequences to pass through the LSTM. Dimensions (seq_len, batch_size, input_size) if batch_first is False, otherwise (batch_size, seq_len, input_size).
lengths: (optional) Tensor, list of sequence lengths for each batch element. Dimension (batch_size). This argument may be omitted if all batch elements are unpadded and have the same sequence length.

Returns:

output: Tensor, the output of the LSTM layer. Dimensions (seq_len, batch_size, hidden_size) if batch_first is False (default) or (batch_size, seq_len, hidden_size) if batch_first is True. Note that if lengths was specified, the output tensor will not be masked. It's the caller's responsibility to either not use the invalid entries or to mask them out before using them.
(h_n, c_n): the hidden and cell states, respectively, for the last sequence item. Dimensions (1, batch_size, hidden_size).

`half`

half()

Casts all floating point parameters and buffers to half datatype.

Returns:

Module: self

`load_state_dict`

load_state_dict(
    state_dict,
    strict=True
)

Copies parameters and buffers from :attr:state_dict into this module and its descendants. If :attr:strict is True, then the keys of :attr:state_dict must exactly match the keys returned by this module's :meth:~torch.nn.Module.state_dict function.

Arguments:

state_dict (dict): a dict containing parameters and persistent buffers. strict (bool, optional): whether to strictly enforce that the keys in :attr:state_dict match the keys returned by this module's :meth:~torch.nn.Module.state_dict function. Default: True

Returns:

NamedTuple with missing_keys and unexpected_keys fields: * missing_keys is a list of str containing the missing keys * unexpected_keys is a list of str containing the unexpected keys

`modules`

modules()

Returns an iterator over all modules in the network.

Yields:

Module: a module in the network

Note:

Duplicate modules are returned only once. In the following example, l will be returned only once.

Example::

```
>>> l = nn.Linear(2, 2)
>>> net = nn.Sequential(l, l)
>>> for idx, m in enumerate(net.modules()):
        print(idx, '->', m)
```

0 -> Sequential(
  (0): Linear(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
)
1 -> Linear(in_features=2, out_features=2, bias=True)

`named_buffers`

named_buffers(
    prefix='',
    recurse=True
)

Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.

Args:

prefix (str): prefix to prepend to all buffer names. recurse (bool): if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module.

Yields:

(string, torch.Tensor): Tuple containing the name and buffer

Example::

```
>>> for name, buf in self.named_buffers():
>>>    if name in ['running_var']:
>>>        print(buf.size())
```

`named_children`

named_children()

Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.

Yields:

(string, Module): Tuple containing a name and child module

Example::

```
>>> for name, module in model.named_children():
>>>     if name in ['conv4', 'conv5']:
>>>         print(module)
```

`named_modules`

named_modules(
    memo=None,
    prefix=''
)

Returns an iterator over all modules in the network, yielding both the name of the module as well as the module itself.

Yields:

(string, Module): Tuple of name and module

Note:

Duplicate modules are returned only once. In the following example, l will be returned only once.

Example::

```
>>> l = nn.Linear(2, 2)
>>> net = nn.Sequential(l, l)
>>> for idx, m in enumerate(net.named_modules()):
        print(idx, '->', m)
```

0 -> ('', Sequential(
  (0): Linear(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
))
1 -> ('0', Linear(in_features=2, out_features=2, bias=True))

`named_parameters`

named_parameters(
    prefix='',
    recurse=True
)

Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.

Args:

prefix (str): prefix to prepend to all parameter names. recurse (bool): if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module.

Yields:

(string, Parameter): Tuple containing the name and parameter

Example::

```
>>> for name, param in self.named_parameters():
>>>    if name in ['bias']:
>>>        print(param.size())
```

`parameters`

parameters(recurse=True)

Returns an iterator over module parameters.

This is typically passed to an optimizer.

Args:

recurse (bool): if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module.

Yields:

Parameter: module parameter

Example::

```
>>> for param in model.parameters():
>>>     print(type(param.data), param.size())
<class 'torch.FloatTensor'> (20L,)
<class 'torch.FloatTensor'> (20L, 1L, 5L, 5L)
```

`register_backward_hook`

register_backward_hook(hook)

Registers a backward hook on the module.

The hook will be called every time the gradients with respect to module inputs are computed. The hook should have the following signature::

hook(module, grad_input, grad_output) -> Tensor or None

The :attr:grad_input and :attr:grad_output may be tuples if the module has multiple inputs or outputs. The hook should not modify its arguments, but it can optionally return a new gradient with respect to input that will be used in place of :attr:grad_input in subsequent computations.

Returns:

:class:torch.utils.hooks.RemovableHandle: a handle that can be used to remove the added hook by calling handle.remove()

.. warning ::

The current implementation will not have the presented behavior
for complex :class:`Module` that perform many operations.
In some failure cases, :attr:`grad_input` and :attr:`grad_output` will only
contain the gradients for a subset of the inputs and outputs.
For such :class:`Module`, you should use :func:`torch.Tensor.register_hook`
directly on a specific input or output to get the required gradients.

`register_buffer`

register_buffer(
    name,
    tensor
)

Adds a persistent buffer to the module.

This is typically used to register a buffer that should not to be considered a model parameter. For example, BatchNorm's running_mean is not a parameter, but is part of the persistent state.

Buffers can be accessed as attributes using given names.

Args:

name (string): name of the buffer. The buffer can be accessed from this module using the given name tensor (Tensor): buffer to be registered.

Example::

```
>>> self.register_buffer('running_mean', torch.zeros(num_features))
```

`register_forward_hook`

register_forward_hook(hook)

Registers a forward hook on the module.

The hook will be called every time after :func:forward has computed an output. It should have the following signature::

hook(module, input, output) -> None or modified output

The hook can modify the output. It can modify the input inplace but it will not have effect on forward since this is called after :func:forward is called.

Returns:

:class:torch.utils.hooks.RemovableHandle: a handle that can be used to remove the added hook by calling handle.remove()

`register_forward_pre_hook`

register_forward_pre_hook(hook)

Registers a forward pre-hook on the module.

The hook will be called every time before :func:forward is invoked. It should have the following signature::

hook(module, input) -> None or modified input

The hook can modify the input. User can either return a tuple or a single modified value in the hook. We will wrap the value into a tuple if a single value is returned(unless that value is already a tuple).

Returns:

:class:torch.utils.hooks.RemovableHandle: a handle that can be used to remove the added hook by calling handle.remove()

`register_parameter`

register_parameter(
    name,
    param
)

Adds a parameter to the module.

The parameter can be accessed as an attribute using given name.

Args:

name (string): name of the parameter. The parameter can be accessed from this module using the given name param (Parameter): parameter to be added to the module.

`requires_grad_`

requires_grad_(requires_grad=True)

Change if autograd should record operations on parameters in this module.

This method sets the parameters' :attr:requires_grad attributes in-place.

This method is helpful for freezing part of the module for finetuning or training parts of a model individually (e.g., GAN training).

Args:

requires_grad (bool): whether autograd should record operations on parameters in this module. Default: True.

Returns:

Module: self

`reset_parameters`

reset_parameters()

Resets this layer's parameters to their initial values.

`share_memory`

share_memory()

`state_dict`

state_dict(
    destination=None,
    prefix='',
    keep_vars=False
)

Returns a dictionary containing a whole state of the module.

Both parameters and persistent buffers (e.g. running averages) are included. Keys are corresponding parameter and buffer names.

Returns:

dict: a dictionary containing a whole state of the module

Example::

```
>>> module.state_dict().keys()
['bias', 'weight']
```

`to`

to(
    *args,
    **kwargs
)

Moves and/or casts the parameters and buffers.

This can be called as

.. function:: to(device=None, dtype=None, non_blocking=False)

.. function:: to(dtype, non_blocking=False)

.. function:: to(tensor, non_blocking=False)

Its signature is similar to :meth:torch.Tensor.to, but only accepts floating point desired :attr:dtype s. In addition, this method will only cast the floating point parameters and buffers to :attr:dtype (if given). The integral parameters and buffers will be moved :attr:device, if that is given, but with dtypes unchanged. When :attr:non_blocking is set, it tries to convert/move asynchronously with respect to the host if possible, e.g., moving CPU Tensors with pinned memory to CUDA devices.

See below for examples.

.. note:: This method modifies the module in-place.

Args:

device (:class:torch.device): the desired device of the parameters and buffers in this module dtype (:class:torch.dtype): the desired floating point type of the floating point parameters and buffers in this module tensor (torch.Tensor): Tensor whose dtype and device are the desired dtype and device for all parameters and buffers in this module

Returns:

Module: self

Example::

```
>>> linear = nn.Linear(2, 2)
>>> linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
        [-0.5113, -0.2325]])
>>> linear.to(torch.double)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
        [-0.5113, -0.2325]], dtype=torch.float64)
>>> gpu1 = torch.device("cuda:1")
>>> linear.to(gpu1, dtype=torch.half, non_blocking=True)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1914, -0.3420],
        [-0.5112, -0.2324]], dtype=torch.float16, device='cuda:1')
>>> cpu = torch.device("cpu")
>>> linear.to(cpu)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1914, -0.3420],
        [-0.5112, -0.2324]], dtype=torch.float16)
```

`train`

train(mode=True)

Sets the module in training mode.

This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. :class:Dropout, :class:BatchNorm, etc.

Args:

mode (bool): whether to set training mode (True) or evaluation mode (False). Default: True.

Returns:

Module: self

`type`

type(dst_type)

Casts all parameters and buffers to :attr:dst_type.

Arguments:

dst_type (type or string): the desired type

Returns:

Module: self

`zero_grad`

zero_grad()

Sets gradients of all model parameters to zero.

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haste_pytorch.LayerNormLSTM

Class LayerNormLSTM

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Class `LayerNormLSTM`