torchrecurrent.RANCell

class torchrecurrent.RANCell(input_size, hidden_size, bias=True, kernel_init=<function xavier_uniform_>, recurrent_kernel_init=<function xavier_uniform_>, bias_init=<function zeros_>, recurrent_bias_init=<function zeros_>, device=None, dtype=None)

A Recurrent Additive Network (RAN) cell.

Implements the RAN update from “Recurrent Additive Networks” <https://arxiv.org/pdf/1705.07393>_.

\[\begin{split}\begin{aligned} \tilde{\mathbf{c}}(t) &= \mathbf{W}_{ih}^{c}\,\mathbf{x}(t) + \mathbf{b}_{ih}^{c}, \\ \mathbf{i}(t) &= \sigma\bigl( \mathbf{W}_{ih}^{i}\,\mathbf{x}(t) + \mathbf{b}_{ih}^{i} + \mathbf{W}_{hh}^{i}\,\mathbf{h}(t-1) + \mathbf{b}_{hh}^{i} \bigr), \\ \mathbf{f}(t) &= \sigma\bigl( \mathbf{W}_{ih}^{f}\,\mathbf{x}(t) + \mathbf{b}_{ih}^{f} + \mathbf{W}_{hh}^{f}\,\mathbf{h}(t-1) + \mathbf{b}_{hh}^{f} \bigr), \\ \mathbf{c}(t) &= \mathbf{i}(t)\circ\tilde{\mathbf{c}}(t) + \mathbf{f}(t)\circ\mathbf{c}(t-1), \\ \mathbf{h}(t) &= \tanh\bigl(\mathbf{c}(t)\bigr) \end{aligned}\end{split}\]

where \(\circ\) denotes element‐wise multiplication and \(\sigma\) is the sigmoid function.

Parameters:

input_size (int) – Number of expected features in the input inp.
hidden_size (int) – Number of features in the hidden/cell states.
bias (bool) – If False, the cell does not use bias terms. Default: True.
kernel_init (Callable) – Initializer for input‐to‐hidden weights (default: nn.init.xavier_uniform_).
recurrent_kernel_init (Callable) – Initializer for hidden‐to‐hidden weights (default: nn.init.xavier_uniform_).
bias_init (Callable) – Initializer for input biases (default: nn.init.zeros_).
recurrent_bias_init (Callable) – Initializer for hidden biases (default: nn.init.zeros_).
device (torch.device, optional) – Device for parameters.
dtype (torch.dtype, optional) – Data type for parameters.

Inputs:

inp (Tensor): shape (batch, input_size) or (input_size,).
state (Tuple[Tensor, Tensor], optional): Previous (h, c) each of shape (batch, hidden_size) or (hidden_size,). If not provided, defaults to zeros.

Outputs:

new_h (Tensor): Next hidden state, same shape as h.
new_c (Tensor): Next cell state, same shape as c.

weight_ih

Input‐to‐hidden weights for content, input & forget gates, shape (3*hidden_size, input_size).

Type:: Tensor

weight_hh

Hidden‐to‐hidden weights for input & forget gates, shape (2*hidden_size, hidden_size).

Type:: Tensor

bias_ih

Input biases for input & forget gates, shape (2*hidden_size,).

Type:: Tensor

bias_hh

Hidden biases for input & forget gates, shape (2*hidden_size,).

Type:: Tensor

Note

RANs omit a separate output gate, applying a simple tanh to the updated cell state to produce the hidden state.

Examples::

>>> cell = RANCell(16, 32)
>>> x = torch.randn(5, 16)           # batch=5, input_size=16
>>> h0 = torch.zeros(5, 32)          # batch=5, hidden_size=32
>>> c0 = torch.zeros(5, 32)
>>> h1, c1 = cell(x, (h0, c0))

__init__(input_size, hidden_size, bias=True, kernel_init=<function xavier_uniform_>, recurrent_kernel_init=<function xavier_uniform_>, bias_init=<function zeros_>, recurrent_bias_init=<function zeros_>, device=None, dtype=None)

Initialize internal Module state, shared by both nn.Module and ScriptModule.

Parameters:

kernel_init (Callable)
recurrent_kernel_init (Callable)
bias_init (Callable)
recurrent_bias_init (Callable)
device (device | None)
dtype (dtype | None)

Methods

`__init__`(input_size, hidden_size[, bias, ...])	Initialize internal Module state, shared by both nn.Module and ScriptModule.
`add_module`(name, module)	Add a child module to the current module.
`apply`(fn)	Apply `fn` recursively to every submodule (as returned by `.children()`) as well as self.
`bfloat16`()	Casts all floating point parameters and buffers to `bfloat16` datatype.
`buffers`([recurse])	Return an iterator over module buffers.
`children`()	Return an iterator over immediate children modules.
`compile`(args, *kwargs)	Compile this Module's forward using `torch.compile()`.
`cpu`()	Move all model parameters and buffers to the CPU.
`cuda`([device])	Move all model parameters and buffers to the GPU.
`double`()	Casts all floating point parameters and buffers to `double` datatype.
`eval`()	Set the module in evaluation mode.
`extra_repr`()	Return the extra representation of the module.
`float`()	Casts all floating point parameters and buffers to `float` datatype.
`forward`(inp[, state])	Run one step of the recurrent cell.
`get_buffer`(target)	Return the buffer given by `target` if it exists, otherwise throw an error.
`get_extra_state`()	Return any extra state to include in the module's state_dict.
`get_parameter`(target)	Return the parameter given by `target` if it exists, otherwise throw an error.
`get_submodule`(target)	Return the submodule given by `target` if it exists, otherwise throw an error.
`half`()	Casts all floating point parameters and buffers to `half` datatype.
`init_weights`()
`ipu`([device])	Move all model parameters and buffers to the IPU.
`load_state_dict`(state_dict[, strict, assign])	Copy parameters and buffers from `state_dict` into this module and its descendants.
`modules`()	Return an iterator over all modules in the network.
`mtia`([device])	Move all model parameters and buffers to the MTIA.
`named_buffers`([prefix, recurse, ...])	Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
`named_children`()	Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
`named_modules`([memo, prefix, remove_duplicate])	Return 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, ...])	Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
`parameters`([recurse])	Return an iterator over module parameters.
`register_backward_hook`(hook)	Register a backward hook on the module.
`register_buffer`(name, tensor[, persistent])	Add a buffer to the module.
`register_forward_hook`(hook, *[, prepend, ...])	Register a forward hook on the module.
`register_forward_pre_hook`(hook, *[, ...])	Register a forward pre-hook on the module.
`register_full_backward_hook`(hook[, prepend])	Register a backward hook on the module.
`register_full_backward_pre_hook`(hook[, prepend])	Register a backward pre-hook on the module.
`register_load_state_dict_post_hook`(hook)	Register a post-hook to be run after module's `load_state_dict()` is called.
`register_load_state_dict_pre_hook`(hook)	Register a pre-hook to be run before module's `load_state_dict()` is called.
`register_module`(name, module)	Alias for `add_module()`.
`register_parameter`(name, param)	Add a parameter to the module.
`register_state_dict_post_hook`(hook)	Register a post-hook for the `state_dict()` method.
`register_state_dict_pre_hook`(hook)	Register a pre-hook for the `state_dict()` method.
`requires_grad_`([requires_grad])	Change if autograd should record operations on parameters in this module.
`set_extra_state`(state)	Set extra state contained in the loaded state_dict.
`set_submodule`(target, module[, strict])	Set the submodule given by `target` if it exists, otherwise throw an error.
`share_memory`()	See `torch.Tensor.share_memory_()`.
`state_dict`(*args[, destination, prefix, ...])	Return a dictionary containing references to the whole state of the module.
`to`(args, *kwargs)	Move and/or cast the parameters and buffers.
`to_empty`(*, device[, recurse])	Move the parameters and buffers to the specified device without copying storage.
`train`([mode])	Set the module in training mode.
`type`(dst_type)	Casts all parameters and buffers to `dst_type`.
`uses_double_state`()	Return True if forward returns (h, c), else just h.
`xpu`([device])	Move all model parameters and buffers to the XPU.
`zero_grad`([set_to_none])	Reset gradients of all model parameters.

Attributes

`T_destination`
`call_super_init`
`dump_patches`
`weight_ih`
`weight_hh`
`bias_ih`
`bias_hh`
`training`