torchrecurrent.UnICORNN#

class torchrecurrent.UnICORNN(input_size, hidden_size, num_layers=1, dropout=0.0, batch_first=False, **kwargs)[source]#

Multi-layer Undamped Independent Controlled Oscillatory RNN (UnICORNN).

[arXiv]

Each layer consists of a UnICORNNCell, which maintains two coupled state vectors, the hidden state \(h(t)\) and the control state \(z(t)\), updated as:

\[\begin{split}\begin{aligned} h(t) &= h(t-1) + \Delta t \, \hat{\sigma}(w_{ch}) \circ z(t), \\ z(t) &= z(t-1) - \Delta t \, \hat{\sigma}(w_{ch}) \circ \Bigl[\sigma(W_{hh} h(t-1) + W_{ih} x(t) + b_{ih}) + \alpha h(t-1)\Bigr], \end{aligned}\end{split}\]

where \(\Delta t\) is the integration step dt, \(\alpha\) is a leakage constant, \(\sigma\) is the sigmoid, and \(\circ\) is the elementwise product.

Parameters:

  • input_size – Number of expected features in the input x.

  • hidden_size – Number of features in the hidden state h (and control z).

  • num_layers – Number of stacked recurrent layers. Default: 1

  • dropout – If non-zero, adds dropout after each layer (except the last). Default: 0

  • batch_first – If True, inputs and outputs are in (batch, seq, feature) format instead of (seq, batch, feature). Default: False

  • bias – If False, disables input bias b_{ih}. Default: True

  • recurrent_bias – If False, disables hidden bias b_{hh}. Default: True

  • kernel_init – Initializer for W_{ih}. Default: torch.nn.init.xavier_uniform_()

  • recurrent_kernel_init – Initializer for W_{hh}. Default: torch.nn.init.xavier_uniform_()

  • control_kernel_init – Initializer for w_{ch}. Default: torch.nn.init.normal_()

  • bias_init – Initializer for b_{ih} when bias=True. Default: torch.nn.init.zeros_()

  • recurrent_bias_init – Initializer for b_{hh} when recurrent_bias=True. Default: torch.nn.init.zeros_()

  • dt – Integration step \(\Delta t\). Default: 1.0

  • alpha – Leakage coefficient \(\alpha\). Default: 0.0

  • device – Desired device of parameters.

  • dtype – Desired floating point type of parameters.

Inputs: input, (h_0, z_0)

  • input: tensor of shape (L, H_in) for unbatched input, (L, N, H_in) when batch_first=False, or (N, L, H_in) when batch_first=True containing input sequence features.

  • h_0: tensor of shape (num_layers, H_out) for unbatched input or (num_layers, N, H_out) containing the initial hidden state. Defaults to zeros if not provided.

  • z_0: tensor of the same shape as h_0, containing the initial control state. Defaults to zeros if not provided.

Where:

\[\begin{split}\begin{aligned} N &= \text{batch size} \\ L &= \text{sequence length} \\ H_{in} &= \text{input size} \\ H_{out} &= \text{hidden size} \end{aligned}\end{split}\]
Outputs: output, (h_n, z_n)

  • output: tensor of shape (L, H_out) for unbatched input, (L, N, H_out) when batch_first=False, or (N, L, H_out) when batch_first=True containing the hidden states from the last layer at each timestep.

  • h_n: final hidden state for each layer, shape (num_layers, H_out) (unbatched) or (num_layers, N, H_out).

  • z_n: final control state for each layer, same shape as h_n.

cells.{k}.weight_ih

input–hidden weights of the \(k\)-th layer, shape (hidden_size, input_size) for k=0, otherwise (hidden_size, hidden_size).

cells.{k}.weight_hh

hidden–hidden weights of the \(k\)-th layer, shape (hidden_size, hidden_size).

cells.{k}.weight_ch

control weights of the \(k\)-th layer, shape (hidden_size,).

cells.{k}.bias_ih

input bias of the \(k\)-th layer, shape (hidden_size,) if bias=True.

cells.{k}.bias_hh

hidden bias of the \(k\)-th layer, shape (hidden_size,) if recurrent_bias=True.

See also

UnICORNNCell

Examples:

>>> rnn = UnICORNN(10, 20, num_layers=2, dt=0.5, alpha=0.1)
>>> x = torch.randn(5, 3, 10)    # (seq_len, batch, input_size)
>>> h0 = torch.zeros(2, 3, 20)
>>> z0 = torch.zeros(2, 3, 20)
>>> out, (hn, zn) = rnn(x, (h0, z0))
__init__(input_size, hidden_size, num_layers=1, dropout=0.0, batch_first=False, **kwargs)[source]#

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

Methods

__init__(input_size, hidden_size[, ...])

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])

Define the computation performed at every call.

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.

initialize_cells(cell_class, **kwargs)

Helper method to initialize cells for the derived recurrent layer class.

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.

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

input_size

hidden_size

bias

dropout

batch_first

training