torchrecurrent.FastGRNN

class torchrecurrent.FastGRNN(input_size, hidden_size, num_layers=1, dropout=0.0, batch_first=False, **kwargs)

Multi-layer FastGRNN.

[arXiv]

Each layer is a FastGRNNCell, a gated recurrent unit with two scalar parameters \(\zeta\) and \(\nu\) controlling the tradeoff between the candidate state and the previous hidden state:

\[\begin{split}\begin{aligned} z(t) &= \sigma(W_{ih} x(t) + b_{ih}^z + W_{hh} h(t-1) + b_{hh}^z), \\ \tilde{h}(t) &= \tanh(W_{ih} x(t) + b_{ih}^h + W_{hh} h(t-1) + b_{hh}^h), \\ h(t) &= \bigl[\sigma(\zeta) (1 - z(t)) + \sigma(\nu)\bigr] \circ \tilde{h}(t) + z(t) \circ h(t-1), \end{aligned}\end{split}\]

where \(\sigma\) is the sigmoid and \(\circ\) is elementwise multiplication.

Parameters:

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

• hidden_size – Number of features in the hidden state.

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

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

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

• bias – If False, disables input biases. Default: True

• recurrent_bias – If False, disables recurrent biases. Default: True

• nonlinearity – Activation for the gate \(z\). Default: torch.sigmoid()

• 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_()

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

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

• zeta_init – Initial value for scalar \(\zeta\). Default: 3.0

• nu_init – Initial value for scalar \(\nu\). Default: -3.0

• device – Desired device of parameters.

• dtype – Desired floating point type of parameters.

Inputs: input, h_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.

• h_0: tensor of shape (num_layers, H_out) for unbatched input, or (num_layers, N, H_out) when batched. Defaults to zeros.

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

• 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 hidden states from the last layer.

• h_n: tensor of shape (num_layers, H_out) (unbatched) or (num_layers, N, H_out) with the final hidden state.

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}.bias_ih

input biases of the \(k\)-th layer, shape (2*hidden_size,) if bias=True.

cells.{k}.bias_hh

hidden biases of the \(k\)-th layer, shape (2*hidden_size,) if recurrent_bias=True.

cells.{k}.zeta

scalar parameter \(\zeta\), shape (1,).

cells.{k}.nu

scalar parameter \(\nu\), shape (1,).

See also

FastGRNNCell

Examples:

>>> rnn = FastGRNN(10, 20, num_layers=2)
>>> x = torch.randn(5, 3, 10)     # (seq_len, batch, input_size)
>>> h0 = torch.zeros(2, 3, 20)
>>> out, hn = rnn(x, h0)

__init__(input_size, hidden_size, num_layers=1, dropout=0.0, batch_first=False, **kwargs)

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