oneflow.nn.LSTM

class oneflow.nn.LSTM(*args, **kwargs)

Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence.

For each element in the input sequence, each layer computes the following

function:

\[\begin{split}\begin{array}{ll} \\ i_t = \sigma(W_{ii} x_t + b_{ii} + W_{hi} h_{t-1} + b_{hi}) \\ f_t = \sigma(W_{if} x_t + b_{if} + W_{hf} h_{t-1} + b_{hf}) \\ g_t = \tanh(W_{ig} x_t + b_{ig} + W_{hg} h_{t-1} + b_{hg}) \\ o_t = \sigma(W_{io} x_t + b_{io} + W_{ho} h_{t-1} + b_{ho}) \\ c_t = f_t \odot c_{t-1} + i_t \odot g_t \\ h_t = o_t \odot \tanh(c_t) \\ \end{array}\end{split}\]

where \(h_t\) is the hidden state at time t, \(c_t\) is the cell state at time t, \(x_t\) is the input at time t, \(h_{t-1}\) is the hidden state of the layer at time t-1 or the initial hidden state at time 0, and \(i_t\), \(f_t\), \(g_t\), \(o_t\) are the input, forget, cell, and output gates, respectively. \(\sigma\) is the sigmoid function, and \(\odot\) is the Hadamard product.

In a multilayer LSTM, the input \(x^{(l)}_t\) of the \(l\) -th layer (\(l >= 2\)) is the hidden state \(h^{(l-1)}_t\) of the previous layer multiplied by dropout \(\delta^{(l-1)}_t\) where each \(\delta^{(l-1)}_t\) is a Bernoulli random variable which is \(0\) with probability dropout.

If proj_size > 0 is specified, LSTM with projections will be used. This changes the LSTM cell in the following way. First, the dimension of \(h_t\) will be changed from hidden_size to proj_size (dimensions of \(W_{hi}\) will be changed accordingly). Second, the output hidden state of each layer will be multiplied by a learnable projection matrix: \(h_t = W_{hr}h_t\). Note that as a consequence of this, the output of LSTM network will be of different shape as well. See Inputs/Outputs sections below for exact dimensions of all variables. You can find more details in https://arxiv.org/abs/1402.1128.

The interface is consistent with PyTorch. The documentation is referenced from: https://pytorch.org/docs/1.10/_modules/torch/nn/modules/rnn.html#LSTM.

Parameters

  • input_size – The number of expected features in the input x

  • hidden_size – The number of features in the hidden state h

  • num_layers – Number of recurrent layers. E.g., setting num_layers=2 would mean stacking two LSTMs together to form a stacked LSTM, with the second LSTM taking in outputs of the first LSTM and computing the final results. Default: 1

  • bias – If False, then the layer does not use bias weights b_ih and b_hh. Default: True

  • batch_first – If True, then the input and output tensors are provided as (batch, seq, feature) instead of (seq, batch, feature). Note that this does not apply to hidden or cell states. See the Inputs/Outputs sections below for details. Default: False

  • dropout – If non-zero, introduces a Dropout layer on the outputs of each LSTM layer except the last layer, with dropout probability equal to dropout. Default: 0

  • bidirectional – If True, becomes a bidirectional LSTM. Default: False

  • proj_size – If > 0, will use LSTM with projections of corresponding size. Default: 0

Inputs: input, (h_0, c_0)

  • input: tensor of shape \((L, N, H_{in})\) when batch_first=False or \((N, L, H_{in})\) when batch_first=True containing the features of the input sequence.

  • h_0: tensor of shape \((D * \text{num\_layers}, N, H_{out})\) containing the initial hidden state for each element in the batch. Defaults to zeros if (h_0, c_0) is not provided.

  • c_0: tensor of shape \((D * \text{num\_layers}, N, H_{cell})\) containing the initial cell state for each element in the batch. Defaults to zeros if (h_0, c_0) is not provided.

where:

\[\begin{split}\begin{aligned} N ={} & \text{batch size} \\ L ={} & \text{sequence length} \\ D ={} & 2 \text{ if bidirectional=True otherwise } 1 \\ H_{in} ={} & \text{input\_size} \\ H_{cell} ={} & \text{hidden\_size} \\ H_{out} ={} & \text{proj\_size if } \text{proj\_size}>0 \text{ otherwise hidden\_size} \\ \end{aligned}\end{split}\]
Outputs: output, (h_n, c_n)

  • output: tensor of shape \((L, N, D * H_{out})\) when batch_first=False or \((N, L, D * H_{out})\) when batch_first=True containing the output features (h_t) from the last layer of the LSTM, for each t.

  • h_n: tensor of shape \((D * \text{num\_layers}, N, H_{out})\) containing the final hidden state for each element in the batch.

  • c_n: tensor of shape \((D * \text{num\_layers}, N, H_{cell})\) containing the final cell state for each element in the batch.

weight_ih_l[k]

the learnable input-hidden weights of the \(\text{k}^{th}\) layer (W_ii|W_if|W_ig|W_io), of shape (4*hidden_size, input_size) for k = 0. Otherwise, the shape is (4*hidden_size, num_directions * hidden_size)

weight_hh_l[k]

the learnable hidden-hidden weights of the \(\text{k}^{th}\) layer (W_hi|W_hf|W_hg|W_ho), of shape (4*hidden_size, hidden_size). If proj_size > 0 was specified, the shape will be (4*hidden_size, proj_size).

bias_ih_l[k]

the learnable input-hidden bias of the \(\text{k}^{th}\) layer (b_ii|b_if|b_ig|b_io), of shape (4*hidden_size)

bias_hh_l[k]

the learnable hidden-hidden bias of the \(\text{k}^{th}\) layer (b_hi|b_hf|b_hg|b_ho), of shape (4*hidden_size)

weight_hr_l[k]

the learnable projection weights of the \(\text{k}^{th}\) layer of shape (proj_size, hidden_size). Only present when proj_size > 0 was specified.

Note

All the weights and biases are initialized from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where \(k = \frac{1}{\text{hidden\_size}}\)

Note

For bidirectional LSTMs, forward and backward are directions 0 and 1 respectively. Example of splitting the output layers when batch_first=False: output.view(seq_len, batch, num_directions, hidden_size).

For example:

>>> import oneflow as flow
>>> import numpy as np
>>> rnn = flow.nn.LSTM(10, 20, 2)
>>> input = flow.tensor(np.random.randn(5, 3, 10), dtype=flow.float32)
>>> h0 = flow.tensor(np.random.randn(2, 3, 20), dtype=flow.float32)
>>> c0 = flow.tensor(np.random.randn(2, 3, 20), dtype=flow.float32)
>>> output, (hn, cn) = rnn(input, (h0, c0))
>>> output.size()
oneflow.Size([5, 3, 20])
__init__(*args, **kwargs)

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__(*args, **kwargs)

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__(attr, 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.

check_forward_args(input, hidden, batch_sizes)

check_hidden_size(hx, expected_hidden_size)

check_input(input, batch_sizes)

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(input[, hx])

get_expected_cell_size(input, batch_sizes)

get_expected_hidden_size(input, batch_sizes)

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.

permute_hidden(hx, permutation)

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

all_weights