Source code for torch.nn.modules.rnn

import math
import torch

from .module import Module
from ..parameter import Parameter
from ..utils.rnn import PackedSequence


class RNNBase(Module):

    def __init__(self, mode, input_size, hidden_size,
                 num_layers=1, bias=True, batch_first=False,
                 dropout=0, bidirectional=False):
        super(RNNBase, self).__init__()
        self.mode = mode
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.bias = bias
        self.batch_first = batch_first
        self.dropout = dropout
        self.dropout_state = {}
        self.bidirectional = bidirectional
        num_directions = 2 if bidirectional else 1

        self._all_weights = []
        for layer in range(num_layers):
            for direction in range(num_directions):
                layer_input_size = input_size if layer == 0 else hidden_size * num_directions
                if mode == 'LSTM':
                    gate_size = 4 * hidden_size
                elif mode == 'GRU':
                    gate_size = 3 * hidden_size
                else:
                    gate_size = hidden_size

                w_ih = Parameter(torch.Tensor(gate_size, layer_input_size))
                w_hh = Parameter(torch.Tensor(gate_size, hidden_size))
                b_ih = Parameter(torch.Tensor(gate_size))
                b_hh = Parameter(torch.Tensor(gate_size))

                suffix = '_reverse' if direction == 1 else ''
                weights = ['weight_ih_l{}{}', 'weight_hh_l{}{}', 'bias_ih_l{}{}', 'bias_hh_l{}{}']
                weights = [x.format(layer, suffix) for x in weights]
                setattr(self, weights[0], w_ih)
                setattr(self, weights[1], w_hh)
                if bias:
                    setattr(self, weights[2], b_ih)
                    setattr(self, weights[3], b_hh)
                    self._all_weights += [weights]
                else:
                    self._all_weights += [weights[:2]]

        self.reset_parameters()

    def reset_parameters(self):
        stdv = 1.0 / math.sqrt(self.hidden_size)
        for weight in self.parameters():
            weight.data.uniform_(-stdv, stdv)

    def forward(self, input, hx=None):
        is_packed = isinstance(input, PackedSequence)
        if is_packed:
            input, batch_sizes = input
            max_batch_size = batch_sizes[0]
        else:
            batch_sizes = None
            max_batch_size = input.size(0) if self.batch_first else input.size(1)

        if hx is None:
            num_directions = 2 if self.bidirectional else 1
            hx = torch.autograd.Variable(input.data.new(self.num_layers *
                                                        num_directions,
                                                        max_batch_size,
                                                        self.hidden_size).zero_())
            if self.mode == 'LSTM':
                hx = (hx, hx)

        func = self._backend.RNN(
            self.mode,
            self.input_size,
            self.hidden_size,
            num_layers=self.num_layers,
            batch_first=self.batch_first,
            dropout=self.dropout,
            train=self.training,
            bidirectional=self.bidirectional,
            batch_sizes=batch_sizes,
            dropout_state=self.dropout_state
        )
        output, hidden = func(input, self.all_weights, hx)
        if is_packed:
            output = PackedSequence(output, batch_sizes)
        return output, hidden

    def __repr__(self):
        s = '{name}({input_size}, {hidden_size}'
        if self.num_layers != 1:
            s += ', num_layers={num_layers}'
        if self.bias is not True:
            s += ', bias={bias}'
        if self.batch_first is not False:
            s += ', batch_first={batch_first}'
        if self.dropout != 0:
            s += ', dropout={dropout}'
        if self.bidirectional is not False:
            s += ', bidirectional={bidirectional}'
        s += ')'
        return s.format(name=self.__class__.__name__, **self.__dict__)

    def __setstate__(self, d):
        self.__dict__.update(d)
        if 'all_weights' in d:
            self._all_weights = d['all_weights']
        if isinstance(self._all_weights[0][0], str):
            return
        num_layers = self.num_layers
        num_directions = 2 if self.bidirectional else 1
        self._all_weights = []
        for layer in range(num_layers):
            for direction in range(num_directions):
                suffix = '_reverse' if direction == 1 else ''
                weights = ['weight_ih_l{}{}', 'weight_hh_l{}{}', 'bias_ih_l{}{}', 'bias_hh_l{}{}']
                weights = [x.format(layer, suffix) for x in weights]
                if self.bias:
                    self._all_weights += [weights]
                else:
                    self._all_weights += [weights[:2]]

    @property
    def all_weights(self):
        return [[getattr(self, weight) for weight in weights] for weights in self._all_weights]


[docs]class RNN(RNNBase): r"""Applies a multi-layer Elman RNN with tanh or ReLU non-linearity to an input sequence. For each element in the input sequence, each layer computes the following function: .. math:: h_t = \tanh(w_{ih} * x_t + b_{ih} + w_{hh} * h_{(t-1)} + b_{hh}) where :math:`h_t` is the hidden state at time `t`, and :math:`x_t` is the hidden state of the previous layer at time `t` or :math:`input_t` for the first layer. If nonlinearity='relu', then `ReLU` is used instead of `tanh`. Args: 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. nonlinearity: The non-linearity to use ['tanh'|'relu']. Default: 'tanh' 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) dropout: If non-zero, introduces a dropout layer on the outputs of each RNN layer except the last layer bidirectional: If True, becomes a bidirectional RNN. Default: False Inputs: input, h_0 - **input** (seq_len, batch, input_size): tensor containing the features of the input sequence. The input can also be a packed variable length sequence. See :func:`torch.nn.utils.rnn.pack_padded_sequence` for details. - **h_0** (num_layers * num_directions, batch, hidden_size): tensor containing the initial hidden state for each element in the batch. Outputs: output, h_n - **output** (seq_len, batch, hidden_size * num_directions): tensor containing the output features (h_k) from the last layer of the RNN, for each k. If a :class:`torch.nn.utils.rnn.PackedSequence` has been given as the input, the output will also be a packed sequence. - **h_n** (num_layers * num_directions, batch, hidden_size): tensor containing the hidden state for k=seq_len. Attributes: weight_ih_l[k]: the learnable input-hidden weights of the k-th layer, of shape `(input_size x hidden_size)` weight_hh_l[k]: the learnable hidden-hidden weights of the k-th layer, of shape `(hidden_size x hidden_size)` bias_ih_l[k]: the learnable input-hidden bias of the k-th layer, of shape `(hidden_size)` bias_hh_l[k]: the learnable hidden-hidden bias of the k-th layer, of shape `(hidden_size)` Examples:: >>> rnn = nn.RNN(10, 20, 2) >>> input = Variable(torch.randn(5, 3, 10)) >>> h0 = Variable(torch.randn(2, 3, 20)) >>> output, hn = rnn(input, h0) """ def __init__(self, *args, **kwargs): if 'nonlinearity' in kwargs: if kwargs['nonlinearity'] == 'tanh': mode = 'RNN_TANH' elif kwargs['nonlinearity'] == 'relu': mode = 'RNN_RELU' else: raise ValueError("Unknown nonlinearity '{}'".format( kwargs['nonlinearity'])) del kwargs['nonlinearity'] else: mode = 'RNN_TANH' super(RNN, self).__init__(mode, *args, **kwargs)
[docs]class LSTM(RNNBase): r"""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: .. math:: \begin{array}{ll} i_t = sigmoid(W_{ii} x_t + b_{ii} + W_{hi} h_{(t-1)} + b_{hi}) \\ f_t = sigmoid(W_{if} x_t + b_{if} + W_{hf} h_{(t-1)} + b_{hf}) \\ g_t = \tanh(W_{ig} x_t + b_{ig} + W_{hc} h_{(t-1)} + b_{hg}) \\ o_t = sigmoid(W_{io} x_t + b_{io} + W_{ho} h_{(t-1)} + b_{ho}) \\ c_t = f_t * c_{(t-1)} + i_t * g_t \\ h_t = o_t * \tanh(c_t) \end{array} where :math:`h_t` is the hidden state at time `t`, :math:`c_t` is the cell state at time `t`, :math:`x_t` is the hidden state of the previous layer at time `t` or :math:`input_t` for the first layer, and :math:`i_t`, :math:`f_t`, :math:`g_t`, :math:`o_t` are the input, forget, cell, and out gates, respectively. Args: 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. 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) dropout: If non-zero, introduces a dropout layer on the outputs of each RNN layer except the last layer bidirectional: If True, becomes a bidirectional RNN. Default: False Inputs: input, (h_0, c_0) - **input** (seq_len, batch, input_size): tensor containing the features of the input sequence. The input can also be a packed variable length sequence. See :func:`torch.nn.utils.rnn.pack_padded_sequence` for details. - **h_0** (num_layers \* num_directions, batch, hidden_size): tensor containing the initial hidden state for each element in the batch. - **c_0** (num_layers \* num_directions, batch, hidden_size): tensor containing the initial cell state for each element in the batch. Outputs: output, (h_n, c_n) - **output** (seq_len, batch, hidden_size * num_directions): tensor containing the output features `(h_t)` from the last layer of the RNN, for each t. If a :class:`torch.nn.utils.rnn.PackedSequence` has been given as the input, the output will also be a packed sequence. - **h_n** (num_layers * num_directions, batch, hidden_size): tensor containing the hidden state for t=seq_len - **c_n** (num_layers * num_directions, batch, hidden_size): tensor containing the cell state for t=seq_len Attributes: weight_ih_l[k] : the learnable input-hidden weights of the k-th layer `(W_ii|W_if|W_ig|W_io)`, of shape `(input_size x 4*hidden_size)` weight_hh_l[k] : the learnable hidden-hidden weights of the k-th layer `(W_hi|W_hf|W_hg|W_ho)`, of shape `(hidden_size x 4*hidden_size)` bias_ih_l[k] : the learnable input-hidden bias of the 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 k-th layer `(b_hi|b_hf|b_hg|b_ho)`, of shape `(4*hidden_size)` Examples:: >>> rnn = nn.LSTM(10, 20, 2) >>> input = Variable(torch.randn(5, 3, 10)) >>> h0 = Variable(torch.randn(2, 3, 20)) >>> c0 = Variable(torch.randn(2, 3, 20)) >>> output, hn = rnn(input, (h0, c0)) """ def __init__(self, *args, **kwargs): super(LSTM, self).__init__('LSTM', *args, **kwargs)
[docs]class GRU(RNNBase): r"""Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence. For each element in the input sequence, each layer computes the following function: .. math:: \begin{array}{ll} r_t = sigmoid(W_{ir} x_t + b_{ir} + W_{hr} h_{(t-1)} + b_{hr}) \\ i_t = sigmoid(W_{ii} x_t + b_{ii} + W_hi h_{(t-1)} + b_{hi}) \\ n_t = \tanh(W_{in} x_t + b_{in} + r_t * (W_{hn} h_{(t-1)}+ b_{hn})) \\ h_t = (1 - i_t) * n_t + i_t * h_{(t-1)} \\ \end{array} where :math:`h_t` is the hidden state at time `t`, :math:`x_t` is the hidden state of the previous layer at time `t` or :math:`input_t` for the first layer, and :math:`r_t`, :math:`i_t`, :math:`n_t` are the reset, input, and new gates, respectively. Args: 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. 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) dropout: If non-zero, introduces a dropout layer on the outputs of each RNN layer except the last layer bidirectional: If True, becomes a bidirectional RNN. Default: False Inputs: input, h_0 - **input** (seq_len, batch, input_size): tensor containing the features of the input sequence. The input can also be a packed variable length sequence. See :func:`torch.nn.utils.rnn.pack_padded_sequence` for details. - **h_0** (num_layers * num_directions, batch, hidden_size): tensor containing the initial hidden state for each element in the batch. Outputs: output, h_n - **output** (seq_len, batch, hidden_size * num_directions): tensor containing the output features h_t from the last layer of the RNN, for each t. If a :class:`torch.nn.utils.rnn.PackedSequence` has been given as the input, the output will also be a packed sequence. - **h_n** (num_layers * num_directions, batch, hidden_size): tensor containing the hidden state for t=seq_len Attributes: weight_ih_l[k] : the learnable input-hidden weights of the k-th layer (W_ir|W_ii|W_in), of shape `(input_size x 3*hidden_size)` weight_hh_l[k] : the learnable hidden-hidden weights of the k-th layer (W_hr|W_hi|W_hn), of shape `(hidden_size x 3*hidden_size)` bias_ih_l[k] : the learnable input-hidden bias of the k-th layer (b_ir|b_ii|b_in), of shape `(3*hidden_size)` bias_hh_l[k] : the learnable hidden-hidden bias of the k-th layer (W_hr|W_hi|W_hn), of shape `(3*hidden_size)` Examples:: >>> rnn = nn.GRU(10, 20, 2) >>> input = Variable(torch.randn(5, 3, 10)) >>> h0 = Variable(torch.randn(2, 3, 20)) >>> output, hn = rnn(input, h0) """ def __init__(self, *args, **kwargs): super(GRU, self).__init__('GRU', *args, **kwargs)
class RNNCellBase(Module): def __repr__(self): s = '{name}({input_size}, {hidden_size}' if 'bias' in self.__dict__ and self.bias is not True: s += ', bias={bias}' if 'nonlinearity' in self.__dict__ and self.nonlinearity != "tanh": s += ', nonlinearity={nonlinearity}' s += ')' return s.format(name=self.__class__.__name__, **self.__dict__)
[docs]class RNNCell(RNNCellBase): r"""An Elman RNN cell with tanh or ReLU non-linearity. .. math:: h' = \tanh(w_{ih} * x + b_{ih} + w_{hh} * h + b_{hh}) If nonlinearity='relu', then ReLU is used in place of tanh. Args: input_size: The number of expected features in the input x hidden_size: The number of features in the hidden state h bias: If False, then the layer does not use bias weights b_ih and b_hh. Default: True nonlinearity: The non-linearity to use ['tanh'|'relu']. Default: 'tanh' Inputs: input, hidden - **input** (batch, input_size): tensor containing input features - **hidden** (batch, hidden_size): tensor containing the initial hidden state for each element in the batch. Outputs: h' - **h'** (batch, hidden_size): tensor containing the next hidden state for each element in the batch Attributes: weight_ih: the learnable input-hidden weights, of shape `(input_size x hidden_size)` weight_hh: the learnable hidden-hidden weights, of shape `(hidden_size x hidden_size)` bias_ih: the learnable input-hidden bias, of shape `(hidden_size)` bias_hh: the learnable hidden-hidden bias, of shape `(hidden_size)` Examples:: >>> rnn = nn.RNNCell(10, 20) >>> input = Variable(torch.randn(6, 3, 10)) >>> hx = Variable(torch.randn(3, 20)) >>> output = [] >>> for i in range(6): ... hx = rnn(input[i], hx) ... output.append(hx) """ def __init__(self, input_size, hidden_size, bias=True, nonlinearity="tanh"): super(RNNCell, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.bias = bias self.nonlinearity = nonlinearity self.weight_ih = Parameter(torch.Tensor(hidden_size, input_size)) self.weight_hh = Parameter(torch.Tensor(hidden_size, hidden_size)) if bias: self.bias_ih = Parameter(torch.Tensor(hidden_size)) self.bias_hh = Parameter(torch.Tensor(hidden_size)) else: self.register_parameter('bias_ih', None) self.register_parameter('bias_hh', None) self.reset_parameters() def reset_parameters(self): stdv = 1.0 / math.sqrt(self.hidden_size) for weight in self.parameters(): weight.data.uniform_(-stdv, stdv) def forward(self, input, hx): if self.nonlinearity == "tanh": func = self._backend.RNNTanhCell elif self.nonlinearity == "relu": func = self._backend.RNNReLUCell else: raise RuntimeError( "Unknown nonlinearity: {}".format(self.nonlinearity)) return func( input, hx, self.weight_ih, self.weight_hh, self.bias_ih, self.bias_hh, )
[docs]class LSTMCell(RNNCellBase): r"""A long short-term memory (LSTM) cell. .. math:: \begin{array}{ll} i = sigmoid(W_{ii} x + b_{ii} + W_{hi} h + b_{hi}) \\ f = sigmoid(W_{if} x + b_{if} + W_{hf} h + b_{hf}) \\ g = \tanh(W_{ig} x + b_{ig} + W_{hc} h + b_{hg}) \\ o = sigmoid(W_{io} x + b_{io} + W_{ho} h + b_{ho}) \\ c' = f * c + i * g \\ h' = o * \tanh(c_t) \\ \end{array} Args: input_size: The number of expected features in the input x hidden_size: The number of features in the hidden state h bias: If `False`, then the layer does not use bias weights `b_ih` and `b_hh`. Default: True Inputs: input, (h_0, c_0) - **input** (batch, input_size): tensor containing input features - **h_0** (batch, hidden_size): tensor containing the initial hidden state for each element in the batch. - **c_0** (batch. hidden_size): tensor containing the initial cell state for each element in the batch. Outputs: h_1, c_1 - **h_1** (batch, hidden_size): tensor containing the next hidden state for each element in the batch - **c_1** (batch, hidden_size): tensor containing the next cell state for each element in the batch Attributes: weight_ih: the learnable input-hidden weights, of shape `(input_size x hidden_size)` weight_hh: the learnable hidden-hidden weights, of shape `(hidden_size x hidden_size)` bias_ih: the learnable input-hidden bias, of shape `(hidden_size)` bias_hh: the learnable hidden-hidden bias, of shape `(hidden_size)` Examples:: >>> rnn = nn.LSTMCell(10, 20) >>> input = Variable(torch.randn(6, 3, 10)) >>> hx = Variable(torch.randn(3, 20)) >>> cx = Variable(torch.randn(3, 20)) >>> output = [] >>> for i in range(6): ... hx, cx = rnn(input[i], (hx, cx)) ... output.append(hx) """ def __init__(self, input_size, hidden_size, bias=True): super(LSTMCell, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.bias = bias self.weight_ih = Parameter(torch.Tensor(4 * hidden_size, input_size)) self.weight_hh = Parameter(torch.Tensor(4 * hidden_size, hidden_size)) if bias: self.bias_ih = Parameter(torch.Tensor(4 * hidden_size)) self.bias_hh = Parameter(torch.Tensor(4 * hidden_size)) else: self.register_parameter('bias_ih', None) self.register_parameter('bias_hh', None) self.reset_parameters() def reset_parameters(self): stdv = 1.0 / math.sqrt(self.hidden_size) for weight in self.parameters(): weight.data.uniform_(-stdv, stdv) def forward(self, input, hx): return self._backend.LSTMCell( input, hx, self.weight_ih, self.weight_hh, self.bias_ih, self.bias_hh, )
[docs]class GRUCell(RNNCellBase): r"""A gated recurrent unit (GRU) cell .. math:: \begin{array}{ll} r = sigmoid(W_{ir} x + b_{ir} + W_{hr} h + b_{hr}) \\ i = sigmoid(W_{ii} x + b_{ii} + W_{hi} h + b_{hi}) \\ n = \tanh(W_{in} x + b_{in} + r * (W_{hn} h + b_{hn})) \\ h' = (1 - i) * n + i * h \end{array} Args: input_size: The number of expected features in the input x hidden_size: The number of features in the hidden state h bias: If `False`, then the layer does not use bias weights `b_ih` and `b_hh`. Default: `True` Inputs: input, hidden - **input** (batch, input_size): tensor containing input features - **hidden** (batch, hidden_size): tensor containing the initial hidden state for each element in the batch. Outputs: h' - **h'**: (batch, hidden_size): tensor containing the next hidden state for each element in the batch Attributes: weight_ih: the learnable input-hidden weights, of shape `(input_size x hidden_size)` weight_hh: the learnable hidden-hidden weights, of shape `(hidden_size x hidden_size)` bias_ih: the learnable input-hidden bias, of shape `(hidden_size)` bias_hh: the learnable hidden-hidden bias, of shape `(hidden_size)` Examples:: >>> rnn = nn.GRUCell(10, 20) >>> input = Variable(torch.randn(6, 3, 10)) >>> hx = Variable(torch.randn(3, 20)) >>> output = [] >>> for i in range(6): ... hx = rnn(input[i], hx) ... output.append(hx) """ def __init__(self, input_size, hidden_size, bias=True): super(GRUCell, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.bias = bias self.weight_ih = Parameter(torch.Tensor(3 * hidden_size, input_size)) self.weight_hh = Parameter(torch.Tensor(3 * hidden_size, hidden_size)) if bias: self.bias_ih = Parameter(torch.Tensor(3 * hidden_size)) self.bias_hh = Parameter(torch.Tensor(3 * hidden_size)) else: self.register_parameter('bias_ih', None) self.register_parameter('bias_hh', None) self.reset_parameters() def reset_parameters(self): stdv = 1.0 / math.sqrt(self.hidden_size) for weight in self.parameters(): weight.data.uniform_(-stdv, stdv) def forward(self, input, hx): return self._backend.GRUCell( input, hx, self.weight_ih, self.weight_hh, self.bias_ih, self.bias_hh, )