import math import torch import warnings import itertools import numbers from .module import Module from ..parameter import Parameter from ..utils.rnn import PackedSequence from .. import init _VF = torch._C._VariableFunctions _rnn_impls = { 'LSTM': _VF.lstm, 'GRU': _VF.gru, 'RNN_TANH': _VF.rnn_tanh, 'RNN_RELU': _VF.rnn_relu, } 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.bidirectional = bidirectional num_directions = 2 if bidirectional else 1 if not isinstance(dropout, numbers.Number) or not 0 <= dropout <= 1 or \ isinstance(dropout, bool): raise ValueError("dropout should be a number in range [0, 1] " "representing the probability of an element being " "zeroed") if dropout > 0 and num_layers == 1: warnings.warn("dropout option adds dropout after all but last " "recurrent layer, so non-zero dropout expects " "num_layers greater than 1, but got dropout={} and " "num_layers={}".format(dropout, num_layers)) if mode == 'LSTM': gate_size = 4 * hidden_size elif mode == 'GRU': gate_size = 3 * hidden_size elif mode == 'RNN_TANH': gate_size = hidden_size elif mode == 'RNN_RELU': gate_size = hidden_size else: raise ValueError("Unrecognized RNN mode: " + mode) 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 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)) layer_params = (w_ih, w_hh, b_ih, b_hh) suffix = '_reverse' if direction == 1 else '' param_names = ['weight_ih_l{}{}', 'weight_hh_l{}{}'] if bias: param_names += ['bias_ih_l{}{}', 'bias_hh_l{}{}'] param_names = [x.format(layer, suffix) for x in param_names] for name, param in zip(param_names, layer_params): setattr(self, name, param) self._all_weights.append(param_names) self.flatten_parameters() self.reset_parameters() def flatten_parameters(self): """Resets parameter data pointer so that they can use faster code paths. Right now, this works only if the module is on the GPU and cuDNN is enabled. Otherwise, it's a no-op. """ any_param = next(self.parameters()).data if not any_param.is_cuda or not torch.backends.cudnn.is_acceptable(any_param): return # If any parameters alias, we fall back to the slower, copying code path. This is # a sufficient check, because overlapping parameter buffers that don't completely # alias would break the assumptions of the uniqueness check in # Module.named_parameters(). all_weights = self._flat_weights unique_data_ptrs = set(p.data_ptr() for p in all_weights) if len(unique_data_ptrs) != len(all_weights): return with torch.cuda.device_of(any_param): import torch.backends.cudnn.rnn as rnn # NB: This is a temporary hack while we still don't have Tensor # bindings for ATen functions with torch.no_grad(): # NB: this is an INPLACE function on all_weights, that's why the # no_grad() is necessary. torch._cudnn_rnn_flatten_weight( all_weights, (4 if self.bias else 2), self.input_size, rnn.get_cudnn_mode(self.mode), self.hidden_size, self.num_layers, self.batch_first, bool(self.bidirectional)) def _apply(self, fn): ret = super(RNNBase, self)._apply(fn) self.flatten_parameters() return ret def reset_parameters(self): stdv = 1.0 / math.sqrt(self.hidden_size) for weight in self.parameters(): init.uniform_(weight, -stdv, stdv) def check_forward_args(self, input, hidden, batch_sizes): is_input_packed = batch_sizes is not None expected_input_dim = 2 if is_input_packed else 3 if input.dim() != expected_input_dim: raise RuntimeError( 'input must have {} dimensions, got {}'.format( expected_input_dim, input.dim())) if self.input_size != input.size(-1): raise RuntimeError( 'input.size(-1) must be equal to input_size. Expected {}, got {}'.format( self.input_size, input.size(-1))) if is_input_packed: mini_batch = int(batch_sizes[0]) else: mini_batch = input.size(0) if self.batch_first else input.size(1) num_directions = 2 if self.bidirectional else 1 expected_hidden_size = (self.num_layers * num_directions, mini_batch, self.hidden_size) def check_hidden_size(hx, expected_hidden_size, msg='Expected hidden size {}, got {}'): if tuple(hx.size()) != expected_hidden_size: raise RuntimeError(msg.format(expected_hidden_size, tuple(hx.size()))) if self.mode == 'LSTM': check_hidden_size(hidden[0], expected_hidden_size, 'Expected hidden[0] size {}, got {}') check_hidden_size(hidden[1], expected_hidden_size, 'Expected hidden[1] size {}, got {}') else: check_hidden_size(hidden, expected_hidden_size) def forward(self, input, hx=None): is_packed = isinstance(input, PackedSequence) if is_packed: input, batch_sizes = input max_batch_size = int(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 = input.new_zeros(self.num_layers * num_directions, max_batch_size, self.hidden_size, requires_grad=False) if self.mode == 'LSTM': hx = (hx, hx) self.check_forward_args(input, hx, batch_sizes) _impl = _rnn_impls[self.mode] if batch_sizes is None: result = _impl(input, hx, self._flat_weights, self.bias, self.num_layers, self.dropout, self.training, self.bidirectional, self.batch_first) else: result = _impl(input, batch_sizes, hx, self._flat_weights, self.bias, self.num_layers, self.dropout, self.training, self.bidirectional) output = result[0] hidden = result[1:] if self.mode == 'LSTM' else result[1] if is_packed: output = PackedSequence(output, batch_sizes) return output, hidden def extra_repr(self): s = '{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}' return s.format(**self.__dict__) def __setstate__(self, d): super(RNNBase, self).__setstate__(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 _flat_weights(self): return list(self._parameters.values()) @property def all_weights(self): return [[getattr(self, weight) for weight in weights] for weights in self._all_weights] class RNN(RNNBase): r"""Applies a multi-layer Elman RNN with :math:`tanh` or :math:`ReLU` non-linearity to an input sequence. For each element in the input sequence, each layer computes the following function: .. math:: h_t = \text{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`, :math:`x_t` is the input at time `t`, and :math:`h_{(t-1)}` is the hidden state of the previous layer at time `t-1` or the initial hidden state at time `0`. If :attr:`nonlinearity` is `'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. E.g., setting ``num_layers=2`` would mean stacking two RNNs together to form a `stacked RNN`, with the second RNN taking in outputs of the first RNN and computing the final results. Default: 1 nonlinearity: The non-linearity to use. Can be either 'tanh' or '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)`. Default: ``False`` dropout: If non-zero, introduces a `Dropout` layer on the outputs of each RNN layer except the last layer, with dropout probability equal to :attr:`dropout`. Default: 0 bidirectional: If ``True``, becomes a bidirectional RNN. Default: ``False`` Inputs: input, h_0 - **input** of shape `(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` or :func:`torch.nn.utils.rnn.pack_sequence` for details. - **h_0** of shape `(num_layers * num_directions, batch, hidden_size)`: tensor containing the initial hidden state for each element in the batch. Defaults to zero if not provided. If the RNN is bidirectional, num_directions should be 2, else it should be 1. Outputs: output, h_n - **output** of shape `(seq_len, batch, num_directions * hidden_size)`: 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. For the unpacked case, the directions can be separated using ``output.view(seq_len, batch, num_directions, hidden_size)``, with forward and backward being direction `0` and `1` respectively. Similarly, the directions can be separated in the packed case. - **h_n** (num_layers * num_directions, batch, hidden_size): tensor containing the hidden state for `k = seq_len`. Like *output*, the layers can be separated using ``h_n.view(num_layers, num_directions, batch, hidden_size)``. Attributes: weight_ih_l[k]: the learnable input-hidden weights of the k-th layer, of shape `(hidden_size * input_size)` for `k = 0`. Otherwise, the shape is `(hidden_size * hidden_size)` weight_hh_l[k]: the learnable hidden-hidden weights of the k-th layer, of shape `(hidden_size * 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)` .. note:: All the weights and biases are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where :math:`k = \frac{1}{\text{hidden\_size}}` .. include:: cudnn_persistent_rnn.rst Examples:: >>> rnn = nn.RNN(10, 20, 2) >>> input = torch.randn(5, 3, 10) >>> h0 = 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) 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 = \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 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 input at time `t`, :math:`h_{(t-1)}` is the hidden state of the layer at time `t-1` or the initial hidden state at time `0`, and :math:`i_t`, :math:`f_t`, :math:`g_t`, :math:`o_t` are the input, forget, cell, and output gates, respectively. :math:`\sigma` is the sigmoid function. In a multilayer LSTM, the input :math:`i^{(l)}_t` of the :math:`l` -th layer (:math:`l >= 2`) is the hidden state :math:`h^{(l-1)}_t` of the previous layer multiplied by dropout :math:`\delta^{(l-1)}_t` where each :math:`\delta^{(l-1)_t}` is a Bernoulli random variable which is :math:`0` with probability :attr:`dropout`. 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. 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). 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 :attr:`dropout`. Default: 0 bidirectional: If ``True``, becomes a bidirectional LSTM. Default: ``False`` Inputs: input, (h_0, c_0) - **input** of shape `(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` or :func:`torch.nn.utils.rnn.pack_sequence` for details. - **h_0** of shape `(num_layers * num_directions, batch, hidden_size)`: tensor containing the initial hidden state for each element in the batch. If the RNN is bidirectional, num_directions should be 2, else it should be 1. - **c_0** of shape `(num_layers * num_directions, batch, hidden_size)`: tensor containing the initial cell state for each element in the batch. If `(h_0, c_0)` is not provided, both **h_0** and **c_0** default to zero. Outputs: output, (h_n, c_n) - **output** of shape `(seq_len, batch, num_directions * hidden_size)`: tensor containing the output features `(h_t)` from the last layer of the LSTM, 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. For the unpacked case, the directions can be separated using ``output.view(seq_len, batch, num_directions, hidden_size)``, with forward and backward being direction `0` and `1` respectively. Similarly, the directions can be separated in the packed case. - **h_n** of shape `(num_layers * num_directions, batch, hidden_size)`: tensor containing the hidden state for `t = seq_len`. Like *output*, the layers can be separated using ``h_n.view(num_layers, num_directions, batch, hidden_size)`` and similarly for *c_n*. - **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 :math:`\text{k}^{th}` layer `(W_ii|W_if|W_ig|W_io)`, of shape `(4*hidden_size x input_size)` weight_hh_l[k] : the learnable hidden-hidden weights of the :math:`\text{k}^{th}` layer `(W_hi|W_hf|W_hg|W_ho)`, of shape `(4*hidden_size x hidden_size)` bias_ih_l[k] : the learnable input-hidden bias of the :math:`\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 :math:`\text{k}^{th}` layer `(b_hi|b_hf|b_hg|b_ho)`, of shape `(4*hidden_size)` .. note:: All the weights and biases are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where :math:`k = \frac{1}{\text{hidden\_size}}` .. include:: cudnn_persistent_rnn.rst Examples:: >>> rnn = nn.LSTM(10, 20, 2) >>> input = torch.randn(5, 3, 10) >>> h0 = torch.randn(2, 3, 20) >>> c0 = torch.randn(2, 3, 20) >>> output, (hn, cn) = rnn(input, (h0, c0)) """ def __init__(self, *args, **kwargs): super(LSTM, self).__init__('LSTM', *args, **kwargs) 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 = \sigma(W_{ir} x_t + b_{ir} + W_{hr} h_{(t-1)} + b_{hr}) \\ z_t = \sigma(W_{iz} x_t + b_{iz} + W_{hz} h_{(t-1)} + b_{hz}) \\ n_t = \tanh(W_{in} x_t + b_{in} + r_t (W_{hn} h_{(t-1)}+ b_{hn})) \\ h_t = (1 - z_t) n_t + z_t h_{(t-1)} \end{array} where :math:`h_t` is the hidden state at time `t`, :math:`x_t` is the input at time `t`, :math:`h_{(t-1)}` is the hidden state of the layer at time `t-1` or the initial hidden state at time `0`, and :math:`r_t`, :math:`z_t`, :math:`n_t` are the reset, update, and new gates, respectively. :math:`\sigma` is the sigmoid function. In a multilayer GRU, the input :math:`i^{(l)}_t` of the :math:`l` -th layer (:math:`l >= 2`) is the hidden state :math:`h^{(l-1)}_t` of the previous layer multiplied by dropout :math:`\delta^{(l-1)}_t` where each :math:`\delta^{(l-1)_t}` is a Bernoulli random variable which is :math:`0` with probability :attr:`dropout`. 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. E.g., setting ``num_layers=2`` would mean stacking two GRUs together to form a `stacked GRU`, with the second GRU taking in outputs of the first GRU 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). Default: ``False`` dropout: If non-zero, introduces a `Dropout` layer on the outputs of each GRU layer except the last layer, with dropout probability equal to :attr:`dropout`. Default: 0 bidirectional: If ``True``, becomes a bidirectional GRU. Default: ``False`` Inputs: input, h_0 - **input** of shape `(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** of shape `(num_layers * num_directions, batch, hidden_size)`: tensor containing the initial hidden state for each element in the batch. Defaults to zero if not provided. If the RNN is bidirectional, num_directions should be 2, else it should be 1. Outputs: output, h_n - **output** of shape `(seq_len, batch, num_directions * hidden_size)`: tensor containing the output features h_t from the last layer of the GRU, 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. For the unpacked case, the directions can be separated using ``output.view(seq_len, batch, num_directions, hidden_size)``, with forward and backward being direction `0` and `1` respectively. Similarly, the directions can be separated in the packed case. - **h_n** of shape `(num_layers * num_directions, batch, hidden_size)`: tensor containing the hidden state for `t = seq_len` Like *output*, the layers can be separated using ``h_n.view(num_layers, num_directions, batch, hidden_size)``. Attributes: weight_ih_l[k] : the learnable input-hidden weights of the :math:`\text{k}^{th}` layer (W_ir|W_iz|W_in), of shape `(3*hidden_size x input_size)` weight_hh_l[k] : the learnable hidden-hidden weights of the :math:`\text{k}^{th}` layer (W_hr|W_hz|W_hn), of shape `(3*hidden_size x hidden_size)` bias_ih_l[k] : the learnable input-hidden bias of the :math:`\text{k}^{th}` layer (b_ir|b_iz|b_in), of shape `(3*hidden_size)` bias_hh_l[k] : the learnable hidden-hidden bias of the :math:`\text{k}^{th}` layer (b_hr|b_hz|b_hn), of shape `(3*hidden_size)` .. note:: All the weights and biases are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where :math:`k = \frac{1}{\text{hidden\_size}}` .. include:: cudnn_persistent_rnn.rst Examples:: >>> rnn = nn.GRU(10, 20, 2) >>> input = torch.randn(5, 3, 10) >>> h0 = 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 __init__(self, input_size, hidden_size, bias, num_chunks): super(RNNCellBase, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.bias = bias self.weight_ih = Parameter(torch.Tensor(num_chunks * hidden_size, input_size)) self.weight_hh = Parameter(torch.Tensor(num_chunks * hidden_size, hidden_size)) if bias: self.bias_ih = Parameter(torch.Tensor(num_chunks * hidden_size)) self.bias_hh = Parameter(torch.Tensor(num_chunks * hidden_size)) else: self.register_parameter('bias_ih', None) self.register_parameter('bias_hh', None) self.reset_parameters() def extra_repr(self): s = '{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}' return s.format(**self.__dict__) def check_forward_input(self, input): if input.size(1) != self.input_size: raise RuntimeError( "input has inconsistent input_size: got {}, expected {}".format( input.size(1), self.input_size)) def check_forward_hidden(self, input, hx, hidden_label=''): if input.size(0) != hx.size(0): raise RuntimeError( "Input batch size {} doesn't match hidden{} batch size {}".format( input.size(0), hidden_label, hx.size(0))) if hx.size(1) != self.hidden_size: raise RuntimeError( "hidden{} has inconsistent hidden_size: got {}, expected {}".format( hidden_label, hx.size(1), self.hidden_size)) def reset_parameters(self): stdv = 1.0 / math.sqrt(self.hidden_size) for weight in self.parameters(): init.uniform_(weight, -stdv, stdv) 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 :attr:`nonlinearity` is `'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. Can be either 'tanh' or 'relu'. Default: 'tanh' Inputs: input, hidden - **input** of shape `(batch, input_size)`: tensor containing input features - **hidden** of shape `(batch, hidden_size)`: tensor containing the initial hidden state for each element in the batch. Defaults to zero if not provided. Outputs: h' - **h'** of shape `(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 `(hidden_size x input_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)` .. note:: All the weights and biases are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where :math:`k = \frac{1}{\text{hidden\_size}}` Examples:: >>> rnn = nn.RNNCell(10, 20) >>> input = torch.randn(6, 3, 10) >>> hx = 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__(input_size, hidden_size, bias, num_chunks=1) self.nonlinearity = nonlinearity def forward(self, input, hx=None): self.check_forward_input(input) if hx is None: hx = input.new_zeros(input.size(0), self.hidden_size, requires_grad=False) self.check_forward_hidden(input, hx) if self.nonlinearity == "tanh": func = _VF.rnn_tanh_cell elif self.nonlinearity == "relu": func = _VF.rnn_relu_cell else: raise RuntimeError( "Unknown nonlinearity: {}".format(self.nonlinearity)) return func( input, hx, self.weight_ih, self.weight_hh, self.bias_ih, self.bias_hh, ) class LSTMCell(RNNCellBase): r"""A long short-term memory (LSTM) cell. .. math:: \begin{array}{ll} i = \sigma(W_{ii} x + b_{ii} + W_{hi} h + b_{hi}) \\ f = \sigma(W_{if} x + b_{if} + W_{hf} h + b_{hf}) \\ g = \tanh(W_{ig} x + b_{ig} + W_{hg} h + b_{hg}) \\ o = \sigma(W_{io} x + b_{io} + W_{ho} h + b_{ho}) \\ c' = f * c + i * g \\ h' = o \tanh(c') \\ \end{array} where :math:`\sigma` is the sigmoid function. 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** of shape `(batch, input_size)`: tensor containing input features - **h_0** of shape `(batch, hidden_size)`: tensor containing the initial hidden state for each element in the batch. - **c_0** of shape `(batch, hidden_size)`: tensor containing the initial cell state for each element in the batch. If `(h_0, c_0)` is not provided, both **h_0** and **c_0** default to zero. Outputs: h_1, c_1 - **h_1** of shape `(batch, hidden_size)`: tensor containing the next hidden state for each element in the batch - **c_1** of shape `(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 `(4*hidden_size x input_size)` weight_hh: the learnable hidden-hidden weights, of shape `(4*hidden_size x hidden_size)` bias_ih: the learnable input-hidden bias, of shape `(4*hidden_size)` bias_hh: the learnable hidden-hidden bias, of shape `(4*hidden_size)` .. note:: All the weights and biases are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where :math:`k = \frac{1}{\text{hidden\_size}}` Examples:: >>> rnn = nn.LSTMCell(10, 20) >>> input = torch.randn(6, 3, 10) >>> hx = torch.randn(3, 20) >>> cx = 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__(input_size, hidden_size, bias, num_chunks=4) def forward(self, input, hx=None): self.check_forward_input(input) if hx is None: hx = input.new_zeros(input.size(0), self.hidden_size, requires_grad=False) hx = (hx, hx) self.check_forward_hidden(input, hx[0], '[0]') self.check_forward_hidden(input, hx[1], '[1]') return _VF.lstm_cell( input, hx, self.weight_ih, self.weight_hh, self.bias_ih, self.bias_hh, ) class GRUCell(RNNCellBase): r"""A gated recurrent unit (GRU) cell .. math:: \begin{array}{ll} r = \sigma(W_{ir} x + b_{ir} + W_{hr} h + b_{hr}) \\ z = \sigma(W_{iz} x + b_{iz} + W_{hz} h + b_{hz}) \\ n = \tanh(W_{in} x + b_{in} + r * (W_{hn} h + b_{hn})) \\ h' = (1 - z) * n + z * h \end{array} where :math:`\sigma` is the sigmoid function. 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** of shape `(batch, input_size)`: tensor containing input features - **hidden** of shape `(batch, hidden_size)`: tensor containing the initial hidden state for each element in the batch. Defaults to zero if not provided. Outputs: h' - **h'** of shape `(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 `(3*hidden_size x input_size)` weight_hh: the learnable hidden-hidden weights, of shape `(3*hidden_size x hidden_size)` bias_ih: the learnable input-hidden bias, of shape `(3*hidden_size)` bias_hh: the learnable hidden-hidden bias, of shape `(3*hidden_size)` .. note:: All the weights and biases are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where :math:`k = \frac{1}{\text{hidden\_size}}` Examples:: >>> rnn = nn.GRUCell(10, 20) >>> input = torch.randn(6, 3, 10) >>> hx = 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__(input_size, hidden_size, bias, num_chunks=3) def forward(self, input, hx=None): self.check_forward_input(input) if hx is None: hx = input.new_zeros(input.size(0), self.hidden_size, requires_grad=False) self.check_forward_hidden(input, hx) return _VF.gru_cell( input, hx, self.weight_ih, self.weight_hh, self.bias_ih, self.bias_hh, )