Source code for mxnet.gluon.rnn.rnn_layer

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# coding: utf-8
# pylint: disable=no-member, invalid-name, protected-access, no-self-use
# pylint: disable=too-many-branches, too-many-arguments, no-self-use
# pylint: disable=too-many-lines, arguments-differ
"""Definition of various recurrent neural network layers."""
from __future__ import print_function
import re

__all__ = ['RNN', 'LSTM', 'GRU']

from ... import ndarray, symbol
from .. import HybridBlock, tensor_types
from . import rnn_cell

class _RNNLayer(HybridBlock):
    """Implementation of recurrent layers."""
    def __init__(self, hidden_size, num_layers, layout,
                 dropout, bidirectional, input_size,
                 i2h_weight_initializer, h2h_weight_initializer,
                 i2h_bias_initializer, h2h_bias_initializer,
                 mode, **kwargs):
        super(_RNNLayer, self).__init__(**kwargs)
        assert layout in ('TNC', 'NTC'), \
            "Invalid layout %s; must be one of ['TNC' or 'NTC']"%layout
        self._hidden_size = hidden_size
        self._num_layers = num_layers
        self._mode = mode
        self._layout = layout
        self._dropout = dropout
        self._dir = 2 if bidirectional else 1
        self._input_size = input_size
        self._i2h_weight_initializer = i2h_weight_initializer
        self._h2h_weight_initializer = h2h_weight_initializer
        self._i2h_bias_initializer = i2h_bias_initializer
        self._h2h_bias_initializer = h2h_bias_initializer

        self._gates = {'rnn_relu': 1, 'rnn_tanh': 1, 'lstm': 4, 'gru': 3}[mode]

        ng, ni, nh = self._gates, input_size, hidden_size
        for i in range(num_layers):
            for j in ['l', 'r'][:self._dir]:
                self._register_param('{}{}_i2h_weight'.format(j, i),
                                     shape=(ng*nh, ni),
                                     init=i2h_weight_initializer)
                self._register_param('{}{}_h2h_weight'.format(j, i),
                                     shape=(ng*nh, nh),
                                     init=h2h_weight_initializer)
                self._register_param('{}{}_i2h_bias'.format(j, i),
                                     shape=(ng*nh,),
                                     init=i2h_bias_initializer)
                self._register_param('{}{}_h2h_bias'.format(j, i),
                                     shape=(ng*nh,),
                                     init=h2h_bias_initializer)
            ni = nh * self._dir

    def _register_param(self, name, shape, init):
        p = self.params.get(name, shape=shape, init=init,
                            allow_deferred_init=True)
        setattr(self, name, p)
        return p

    def __repr__(self):
        s = '{name}({mapping}, {_layout}'
        if self._num_layers != 1:
            s += ', num_layers={_num_layers}'
        if self._dropout != 0:
            s += ', dropout={_dropout}'
        if self._dir == 2:
            s += ', bidirectional'
        s += ')'
        shape = self.l0_i2h_weight.shape
        mapping = '{0} -> {1}'.format(shape[1] if shape[1] else None, shape[0] // self._gates)
        return s.format(name=self.__class__.__name__,
                        mapping=mapping,
                        **self.__dict__)

    def _collect_params_with_prefix(self, prefix=''):
        if prefix:
            prefix += '.'
        pattern = re.compile(r'(l|r)(\d)_(i2h|h2h)_(weight|bias)\Z')
        def convert_key(m, bidirectional): # for compatibility with old parameter format
            d, l, g, t = [m.group(i) for i in range(1, 5)]
            if bidirectional:
                return '_unfused.{}.{}_cell.{}_{}'.format(l, d, g, t)
            else:
                return '_unfused.{}.{}_{}'.format(l, g, t)
        bidirectional = any(pattern.match(k).group(1) == 'r' for k in self._reg_params)

        ret = {prefix + convert_key(pattern.match(key), bidirectional) : val
               for key, val in self._reg_params.items()}
        for name, child in self._children.items():
            ret.update(child._collect_params_with_prefix(prefix + name))
        return ret

    def state_info(self, batch_size=0):
        raise NotImplementedError

    def _unfuse(self):
        """Unfuses the fused RNN in to a stack of rnn cells."""
        get_cell = {'rnn_relu': lambda **kwargs: rnn_cell.RNNCell(self._hidden_size,
                                                                  activation='relu',
                                                                  **kwargs),
                    'rnn_tanh': lambda **kwargs: rnn_cell.RNNCell(self._hidden_size,
                                                                  activation='tanh',
                                                                  **kwargs),
                    'lstm': lambda **kwargs: rnn_cell.LSTMCell(self._hidden_size,
                                                               **kwargs),
                    'gru': lambda **kwargs: rnn_cell.GRUCell(self._hidden_size,
                                                             **kwargs)}[self._mode]

        stack = rnn_cell.HybridSequentialRNNCell(prefix=self.prefix, params=self.params)
        with stack.name_scope():
            ni = self._input_size
            for i in range(self._num_layers):
                kwargs = {'input_size': ni,
                          'i2h_weight_initializer': self._i2h_weight_initializer,
                          'h2h_weight_initializer': self._h2h_weight_initializer,
                          'i2h_bias_initializer': self._i2h_bias_initializer,
                          'h2h_bias_initializer': self._h2h_bias_initializer}
                if self._dir == 2:
                    stack.add(rnn_cell.BidirectionalCell(
                        get_cell(prefix='l%d_'%i, **kwargs),
                        get_cell(prefix='r%d_'%i, **kwargs)))
                else:
                    stack.add(get_cell(prefix='l%d_'%i, **kwargs))

                if self._dropout > 0 and i != self._num_layers - 1:
                    stack.add(rnn_cell.DropoutCell(self._dropout))

                ni = self._hidden_size * self._dir

        return stack

    def begin_state(self, batch_size=0, func=ndarray.zeros, **kwargs):
        """Initial state for this cell.

        Parameters
        ----------
        batch_size: int
            Only required for `NDArray` API. Size of the batch ('N' in layout).
            Dimension of the input.
        func : callable, default `ndarray.zeros`
            Function for creating initial state.

            For Symbol API, func can be `symbol.zeros`, `symbol.uniform`,
            `symbol.var` etc. Use `symbol.var` if you want to directly
            feed input as states.

            For NDArray API, func can be `ndarray.zeros`, `ndarray.ones`, etc.

        **kwargs :
            Additional keyword arguments passed to func. For example
            `mean`, `std`, `dtype`, etc.

        Returns
        -------
        states : nested list of Symbol
            Starting states for the first RNN step.
        """
        states = []
        for i, info in enumerate(self.state_info(batch_size)):
            if info is not None:
                info.update(kwargs)
            else:
                info = kwargs
            states.append(func(name='%sh0_%d'%(self.prefix, i), **info))
        return states

    def hybrid_forward(self, F, inputs, states=None, **kwargs):
        if F is ndarray:
            batch_size = inputs.shape[self._layout.find('N')]
        skip_states = states is None
        if skip_states:
            if F is ndarray:
                states = self.begin_state(batch_size, ctx=inputs.context)
            else:
                states = self.begin_state(0, func=symbol.zeros)
        if isinstance(states, tensor_types):
            states = [states]
        if F is ndarray:
            for state, info in zip(states, self.state_info(batch_size)):
                if state.shape != info['shape']:
                    raise ValueError(
                        "Invalid recurrent state shape. Expecting %s, got %s."%(
                            str(info['shape']), str(state.shape)))
        out = self._forward_kernel(F, inputs, states, **kwargs)

        # out is (output, state)
        return out[0] if skip_states else out

    def _forward_kernel(self, F, inputs, states, **kwargs):
        """ forward using CUDNN or CPU kenrel"""
        if self._layout == 'NTC':
            inputs = F.swapaxes(inputs, dim1=0, dim2=1)
        params = (kwargs['{}{}_{}_{}'.format(d, l, g, t)].reshape(-1)
                  for t in ['weight', 'bias']
                  for l in range(self._num_layers)
                  for d in ['l', 'r'][:self._dir]
                  for g in ['i2h', 'h2h'])
        params = F._internal._rnn_param_concat(*params, dim=0)

        rnn = F.RNN(inputs, params, *states, state_size=self._hidden_size,
                    num_layers=self._num_layers, bidirectional=self._dir == 2,
                    p=self._dropout, state_outputs=True, mode=self._mode)

        if self._mode == 'lstm':
            outputs, states = rnn[0], [rnn[1], rnn[2]]
        else:
            outputs, states = rnn[0], [rnn[1]]

        if self._layout == 'NTC':
            outputs = F.swapaxes(outputs, dim1=0, dim2=1)

        return outputs, states


[docs]class RNN(_RNNLayer): 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 output 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`. Parameters ---------- hidden_size: int The number of features in the hidden state h. num_layers: int, default 1 Number of recurrent layers. activation: {'relu' or 'tanh'}, default 'relu' The activation function to use. layout : str, default 'TNC' The format of input and output tensors. T, N and C stand for sequence length, batch size, and feature dimensions respectively. dropout: float, default 0 If non-zero, introduces a dropout layer on the outputs of each RNN layer except the last layer. bidirectional: bool, default False If `True`, becomes a bidirectional RNN. i2h_weight_initializer : str or Initializer Initializer for the input weights matrix, used for the linear transformation of the inputs. h2h_weight_initializer : str or Initializer Initializer for the recurrent weights matrix, used for the linear transformation of the recurrent state. i2h_bias_initializer : str or Initializer Initializer for the bias vector. h2h_bias_initializer : str or Initializer Initializer for the bias vector. input_size: int, default 0 The number of expected features in the input x. If not specified, it will be inferred from input. prefix : str or None Prefix of this `Block`. params : ParameterDict or None Shared Parameters for this `Block`. Inputs: - **data**: input tensor with shape `(sequence_length, batch_size, input_size)` when `layout` is "TNC". For other layouts, dimensions are permuted accordingly using transpose() operator which adds performance overhead. Consider creating batches in TNC layout during data batching step. - **states**: initial recurrent state tensor with shape `(num_layers, batch_size, num_hidden)`. If `bidirectional` is True, shape will instead be `(2*num_layers, batch_size, num_hidden)`. If `states` is None, zeros will be used as default begin states. Outputs: - **out**: output tensor with shape `(sequence_length, batch_size, num_hidden)` when `layout` is "TNC". If `bidirectional` is True, output shape will instead be `(sequence_length, batch_size, 2*num_hidden)` - **out_states**: output recurrent state tensor with the same shape as `states`. If `states` is None `out_states` will not be returned. Examples -------- >>> layer = mx.gluon.rnn.RNN(100, 3) >>> layer.initialize() >>> input = mx.nd.random.uniform(shape=(5, 3, 10)) >>> # by default zeros are used as begin state >>> output = layer(input) >>> # manually specify begin state. >>> h0 = mx.nd.random.uniform(shape=(3, 3, 100)) >>> output, hn = layer(input, h0) """ def __init__(self, hidden_size, num_layers=1, activation='relu', layout='TNC', dropout=0, bidirectional=False, i2h_weight_initializer=None, h2h_weight_initializer=None, i2h_bias_initializer='zeros', h2h_bias_initializer='zeros', input_size=0, **kwargs): super(RNN, self).__init__(hidden_size, num_layers, layout, dropout, bidirectional, input_size, i2h_weight_initializer, h2h_weight_initializer, i2h_bias_initializer, h2h_bias_initializer, 'rnn_'+activation, **kwargs) def state_info(self, batch_size=0): return [{'shape': (self._num_layers * self._dir, batch_size, self._hidden_size), '__layout__': 'LNC'}]
[docs]class LSTM(_RNNLayer): 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. Parameters ---------- hidden_size: int The number of features in the hidden state h. num_layers: int, default 1 Number of recurrent layers. layout : str, default 'TNC' The format of input and output tensors. T, N and C stand for sequence length, batch size, and feature dimensions respectively. dropout: float, default 0 If non-zero, introduces a dropout layer on the outputs of each RNN layer except the last layer. bidirectional: bool, default False If `True`, becomes a bidirectional RNN. i2h_weight_initializer : str or Initializer Initializer for the input weights matrix, used for the linear transformation of the inputs. h2h_weight_initializer : str or Initializer Initializer for the recurrent weights matrix, used for the linear transformation of the recurrent state. i2h_bias_initializer : str or Initializer, default 'lstmbias' Initializer for the bias vector. By default, bias for the forget gate is initialized to 1 while all other biases are initialized to zero. h2h_bias_initializer : str or Initializer Initializer for the bias vector. input_size: int, default 0 The number of expected features in the input x. If not specified, it will be inferred from input. prefix : str or None Prefix of this `Block`. params : `ParameterDict` or `None` Shared Parameters for this `Block`. Inputs: - **data**: input tensor with shape `(sequence_length, batch_size, input_size)` when `layout` is "TNC". For other layouts, dimensions are permuted accordingly using transpose() operator which adds performance overhead. Consider creating batches in TNC layout during data batching step. - **states**: a list of two initial recurrent state tensors. Each has shape `(num_layers, batch_size, num_hidden)`. If `bidirectional` is True, shape will instead be `(2*num_layers, batch_size, num_hidden)`. If `states` is None, zeros will be used as default begin states. Outputs: - **out**: output tensor with shape `(sequence_length, batch_size, num_hidden)` when `layout` is "TNC". If `bidirectional` is True, output shape will instead be `(sequence_length, batch_size, 2*num_hidden)` - **out_states**: a list of two output recurrent state tensors with the same shape as in `states`. If `states` is None `out_states` will not be returned. Examples -------- >>> layer = mx.gluon.rnn.LSTM(100, 3) >>> layer.initialize() >>> input = mx.nd.random.uniform(shape=(5, 3, 10)) >>> # by default zeros are used as begin state >>> output = layer(input) >>> # manually specify begin state. >>> h0 = mx.nd.random.uniform(shape=(3, 3, 100)) >>> c0 = mx.nd.random.uniform(shape=(3, 3, 100)) >>> output, hn = layer(input, [h0, c0]) """ def __init__(self, hidden_size, num_layers=1, layout='TNC', dropout=0, bidirectional=False, input_size=0, i2h_weight_initializer=None, h2h_weight_initializer=None, i2h_bias_initializer='zeros', h2h_bias_initializer='zeros', **kwargs): super(LSTM, self).__init__(hidden_size, num_layers, layout, dropout, bidirectional, input_size, i2h_weight_initializer, h2h_weight_initializer, i2h_bias_initializer, h2h_bias_initializer, 'lstm', **kwargs) def state_info(self, batch_size=0): return [{'shape': (self._num_layers * self._dir, batch_size, self._hidden_size), '__layout__': 'LNC'}, {'shape': (self._num_layers * self._dir, batch_size, self._hidden_size), '__layout__': 'LNC'}]
[docs]class GRU(_RNNLayer): 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. Parameters ---------- hidden_size: int The number of features in the hidden state h num_layers: int, default 1 Number of recurrent layers. layout : str, default 'TNC' The format of input and output tensors. T, N and C stand for sequence length, batch size, and feature dimensions respectively. dropout: float, default 0 If non-zero, introduces a dropout layer on the outputs of each RNN layer except the last layer bidirectional: bool, default False If True, becomes a bidirectional RNN. i2h_weight_initializer : str or Initializer Initializer for the input weights matrix, used for the linear transformation of the inputs. h2h_weight_initializer : str or Initializer Initializer for the recurrent weights matrix, used for the linear transformation of the recurrent state. i2h_bias_initializer : str or Initializer Initializer for the bias vector. h2h_bias_initializer : str or Initializer Initializer for the bias vector. input_size: int, default 0 The number of expected features in the input x. If not specified, it will be inferred from input. prefix : str or None Prefix of this `Block`. params : ParameterDict or None Shared Parameters for this `Block`. Inputs: - **data**: input tensor with shape `(sequence_length, batch_size, input_size)` when `layout` is "TNC". For other layouts, dimensions are permuted accordingly using transpose() operator which adds performance overhead. Consider creating batches in TNC layout during data batching step. - **states**: initial recurrent state tensor with shape `(num_layers, batch_size, num_hidden)`. If `bidirectional` is True, shape will instead be `(2*num_layers, batch_size, num_hidden)`. If `states` is None, zeros will be used as default begin states. Outputs: - **out**: output tensor with shape `(sequence_length, batch_size, num_hidden)` when `layout` is "TNC". If `bidirectional` is True, output shape will instead be `(sequence_length, batch_size, 2*num_hidden)` - **out_states**: output recurrent state tensor with the same shape as `states`. If `states` is None `out_states` will not be returned. Examples -------- >>> layer = mx.gluon.rnn.GRU(100, 3) >>> layer.initialize() >>> input = mx.nd.random.uniform(shape=(5, 3, 10)) >>> # by default zeros are used as begin state >>> output = layer(input) >>> # manually specify begin state. >>> h0 = mx.nd.random.uniform(shape=(3, 3, 100)) >>> output, hn = layer(input, h0) """ def __init__(self, hidden_size, num_layers=1, layout='TNC', dropout=0, bidirectional=False, input_size=0, i2h_weight_initializer=None, h2h_weight_initializer=None, i2h_bias_initializer='zeros', h2h_bias_initializer='zeros', **kwargs): super(GRU, self).__init__(hidden_size, num_layers, layout, dropout, bidirectional, input_size, i2h_weight_initializer, h2h_weight_initializer, i2h_bias_initializer, h2h_bias_initializer, 'gru', **kwargs) def state_info(self, batch_size=0): return [{'shape': (self._num_layers * self._dir, batch_size, self._hidden_size), '__layout__': 'LNC'}]