[GitHub] eric-haibin-lin commented on a change in pull request #11948: [MXNET-766] add unroll RNN for HybridBlock

[GitBox <gi...@apache.org>]

eric-haibin-lin commented on a change in pull request #11948: [MXNET-766] add unroll RNN for HybridBlock
URL: https://github.com/apache/incubator-mxnet/pull/11948#discussion_r207714212
 
 

 ##########
 File path: python/mxnet/gluon/contrib/rnn/rnn_cell.py
 ##########
 @@ -315,3 +317,138 @@ def hybrid_forward(self, F, inputs, states, i2h_weight,
 
         return next_r, [next_r, next_c]
     # pylint: enable= arguments-differ
+
+
+def _contrib_format_sequence(inputs, layout, in_layout=None):
+    assert inputs is not None, \
+        "unroll(inputs=None) has been deprecated. " \
+        "Please create input variables outside unroll."
+
+    axis = layout.find('T')
+    batch_axis = layout.find('N')
+    batch_size = 0
+    in_axis = in_layout.find('T') if in_layout is not None else axis
+    assert isinstance(inputs, tensor_types)
+    if isinstance(inputs, symbol.Symbol):
+        F = symbol
+    else:
+        F = ndarray
+        batch_size = inputs.shape[batch_axis]
+
+    if axis != in_axis:
+        inputs = F.swapaxes(inputs, dim1=axis, dim2=in_axis)
+
+    return inputs, axis, F, batch_size
+
+
+def unroll(cell, inputs, begin_state, drop_inputs=0, drop_outputs=0,
+           layout='TNC', valid_length=None):
+    """Unrolls an RNN cell across time steps.
+
+    Currently, 'TNC' is a preferred layout. unroll on the input of this layout
+    runs much faster.
+
+    Parameters
+    ----------
+    cell : an object whose base class is RNNCell.
+        The RNN cell to run on the input sequence.
+    inputs : Symbol
+        It should have shape (batch_size, length, ...) if `layout` is 'NTC',
+        or (length, batch_size, ...) if `layout` is 'TNC'.
+    begin_state : nested list of Symbol
+        The initial states of the RNN sequence.
+    drop_inputs : float, default 0.
+        The dropout rate for inputs. Won't apply dropout if it equals 0.
+    drop_outputs : float, default 0.
+        The dropout rate for outputs. Won't apply dropout if it equals 0.
+    layout : str, optional
+        `layout` of input symbol. Only used if inputs
+        is a single Symbol.
+    valid_length : Symbol, NDArray or None
+        `valid_length` specifies the length of the sequences in the batch without padding.
+        This option is especially useful for building sequence-to-sequence models where
+        the input and output sequences would potentially be padded.
+        If `valid_length` is None, all sequences are assumed to have the same length.
+        If `valid_length` is a Symbol or NDArray, it should have shape (batch_size,).
+        The ith element will be the length of the ith sequence in the batch.
+        The last valid state will be return and the padded outputs will be masked with 0.
+        Note that `valid_length` must be smaller or equal to `length`.
+
+    Returns
+    -------
+    outputs : Symbol
+        the output of the RNN from this unrolling.
+
+    states : list of Symbol
+        The new state of this RNN after this unrolling.
+        The type of this symbol is same as the output of `begin_state`.
+
+    Examples
+    --------
+    >>> seq_len = 3
+    >>> batch_size = 2
+    >>> input_size = 5
+    >>> cell = mx.gluon.rnn.LSTMCell(input_size, prefix='rnn_')
+    >>> cell.initialize(ctx=mx.cpu())
+    >>> rnn_data = mx.nd.normal(loc=0, scale=1, shape=(seq_len, batch_size, input_size))
+    >>> state_shape = (batch_size, input_size)
+    >>> states = [mx.nd.normal(loc=0, scale=1, shape=state_shape) for i in range(2)]
+    >>> valid_length = mx.nd.array([2, 3])
+    >>> output, states = mx.gluon.contrib.rnn.rnn_cell.unroll(cell, rnn_data, states,
+                                                              valid_length=valid_length,
+                                                              layout='TNC')
+    >>> print(output)
+    [[[ 0.00767238  0.00023103  0.03973929 -0.00925503 -0.05660512]
+      [ 0.00881535  0.05428379 -0.02493718 -0.01834097  0.02189514]]
+     [[-0.00676967  0.01447039  0.01287002 -0.00574152 -0.05734247]
+      [ 0.01568508  0.02650866 -0.04270559 -0.04328435  0.00904011]]
+     [[ 0.          0.          0.          0.          0.        ]
+      [ 0.01055336  0.02734251 -0.03153727 -0.03742751 -0.01378113]]]
+     <NDArray 3x2x5 @cpu(0)>
+    """
+
+    inputs, axis, F, _ = _contrib_format_sequence(inputs, layout)
+    if axis != 0:
+        axes = list(range(len(layout)))
+        tmp = axes[0]
 
 Review comment:
   nit: you can swap element by `foo[i], foo[j] = foo[j], foo[i]` in python

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