Merge pull request #7694 from pkuyym/fix-7691

Add python wrapper for sequence reshape operator.

doc/api/v2/fluid/layers.rst

@@ -504,3 +504,8 @@ l2_normalize
 ------------
 ..  autofunction:: paddle.v2.fluid.layers.l2_normalize
     :noindex:
+
+sequence_reshape
+----------------
+..  autofunction:: paddle.v2.fluid.layers.sequence_reshape
+    :noindex:

python/paddle/v2/fluid/layers/nn.py

@@ -28,7 +28,7 @@ __all__ = [
     'batch_norm', 'beam_search_decode', 'conv2d_transpose', 'sequence_expand',
     'lstm_unit', 'reduce_sum', 'reduce_mean', 'reduce_max', 'reduce_min',
     'sequence_first_step', 'sequence_last_step', 'dropout', 'split',
-    'l2_normalize', 'matmul', 'warpctc'
+    'l2_normalize', 'matmul', 'warpctc', 'sequence_reshape'
 ]
 
 
@@ -1914,3 +1914,57 @@ def warpctc(input, label, blank=0, norm_by_times=False, **kwargs):
         attrs={'blank': blank,
                'norm_by_times': norm_by_times})
     return loss_out
+
+
+def sequence_reshape(input, new_dim):
+    """
+    **Sequence Reshape Layer**
+
+    This layer will rearrange the input sequences. The new dimension is set by
+    user. Length of each sequence is computed according to original length,
+    original dimension and new dimension. The following example will help to
+    illustrate the function of this layer:
+
+    .. code-block:: text
+
+        x is a LoDTensor:
+            x.lod  = [[0, 2, 6]]
+            x.data = [[1, 2], [3, 4],
+                      [5, 6], [7, 8], [9, 10], [11, 12]]
+            x.dims = [6, 2]
+
+        set new_dim = 4
+
+        then out is a LoDTensor:
+            out.lod  = [[0, 1, 3]]
+            out.data = [[1, 2, 3, 4],
+                        [5, 6, 7, 8], [9, 10, 11, 12]]
+            out.dims = [3, 4]
+
+    Currently, only 1-level LoDTensor is supported and please make sure
+    (original length * original dimension) can be divided by new dimension with
+    no remainder for each sequence.
+
+    Args:
+       input (Variable): (LodTensor, default: LoDTensor<float>), a 2-D LoDTensor
+                with shape being [N, M] where M for dimension.
+       new_dim (int): New dimension which the input LoDTensor is reshaped to.
+
+    Returns:
+        Variable: Reshaped LoDTensor according to new dimension.
+
+    Examples:
+        .. code-block:: python
+
+            x = fluid.layers.data(name='x', shape=[5, 20],
+                              dtype='float32', lod_level=1)
+            x_reshaped = layers.sequence_reshape(input=x, new_dim=10)
+    """
+    helper = LayerHelper('sequence_reshape', **locals())
+    out = helper.create_tmp_variable(helper.input_dtype())
+    helper.append_op(
+        type='sequence_reshape',
+        inputs={'X': [input]},
+        outputs={'Out': [out]},
+        attrs={'new_dim': new_dim})
+    return out

python/paddle/v2/fluid/tests/test_layers.py

@@ -216,6 +216,14 @@ class TestBook(unittest.TestCase):
             self.assertIsNotNone(x)
         print(str(program))
 
+    def test_sequence_reshape(self):
+        program = Program()
+        with program_guard(program):
+            x = layers.data(name='x', shape=[8], dtype='float32', lod_level=1)
+            out = layers.sequence_reshape(input=x, new_dim=16)
+            self.assertIsNotNone(out)
+        print(str(program))
+
 
 if __name__ == '__main__':
     unittest.main()