Merge pull request #7575 from pkuyym/fix-7555

Add pyton wrapper for row conv operator.

doc/api/v2/fluid/layers.rst

@@ -529,3 +529,8 @@ sequence_reshape
 ----------------
 ..  autofunction:: paddle.v2.fluid.layers.sequence_reshape
     :noindex:
+
+row_conv
+--------
+..  autofunction:: paddle.v2.fluid.layers.row_conv
+    :noindex:

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

@@ -62,6 +62,7 @@ __all__ = [
     'im2sequence',
     'nce',
     'beam_search',
+    'row_conv',
 ]
 
 
@@ -2564,3 +2565,56 @@ def im2sequence(input, filter_size=1, stride=1, padding=0, name=None):
             'paddings': padding,
         })
     return out
+
+
+def row_conv(input, future_context_size, param_attr=None, act=None):
+    """Row Conv Operator. This layer will apply lookahead convolution to
+    **input**. The input variable should be a 2D LoDTensor with shape [T, D].
+    Parameters with shape [future_context_size + 1, D] will be created. The math
+    equation of row convolution is as follows:
+
+    .. math::
+        Out_{i} = \sum_{j = i} ^ {i + \\tau} X_{j} \odot W_{i - j}
+
+    In the above equation:
+
+    * :math:`Out_{i}`: The i-th row of output variable with shape [1, D].
+    * :math:`\\tau`: Future context size.
+    * :math:`X_{j}`: The j-th row of input variable with shape [1, D].
+    * :math:`W_{i-j}`: The (i-j)-th row of parameters with shape [1, D].
+
+    More details about row_conv please refer to the paper \
+    (http://www.cs.cmu.edu/~dyogatam/papers/wang+etal.iclrworkshop2016.pdf) and
+    the design document \
+    (https://github.com/PaddlePaddle/Paddle/issues/2228#issuecomment-303903645).
+
+    Args:
+        input (Variable): Input variable, a 2D LoDTensor with shape [T, D].
+        future_context_size (int): Future context size. Please note, the shape
+            of convolution kernel is [future_context_size + 1, D].
+        param_attr (ParamAttr): Attributes of parameters, including
+            name, initializer etc.
+        act (str): Non-linear activation to be applied to output variable.
+
+    Returns:
+        Variable: The output tensor with same shape as input tensor.
+
+    Examples:
+        .. code-block:: python
+
+            x = fluid.layers.data(name='x', shape=[16],
+                            dtype='float32', lod_level=1)
+            out = fluid.layers.row_conv(input=x, future_context_size=2)
+    """
+    helper = LayerHelper('row_conv', **locals())
+    dtype = helper.input_dtype()
+    filter_shape = [future_context_size + 1, input.shape[1]]
+    filter_param = helper.create_parameter(
+        attr=helper.param_attr, shape=filter_shape, dtype=dtype)
+    out = helper.create_tmp_variable(dtype)
+    helper.append_op(
+        type='row_conv',
+        inputs={'X': [input],
+                'Filter': [filter_param]},
+        outputs={'Out': [out]})
+    return helper.append_activation(out)

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

@@ -271,6 +271,14 @@ class TestBook(unittest.TestCase):
         self.assertIsNotNone(avg_loss)
         print(str(default_main_program()))
 
+    def test_row_conv(self):
+        program = Program()
+        with program_guard(program):
+            x = layers.data(name='x', shape=[16], dtype='float32', lod_level=1)
+            out = layers.row_conv(input=x, future_context_size=2)
+            self.assertIsNotNone(out)
+        print(str(program))
+
 
 if __name__ == '__main__':
     unittest.main()