Expose rank_loss op Python API (#12132)

* expose rank_loss python api

doc/fluid/api/layers.rst

@@ -1768,3 +1768,11 @@ reverse
 ..  autofunction:: paddle.fluid.layers.reverse
     :noindex:
 
+.. _api_fluid_layers_rank_loss:
+
+rank_loss
+-------
+
+..  autofunction:: paddle.fluid.layers.rank_loss
+    :noindex:
+

python/paddle/fluid/layers/nn.py

@@ -110,6 +110,7 @@ __all__ = [
     'relu',
     'log',
     'crop',
+    'rank_loss',
 ]
 
 
@@ -5282,3 +5283,74 @@ def crop(x, shape=None, offsets=None, name=None):
         outputs={'Out': out},
         attrs=None if len(attrs) == 0 else attrs)
     return out
+
+
+def rank_loss(label, left, right, name=None):
+    """
+    **Rank loss layer for RankNet**
+
+    RankNet(http://icml.cc/2015/wp-content/uploads/2015/06/icml_ranking.pdf)
+    is a pairwise ranking model with a training sample consisting of a pair
+    of documents, A and B. Label P indicates whether A is ranked higher than B
+    or not:
+ 
+    P = {0, 1} or {0, 0.5, 1}, where 0.5 means that there is no information
+    about the rank of the input pair.
+    
+    Rank loss layer takes three inputs: left (o_i), right (o_j) and
+    label (P_{i,j}). The inputs respectively represent RankNet's output scores
+    for documents A and B and the value of label P. The following equation
+    computes rank loss C_{i,j} from the inputs:
+    
+    $$
+      C_{i,j} = -\tilde{P_{ij}} * o_{i,j} + \log(1 + e^{o_{i,j}}) \\
+      o_{i,j} =  o_i - o_j  \\
+      \tilde{P_{i,j}} = \left \{0, 0.5, 1 \right \} \ or \ \left \{0, 1 \right \}
+    $$
+    
+    Rank loss layer takes batch inputs with size batch_size (batch_size >= 1).   
+ 
+    Args:
+        label (Variable): Indicats whether A ranked higher than B or not.
+        left (Variable): RankNet's output score for doc A.
+        right (Variable): RankNet's output score for doc B.
+        name(str|None): A name for this layer(optional). If set None, the layer
+                        will be named automatically.
+
+    Returns:
+        list: The value of rank loss.
+
+    Raises:
+        ValueError: Any of label, left, and right is not a variable.
+
+    Examples:
+
+        .. code-block:: python
+
+            label = fluid.layers.data(name="label", shape=[4, 1], dtype="float32")
+            left = fluid.layers.data(name="left", shape=[4, 1], dtype="float32")
+            right = fluid.layers.data(name="right", shape=[4, 1], dtype="float32")
+            out = fluid.layers.rank_loss(label, left, right)
+
+
+    """
+    helper = LayerHelper('rank_loss', **locals())
+
+    if not (isinstance(label, Variable)):
+        raise ValueError("The label should be a Variable")
+
+    if not (isinstance(left, Variable)):
+        raise ValueError("The left should be a Variable")
+
+    if not (isinstance(right, Variable)):
+        raise ValueError("The right should be a Variable")
+
+    out = helper.create_tmp_variable("float32")
+
+    helper.append_op(
+        type='rank_loss',
+        inputs={"Label": label,
+                "Left": left,
+                "Right": right},
+        outputs={'Out': out})
+    return out

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

@@ -443,6 +443,28 @@ class TestBook(unittest.TestCase):
             self.assertIsNotNone(ids)
         print(str(program))
 
+    def test_rank_loss(self):
+        program = Program()
+        with program_guard(program):
+            label = layers.data(
+                name='label',
+                append_batch_size=False,
+                shape=[16, 1],
+                dtype="float32")
+            left = layers.data(
+                name='left',
+                append_batch_size=False,
+                shape=[16, 1],
+                dtype="float32")
+            right = layers.data(
+                name='right',
+                append_batch_size=False,
+                shape=[16, 1],
+                dtype="float32")
+            out = layers.rank_loss(label, left, right, name="rank_loss")
+            self.assertIsNotNone(out)
+        print(str(program))
+
 
 if __name__ == '__main__':
     unittest.main()