Add python split and glu

doc/api/v2/fluid/layers.rst

@@ -348,3 +348,9 @@ reduce_min
 ..  autofunction:: paddle.v2.fluid.layers.reduce_min
     :noindex:
 
+
+split
+-----
+..  autofunction:: paddle.v2.fluid.layers.split
+    :noindex:
+

doc/api/v2/fluid/nets.rst

@@ -20,3 +20,8 @@ sequence_conv_pool
     :noindex:
 
 
+glu
+---
+..  autofunction:: paddle.v2.fluid.nets.glu
+    :noindex:
+

paddle/operators/split_op.cc

@@ -60,6 +60,12 @@ class SplitOp : public framework::OperatorWithKernel {
       }
     }
     ctx->SetOutputsDim("Out", outs_dims);
+    if (axis != 0) {
+      // Only pass LoD when not spliting along the first dim.
+      for (size_t i = 0; i < outs_number; ++i) {
+        ctx->ShareLoD("X", "Out", 0, i);
+      }
+    }
   }
 };
 

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

@@ -14,7 +14,7 @@ __all__ = [
     'chunk_eval', 'sequence_conv', 'conv2d', 'sequence_pool', 'pool2d',
     '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'
+    'sequence_first_step', 'sequence_last_step', 'dropout', 'split'
 ]
 
 
@@ -1504,3 +1504,63 @@ def reduce_min(input, dim=None, keep_dim=False):
             'reduce_all': True if dim == None else False
         })
     return out
+
+
+def split(input, num_or_sections, dim=-1):
+    """
+    Splits the tensor into multiple sub-tensors.
+
+    Args:
+        input (Variable): The input variable which is a Tensor or LoDTensor.
+        num_or_sections (int|list): If :attr:`num_or_sections` is an integer, 
+            then the integer indicates the number of equal sized sub-tensors 
+            that the tensor will be divided into. If :attr:`num_or_sections` 
+            is a list of integers, the length of list indicates the number of 
+            sub-tensors and the integers indicate the sizes of sub-tensors' 
+            :attr:`dim` dimension orderly.
+        dim (int): The dimension along which to split. If :math:`dim < 0`, the 
+            dimension to split along is :math:`rank(input) + dim`.
+
+    Returns:
+        List: The list of segmented tensor variables.
+
+    Examples:
+        .. code-block:: python
+
+            # x is a Tensor variable with shape [3, 9, 5]:
+            x0, x1, x2 = fluid.layers.split(x, num_or_sections=3, dim=1)
+            x0.shape  # [3, 3, 5]
+            x1.shape  # [3, 3, 5]
+            x2.shape  # [3, 3, 5]
+            x0, x1, x2 = fluid.layers.split(x, num_or_sections=[2, 3, 4], dim=1)
+            x0.shape  # [3, 2, 5]
+            x1.shape  # [3, 3, 5]
+            x2.shape  # [3, 4, 5]
+    """
+    helper = LayerHelper('split', **locals())
+    input_shape = input.shape
+    dim = (len(input_shape) + dim) if dim < 0 else dim
+    if isinstance(num_or_sections, int):
+        assert num_or_sections > 1, 'num_or_sections must be more than 1.'
+        assert input_shape[
+            dim] % num_or_sections == 0, 'num_or_sections must evenly divide input.shape[dim].'
+        num = num_or_sections
+    else:
+        assert len(num_or_sections) < input_shape[
+            dim], 'len(num_or_sections) must not be more than input.shape[dim].'
+        num = len(num_or_sections)
+    outs = [
+        helper.create_tmp_variable(dtype=helper.input_dtype())
+        for i in range(num)
+    ]
+    helper.append_op(
+        type='split',
+        inputs={'X': input},
+        outputs={'Out': outs},
+        attrs={
+            'num': num_or_sections if isinstance(num_or_sections, int) else 0,
+            'sections': num_or_sections
+            if isinstance(num_or_sections, list) else [],
+            'axis': dim
+        })
+    return outs

python/paddle/v2/fluid/nets.py

 import layers
 
-__all__ = ["simple_img_conv_pool", "sequence_conv_pool"]
+__all__ = ["simple_img_conv_pool", "sequence_conv_pool", "glu"]
 
 
 def simple_img_conv_pool(input,
@@ -98,3 +98,36 @@ def sequence_conv_pool(input,
 
     pool_out = layers.sequence_pool(input=conv_out, pool_type=pool_type)
     return pool_out
+
+
+def glu(input, dim=-1):
+    """
+    The gated linear unit composed by split and elementwise multiplication. 
+    Specifically, Split the input into two equal sized parts :math:`a` and 
+    :math:`b` along the given dimension and then compute as following:
+
+        .. math::
+
+            {GLU}(a, b)= a \otimes \sigma(b)
+
+    Refer to `Language Modeling with Gated Convolutional Networks 
+    <https://arxiv.org/pdf/1612.08083.pdf>`_.
+    
+    Args:
+        input (Variable): The input variable which is a Tensor or LoDTensor.
+        dim (int): The dimension along which to split. If :math:`dim < 0`, the 
+            dimension to split along is :math:`rank(input) + dim`.
+
+    Returns:
+        Variable: The Tensor variable with half the size of input.
+
+    Examples:
+        .. code-block:: python
+
+            # x is a Tensor variable with shape [3, 6, 9]
+            fluid.nets.glu(input=x, dim=-1)  # shape of output: [3, 3, 9]
+    """
+
+    a, b = layers.split(input, num_or_sections=2, dim=dim)
+    out = layers.elementwise_mul(x=a, y=b)
+    return out

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

@@ -6,8 +6,8 @@ import numpy
 
 class TestReorderLoDTensor(unittest.TestCase):
     num_seq = 5
-    # [name, dim, lod_level] pair indicating data info of source and target
-    data_desc = (['input', 9, 0], ['ref', 5, 1])
+    # [name, shape, lod_level] pair indicating data info of source and target
+    data_desc = (['input', [9], 0], ['ref', [5], 1])
 
     @classmethod
     def setUpClass(cls):
@@ -16,10 +16,10 @@ class TestReorderLoDTensor(unittest.TestCase):
     @classmethod
     def set_program(cls):
         dat = fluid.layers.data(
-            name=cls.data_desc[0][0], shape=[cls.data_desc[0][1]])
+            name=cls.data_desc[0][0], shape=cls.data_desc[0][1])
         dat.stop_gradient = False
         rank_dat = fluid.layers.data(
-            name=cls.data_desc[1][0], shape=[cls.data_desc[1][1]])
+            name=cls.data_desc[1][0], shape=cls.data_desc[1][1])
         table = fluid.layers.lod_rank_table(rank_dat)
         new_dat = fluid.layers.reorder_lod_tensor_by_rank(
             x=dat, rank_table=table)
@@ -49,7 +49,7 @@ class TestReorderLoDTensor(unittest.TestCase):
         self.data = {}
         for desc in self.data_desc:
             data_name = desc[0]
-            data_dim = desc[1]
+            data_shape = desc[1]
             data_lod_level = desc[2]
             data_lod = []
             for i in range(data_lod_level):
@@ -59,9 +59,9 @@ class TestReorderLoDTensor(unittest.TestCase):
                     size=self.num_seq if i == 0 else lod_level_i[-1])
                 lod_level_i = [0] + numpy.cumsum(lod_level_i).tolist()
                 data_lod.append(lod_level_i)
-            data_value = numpy.random.random(size=[
-                data_lod[-1][-1] if data_lod else self.num_seq, data_dim
-            ]).astype('float32')
+            data_value = numpy.random.random(
+                size=[data_lod[-1][-1] if data_lod else self.num_seq
+                      ] + data_shape).astype('float32')
             self.data[data_name] = (data_value, data_lod)
 
     def set_inputs(self, place):
@@ -163,8 +163,6 @@ class TestReorderLoDTensor(unittest.TestCase):
                 numpy.allclose(
                     numpy.array(actual_grad), expect_grad, atol=0.001))
             self.assertEqual(expect_grad_lod, actual_grad.lod())
-        global outputs_from_tensor_implicit_lod
-        outputs_from_tensor_implicit_lod = self.actual_outputs
 
         # compare outputs between LodTensors with explicit and implicit lod
         # use the same data but set the input lod explicitly