Add python wrapper for lstm unit op.

doc/api/v2/fluid/layers.rst

@@ -188,12 +188,6 @@ beam_search_decode
     :noindex:
 
 
-lstm
----------
-..  autofunction:: paddle.v2.fluid.layers.lstm
-    :noindex:
-
-
 lod_rank_table
 ---------
 ..  autofunction:: paddle.v2.fluid.layers.lod_rank_table
@@ -300,3 +294,8 @@ conv2d_transpose
 ..  autofunction:: paddle.v2.fluid.layers.conv2d_transpose
     :noindex:
 
+
+lstm_unit
+---------
+..  autofunction:: paddle.v2.fluid.layers.lstm_unit
+    :noindex:

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

@@ -5,12 +5,13 @@ All layers just related to the neural network.
 from ..layer_helper import LayerHelper
 from ..initializer import Normal, Constant
 from ..framework import Variable
+from tensor import concat
 
 __all__ = [
     'fc', 'embedding', 'dynamic_lstm', 'gru_unit', 'linear_chain_crf',
     'crf_decoding', 'cos_sim', 'cross_entropy', 'square_error_cost', 'accuracy',
     'chunk_eval', 'sequence_conv', 'conv2d', 'sequence_pool', 'pool2d',
-    'batch_norm', 'beam_search_decode', 'conv2d_transpose'
+    'batch_norm', 'beam_search_decode', 'conv2d_transpose', 'lstm_unit'
 ]
 
 
@@ -392,7 +393,7 @@ def chunk_eval(input,
                excluded_chunk_types=None,
                **kwargs):
     """
-    This function computes and outputs the precision, recall and 
+    This function computes and outputs the precision, recall and
     F1-score of chunk detection.
     """
     helper = LayerHelper("chunk_eval", **kwargs)
@@ -789,3 +790,110 @@ def conv2d_transpose(input,
         attrs=op_attr)
 
     return out
+
+
+def lstm_unit(x_t,
+              hidden_t_prev,
+              cell_t_prev,
+              forget_bias=0.0,
+              main_program=None,
+              startup_program=None):
+    """Lstm unit layer. The equation of a lstm step is:
+
+        .. math::
+
+            i_t & = \sigma(W_{x_i}x_{t} + W_{h_i}h_{t-1} + W_{c_i}c_{t-1} + b_i)
+
+            f_t & = \sigma(W_{x_f}x_{t} + W_{h_f}h_{t-1} + W_{c_f}c_{t-1} + b_f)
+
+            c_t & = f_tc_{t-1} + i_t tanh (W_{x_c}x_t+W_{h_c}h_{t-1} + b_c)
+
+            o_t & = \sigma(W_{x_o}x_{t} + W_{h_o}h_{t-1} + W_{c_o}c_t + b_o)
+
+            h_t & = o_t tanh(c_t)
+
+    The inputs of lstm unit includes :math:`x_t`, :math:`h_{t-1}` and
+    :math:`c_{t-1}`. The implementation separates the linear transformation
+    and non-linear transformation apart. Here, we take :math:`i_t` as an
+    example. The linear transformation is applied by calling a `fc` layer and
+    the equation is:
+
+        .. math::
+
+            L_{i_t} = W_{x_i}x_{t} + W_{h_i}h_{t-1} + W_{c_i}c_{t-1} + b_i
+
+    The non-linear transformation is applied by calling `lstm_unit_op` and the
+    equation is:
+
+        .. math::
+
+            i_t = \sigma(L_{i_t})
+
+    This layer has two outputs including :math:`o_t` and :math:`h_t`.
+
+    Args:
+        x_t (Variable): The input value of current step.
+        hidden_t_prev (Variable): The hidden value of lstm unit.
+        cell_t_prev (Variable): The cell value of lstm unit.
+        forget_bias (float): The forget bias of lstm unit.
+        main_program (Program): The main program.
+        startup_program (Program): the startup program.
+
+    Returns:
+        tuple: The cell value and hidden value of lstm unit.
+
+    Raises:
+        ValueError: The ranks of **x_t**, **hidden_t_prev** and **cell_t_prev**\
+                not be 2 or the 1st dimensions of **x_t**, **hidden_t_prev** \
+                and **cell_t_prev** not be the same.
+
+    Examples:
+
+        .. code-block:: python
+
+             x_t = fluid.layers.fc(input=x_t_data, size=10)
+             prev_hidden = fluid.layers.fc(input=prev_hidden_data, size=20)
+             prev_cell = fluid.layers.fc(input=prev_cell_data, size=30)
+             cell_value, hidden_value = fluid.layers.lstm_unit(x_t=x_t,
+                                                    hidden_t_prev=prev_hidden,
+                                                    cell_t_prev=prev_cell)
+    """
+    helper = LayerHelper('lstm_unit', **locals())
+
+    if len(x_t.shape) != 2:
+        raise ValueError("Rank of x_t must be 2.")
+
+    if len(hidden_t_prev.shape) != 2:
+        raise ValueError("Rank of hidden_t_prev must be 2.")
+
+    if len(cell_t_prev.shape) != 2:
+        raise ValueError("Rank of cell_t_prev must be 2.")
+
+    if x_t.shape[0] != hidden_t_prev.shape[0] or x_t.shape[
+            0] != cell_t_prev.shape[0]:
+        raise ValueError("The 1s dimension of x_t, hidden_t_prev and "
+                         "cell_t_prev must be the same.")
+
+    size = cell_t_prev.shape[1]
+    concat_out = concat(
+        input=[x_t, hidden_t_prev],
+        axis=1,
+        main_program=main_program,
+        startup_program=startup_program)
+    fc_out = fc(input=concat_out,
+                size=4 * size,
+                main_program=main_program,
+                startup_program=startup_program)
+    dtype = x_t.dtype
+    c = helper.create_tmp_variable(dtype)
+    h = helper.create_tmp_variable(dtype)
+
+    helper.append_op(
+        type='lstm_unit',
+        inputs={"X": fc_out,
+                "C_prev": cell_t_prev},
+        outputs={"C": c,
+                 "H": h},
+        attrs={"forget_bias": forget_bias})
+
+    return c, h

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

@@ -161,6 +161,23 @@ class TestBook(unittest.TestCase):
                     x=dat, label=lbl))
         print(str(program))
 
+    def test_lstm_unit(self):
+        program = Program()
+        with program_guard(program):
+            x_t_data = layers.data(
+                name='x_t_data', shape=[10, 10], dtype='float32')
+            x_t = layers.fc(input=x_t_data, size=10)
+            prev_hidden_data = layers.data(
+                name='prev_hidden_data', shape=[10, 20], dtype='float32')
+            prev_hidden = layers.fc(input=prev_hidden_data, size=20)
+            prev_cell_data = layers.data(
+                name='prev_cell', shape=[10, 30], dtype='float32')
+            prev_cell = layers.fc(input=prev_cell_data, size=30)
+            self.assertIsNotNone(
+                layers.lstm_unit(
+                    x_t=x_t, hidden_t_prev=prev_hidden, cell_t_prev=prev_cell))
+        print(str(program))
+
 
 if __name__ == '__main__':
     unittest.main()