add gru unit layer wrapper (#6325)

Author: Superjomn

python/paddle/v2/fluid/layers.py

@@ -180,6 +180,77 @@ def dynamic_lstm(input,
  return hidden, cell
 
 
+def gru_unit(input,
+ hidden,
+ size,
+ weight=None,
+ bias=None,
+ activation='tanh',
+ gate_activation='sigmoid',
+ main_program=None,
+ startup_program=None):
+ """
+ GRUUnit Operator implements partial calculations of the GRU unit as following:
+
+ $$
+ update \ gate: u_t = actGate(xu_t + W_u * h_{t-1} + b_u) \\
+ reset \ gate: r_t = actGate(xr_t + W_r * h_{t-1} + b_r) \\
+ output \ candidate: {h}_t = actNode(xc_t + W_c * dot(r_t, h_{t-1}) + b_c) \\
+ output: h_t = dot((1 - u_t), h_{t-1}) + dot(u_t, {h}_t)
+ $$
+
+ which is same as one time step of GRU Operator.
+
+ @note To implement the complete GRU unit, fully-connected operator must be
+ used before to feed xu, xr and xc as the Input of GRUUnit operator.
+
+ TODO(ChunweiYan) add more document here
+ """
+ activation_dict = dict(
+ identity=0,
+ sigmoid=1,
+ tanh=2,
+ relu=3, )
+ activation = activation_dict[activation]
+ gate_activation = activation_dict[gate_activation]
+
+ helper = LayerHelper('gru_unit', **locals())
+ dtype = helper.input_dtype()
+ size = size / 3
+
+ # create weight
+ if weight is None:
+ weight = helper.create_parameter(
+ attr=helper.param_attr, shape=[size, 3 * size], dtype=dtype)
+
+ # create bias
+ if bias is None:
+ bias_size = [1, 3 * size]
+ bias = helper.create_parameter(
+ attr=helper.bias_attr, shape=bias_size, dtype=dtype, is_bias=True)
+
+ gate = helper.create_tmp_variable(dtype)
+ reset_hidden_pre = helper.create_tmp_variable(dtype)
+ updated_hidden = helper.create_tmp_variable(dtype)
+
+ helper.append_op(
+ type='gru_unit',
+ inputs={'Input': input,
+ 'HiddenPrev': hidden,
+ 'Weight': weight},
+ outputs={
+ 'Gate': gate,
+ 'ResetHiddenPrev': reset_hidden_pre,
+ 'Hidden': updated_hidden,
+ },
+ attrs={
+ 'activation': 0,
+ 'gate_activation': 1,
+ })
+
+ return updated_hidden, reset_hidden_pre, gate
+
+
 def data(name,
  shape,
  append_batch_size=True,