expose h0 in dynamic_lstm (#11391)

* expose h0 in dynamic_lstm

* update by comment

* update by comment

* h0 to H0

python/paddle/fluid/layers/nn.py

@@ -261,9 +261,10 @@ def embedding(input,
     return tmp
 
 
-# TODO(qijun): expose H0 and C0
 def dynamic_lstm(input,
                  size,
+                 h_0=None,
+                 c_0=None,
                  param_attr=None,
                  bias_attr=None,
                  use_peepholes=True,
@@ -324,6 +325,13 @@ def dynamic_lstm(input,
                          (T X 4D), where T is the total time steps in this
                          mini-batch, D is the hidden size.
         size(int): 4 * hidden size.
+        h_0(Variable): The initial hidden state is an optional input, default is zero.
+                       This is a tensor with shape (N x D), where N is the
+                       batch size and D is the hidden size.
+        c_0(Variable): The initial cell state is an optional input, default is zero.
+                       This is a tensor with shape (N x D), where N is the
+                       batch size. `h_0` and `c_0` can be NULL but only at the same time.
+
         param_attr(ParamAttr|None): The parameter attribute for the learnable
                                hidden-hidden weights.
 
@@ -387,12 +395,20 @@ def dynamic_lstm(input,
     cell = helper.create_tmp_variable(dtype)
     batch_gate = helper.create_tmp_variable(dtype)
     batch_cell_pre_act = helper.create_tmp_variable(dtype)
+    inputs = {'Input': input, 'Weight': weight, 'Bias': bias}
+    batch_size = input.shape[0]
+    if h_0:
+        assert h_0.shape == (batch_size, size), \
+            'The shape of h0 should be (batch_size, %d)' % size
+        inputs['H0'] = h_0
+    if c_0:
+        assert c_0.shape == (batch_size, size), \
+            'The shape of c0 should be (batch_size, %d)' % size
+        inputs['C0'] = c_0
 
     helper.append_op(
         type='lstm',
-        inputs={'Input': input,
-                'Weight': weight,
-                'Bias': bias},
+        inputs=inputs,
         outputs={
             'Hidden': hidden,
             'Cell': cell,
@@ -677,11 +693,13 @@ def dynamic_gru(input,
         attr=helper.param_attr, shape=[size, 3 * size], dtype=dtype)
     bias = helper.create_parameter(
         attr=helper.bias_attr, shape=[1, 3 * size], dtype=dtype, is_bias=True)
+    batch_size = input.shape[0]
     inputs = {'Input': input, 'Weight': weight, 'Bias': bias}
     if h_0 != None:
         assert h_0.shape == (
-            size, size), 'The shape of h0 should be(%d, %d)' % (size, size)
-        inputs['h0'] = h_0
+            batch_size, size
+        ), 'The shape of h0 should be(batch_size, %d)' % size
+        inputs['H0'] = h_0
 
     hidden = helper.create_tmp_variable(dtype)
     batch_gate = helper.create_tmp_variable(dtype)