feature/nmt add encoder (#6323)

* init nmt

* encoder ready

* only generation implementation

waiting for dynamic rnn ready to train

* init python

* remove decoder temporary

* clean

* clean

python/paddle/v2/fluid/tests/book/test_machine_translation.py

+import numpy as np
+import paddle.v2 as paddle
+import paddle.v2.dataset.conll05 as conll05
+import paddle.v2.fluid.core as core
+import paddle.v2.fluid.framework as framework
+import paddle.v2.fluid.layers as layers
+from paddle.v2.fluid.executor import Executor, g_scope
+from paddle.v2.fluid.optimizer import SGDOptimizer
+import paddle.v2.fluid as fluid
+import paddle.v2.fluid.layers as pd
+
+dict_size = 30000
+source_dict_dim = target_dict_dim = dict_size
+src_dict, trg_dict = paddle.dataset.wmt14.get_dict(dict_size)
+hidden_dim = 512
+word_dim = 512
+IS_SPARSE = True
+batch_size = 50
+max_length = 50
+topk_size = 50
+trg_dic_size = 10000
+
+src_word_id = layers.data(name="src_word_id", shape=[1], dtype='int64')
+src_embedding = layers.embedding(
+    input=src_word_id,
+    size=[dict_size, word_dim],
+    dtype='float32',
+    is_sparse=IS_SPARSE,
+    param_attr=fluid.ParamAttr(name='vemb'))
+
+
+def encoder():
+
+    lstm_hidden0, lstm_0 = layers.dynamic_lstm(
+        input=src_embedding,
+        size=hidden_dim,
+        candidate_activation='sigmoid',
+        cell_activation='sigmoid')
+
+    lstm_hidden1, lstm_1 = layers.dynamic_lstm(
+        input=src_embedding,
+        size=hidden_dim,
+        candidate_activation='sigmoid',
+        cell_activation='sigmoid',
+        is_reverse=True)
+
+    bidirect_lstm_out = layers.concat([lstm_hidden0, lstm_hidden1], axis=0)
+
+    return bidirect_lstm_out
+
+
+def decoder_trainer(context):
+    '''
+    decoder with trainer
+    '''
+    pass
+
+
+def to_lodtensor(data, place):
+    seq_lens = [len(seq) for seq in data]
+    cur_len = 0
+    lod = [cur_len]
+    for l in seq_lens:
+        cur_len += l
+        lod.append(cur_len)
+    flattened_data = np.concatenate(data, axis=0).astype("int64")
+    flattened_data = flattened_data.reshape([len(flattened_data), 1])
+    res = core.LoDTensor()
+    res.set(flattened_data, place)
+    res.set_lod([lod])
+    return res
+
+
+def main():
+    encoder_out = encoder()
+    # TODO(jacquesqiao) call here
+    decoder_trainer(encoder_out)
+
+    train_data = paddle.batch(
+        paddle.reader.shuffle(
+            paddle.dataset.wmt14.train(8000), buf_size=1000),
+        batch_size=batch_size)
+
+    place = core.CPUPlace()
+    exe = Executor(place)
+
+    exe.run(framework.default_startup_program())
+
+    batch_id = 0
+    for pass_id in xrange(2):
+        print 'pass_id', pass_id
+        for data in train_data():
+            print 'batch', batch_id
+            batch_id += 1
+            if batch_id > 10: break
+            word_data = to_lodtensor(map(lambda x: x[0], data), place)
+            outs = exe.run(framework.default_main_program(),
+                           feed={'src_word_id': word_data, },
+                           fetch_list=[encoder_out])
+
+
+if __name__ == '__main__':
+    main()