add v2 demo to seqtoseq, fix __dfs_travel__ for v2 layers

demo/seqToseq/api_train_v2.py

+import os
+
+import paddle.v2 as paddle
+
+from seqToseq_net_v2 import seqToseq_net_v2
+
+### Data Definiation
+data_dir = "./data/pre-wmt14"
+src_lang_dict = os.path.join(data_dir, 'src.dict')
+trg_lang_dict = os.path.join(data_dir, 'trg.dict')
+
+source_dict_dim = len(open(src_lang_dict, "r").readlines())
+target_dict_dim = len(open(trg_lang_dict, "r").readlines())
+
+
+def read_to_dict(dict_path):
+    with open(dict_path, "r") as fin:
+        out_dict = {
+            line.strip(): line_count
+            for line_count, line in enumerate(fin)
+        }
+    return out_dict
+
+
+src_dict = read_to_dict(src_lang_dict)
+trg_dict = read_to_dict(trg_lang_dict)
+
+train_list = os.path.join(data_dir, 'train.list')
+test_list = os.path.join(data_dir, 'test.list')
+
+UNK_IDX = 2
+START = "<s>"
+END = "<e>"
+
+
+def _get_ids(s, dictionary):
+    words = s.strip().split()
+    return [dictionary[START]] + \
+           [dictionary.get(w, UNK_IDX) for w in words] + \
+           [dictionary[END]]
+
+
+def train_reader(file_name):
+    def reader():
+        with open(file_name, 'r') as f:
+            for line_count, line in enumerate(f):
+                line_split = line.strip().split('\t')
+                if len(line_split) != 2:
+                    continue
+                src_seq = line_split[0]  # one source sequence
+                src_ids = _get_ids(src_seq, src_dict)
+
+                trg_seq = line_split[1]  # one target sequence
+                trg_words = trg_seq.split()
+                trg_ids = [trg_dict.get(w, UNK_IDX) for w in trg_words]
+
+                # remove sequence whose length > 80 in training mode
+                if len(src_ids) > 80 or len(trg_ids) > 80:
+                    continue
+                trg_ids_next = trg_ids + [trg_dict[END]]
+                trg_ids = [trg_dict[START]] + trg_ids
+
+                yield src_ids, trg_ids, trg_ids_next
+
+    return reader
+
+
+def main():
+    paddle.init(use_gpu=False, trainer_count=1)
+
+    # reader = train_reader("data/pre-wmt14/train/train")
+    # define network topology
+    cost = seqToseq_net_v2(source_dict_dim, target_dict_dim)
+    parameters = paddle.parameters.create(cost)
+    optimizer = paddle.optimizer.Adam(batch_size=50, learning_rate=5e-4)
+
+    def event_handler(event):
+        if isinstance(event, paddle.event.EndIteration):
+            if event.batch_id % 100 == 0:
+                print "Pass %d, Batch %d, Cost %f, %s" % (
+                    event.pass_id, event.batch_id, event.cost, event.metrics)
+
+    trainer = paddle.trainer.SGD(cost=cost,
+                                 parameters=parameters,
+                                 update_equation=optimizer)
+
+    reader_dict = {
+        'source_language_word': 0,
+        'target_language_word': 1,
+        'target_language_next_word': 2
+    }
+
+    trn_reader = paddle.reader.batched(
+        paddle.reader.shuffle(
+            train_reader("data/pre-wmt14/train/train"), buf_size=8192),
+        batch_size=10)
+
+    trainer.train(
+        reader=trn_reader,
+        event_handler=event_handler,
+        num_passes=10000,
+        reader_dict=reader_dict)
+
+
+if __name__ == '__main__':
+    main()

demo/seqToseq/seqToseq_net_v2.py

+import paddle.v2.activation as activation
+import paddle.v2.attr as attr
+import paddle.v2.data_type as data_type
+import paddle.v2.layer as layer
+import paddle.v2.networks as networks
+
+
+def seqToseq_net_v2(source_dict_dim, target_dict_dim):
+    ### Network Architecture
+    word_vector_dim = 512  # dimension of word vector
+    decoder_size = 512  # dimension of hidden unit in GRU Decoder network
+    encoder_size = 512  # dimension of hidden unit in GRU Encoder network
+
+    #### Encoder
+    src_word_id = layer.data(
+        name='source_language_word',
+        type=data_type.dense_vector(source_dict_dim))
+    src_embedding = layer.embedding(
+        input=src_word_id,
+        size=word_vector_dim,
+        param_attr=attr.ParamAttr(name='_source_language_embedding'))
+    src_forward = networks.simple_gru(input=src_embedding, size=encoder_size)
+    src_backward = networks.simple_gru(
+        input=src_embedding, size=encoder_size, reverse=True)
+    encoded_vector = layer.concat(input=[src_forward, src_backward])
+
+    #### Decoder
+    with layer.mixed(size=decoder_size) as encoded_proj:
+        encoded_proj += layer.full_matrix_projection(input=encoded_vector)
+
+    backward_first = layer.first_seq(input=src_backward)
+
+    with layer.mixed(size=decoder_size, act=activation.Tanh()) as decoder_boot:
+        decoder_boot += layer.full_matrix_projection(input=backward_first)
+
+    def gru_decoder_with_attention(enc_vec, enc_proj, current_word):
+
+        decoder_mem = layer.memory(
+            name='gru_decoder', size=decoder_size, boot_layer=decoder_boot)
+
+        context = networks.simple_attention(
+            encoded_sequence=enc_vec,
+            encoded_proj=enc_proj,
+            decoder_state=decoder_mem)
+
+        with layer.mixed(size=decoder_size * 3) as decoder_inputs:
+            decoder_inputs += layer.full_matrix_projection(input=context)
+            decoder_inputs += layer.full_matrix_projection(input=current_word)
+
+        gru_step = layer.gru_step(
+            name='gru_decoder',
+            input=decoder_inputs,
+            output_mem=decoder_mem,
+            size=decoder_size)
+
+        with layer.mixed(
+                size=target_dict_dim, bias_attr=True,
+                act=activation.Softmax()) as out:
+            out += layer.full_matrix_projection(input=gru_step)
+        return out
+
+    decoder_group_name = "decoder_group"
+    group_input1 = layer.StaticInputV2(input=encoded_vector, is_seq=True)
+    group_input2 = layer.StaticInputV2(input=encoded_proj, is_seq=True)
+    group_inputs = [group_input1, group_input2]
+
+    trg_embedding = layer.embedding(
+        input=layer.data(
+            name='target_language_word',
+            type=data_type.dense_vector(target_dict_dim)),
+        size=word_vector_dim,
+        param_attr=attr.ParamAttr(name='_target_language_embedding'))
+    group_inputs.append(trg_embedding)
+
+    # For decoder equipped with attention mechanism, in training,
+    # target embeding (the groudtruth) is the data input,
+    # while encoded source sequence is accessed to as an unbounded memory.
+    # Here, the StaticInput defines a read-only memory
+    # for the recurrent_group.
+    decoder = layer.recurrent_group(
+        name=decoder_group_name,
+        step=gru_decoder_with_attention,
+        input=group_inputs)
+
+    lbl = layer.data(
+        name='target_language_next_word',
+        type=data_type.dense_vector(target_dict_dim))
+    cost = layer.classification_cost(input=decoder, label=lbl)
+
+    return cost

python/paddle/v2/layer.py

@@ -262,7 +262,7 @@ class StaticInputV2(object):
         self.input = input
         self.is_seq = is_seq
         self.size = size
-        # TODO(qiaolongfei): add size
+        # TODO(add size check)
         # assert input.size is not None or size is not None
 
 

python/paddle/v2/topology.py

@@ -17,6 +17,7 @@ import collections
 from paddle.proto.ModelConfig_pb2 import ModelConfig
 
 import layer as v2_layer
+from layer import WithExtraParent
 
 __all__ = ['Topology']
 
@@ -40,7 +41,10 @@ def __bfs_travel__(callback, *layers):
         __break__ = callback(each_layer)
         if __break__:
             return
-        __bfs_travel__(callback, *each_layer.__parent_layers__.values())
+        __layers__ = each_layer.__parent_layers__.values()
+        if isinstance(each_layer, WithExtraParent):
+            __layers__ = __layers__ + each_layer.extra_parent()
+        __bfs_travel__(callback, *__layers__)
 
 
 class Topology(object):