cherry-pick 1.6 to develop (#4004)

PaddleRec/gru4rec/README.md

@@ -45,6 +45,9 @@ session-based推荐应用场景非常广泛，比如用户的商品浏览、新
 
 运行样例程序可跳过'RSC15 数据下载及预处理'部分
 
+
+**要求使用PaddlePaddle 1.6及以上版本或适当的develop版本。**
+
 同时推荐用户参考[ IPython Notebook demo](https://aistudio.baidu.com/aistudio/projectDetail/122296)
 
 ## RSC15 数据下载及预处理
@@ -278,7 +281,7 @@ model:model_r@20/epoch_10 recall@20:0.681 time_cost(s):12.2
 
 可参考cluster_train.py 配置其他多机环境
 
-运行命令本地模拟多机场景
+运行命令本地模拟多机场景, 暂不支持windows
 ```
 sh cluster_train.sh
 ```

PaddleRec/gru4rec/convert_format.py

@@ -2,8 +2,8 @@ import sys
 
 
 def convert_format(input, output):
-    with open(input) as rf:
-        with open(output, "w") as wf:
+    with open(input, "r", encoding='utf-8') as rf:
+        with open(output, "w", encoding='utf-8') as wf:
             last_sess = -1
             sign = 1
             i = 0

PaddleRec/gru4rec/infer.py

@@ -71,6 +71,7 @@ def infer(test_reader, use_cuda, model_path):
 
 
 if __name__ == "__main__":
+    utils.check_version()
     args = parse_args()
     start_index = args.start_index
     last_index = args.last_index

PaddleRec/gru4rec/infer_sample_neg.py

@@ -84,6 +84,7 @@ def infer(args, vocab_size, test_reader, use_cuda):
 
 
 if __name__ == "__main__":
+    utils.check_version()
     args = parse_args()
     start_index = args.start_index
     last_index = args.last_index

PaddleRec/gru4rec/net.py

@@ -10,12 +10,12 @@ def all_vocab_network(vocab_size,
     gru_lr_x = 1.0
     fc_lr_x = 1.0
     # Input data
-    src_wordseq = fluid.layers.data(
-        name="src_wordseq", shape=[1], dtype="int64", lod_level=1)
-    dst_wordseq = fluid.layers.data(
-        name="dst_wordseq", shape=[1], dtype="int64", lod_level=1)
+    src_wordseq = fluid.data(
+        name="src_wordseq", shape=[None, 1], dtype="int64", lod_level=1)
+    dst_wordseq = fluid.data(
+        name="dst_wordseq", shape=[None, 1], dtype="int64", lod_level=1)
 
-    emb = fluid.layers.embedding(
+    emb = fluid.embedding(
         input=src_wordseq,
         size=[vocab_size, hid_size],
         param_attr=fluid.ParamAttr(
@@ -56,19 +56,20 @@ def train_bpr_network(vocab_size, neg_size, hid_size, drop_out=0.2):
     gru_lr_x = 1.0
     fc_lr_x = 1.0
     # Input data
-    src = fluid.layers.data(name="src", shape=[1], dtype="int64", lod_level=1)
-    pos_label = fluid.layers.data(
-        name="pos_label", shape=[1], dtype="int64", lod_level=1)
-    label = fluid.layers.data(
-        name="label", shape=[neg_size + 1], dtype="int64", lod_level=1)
+    src = fluid.data(name="src", shape=[None, 1], dtype="int64", lod_level=1)
+    pos_label = fluid.data(
+        name="pos_label", shape=[None, 1], dtype="int64", lod_level=1)
+    label = fluid.data(
+        name="label", shape=[None, neg_size + 1], dtype="int64", lod_level=1)
 
-    emb_src = fluid.layers.embedding(
+    emb_src = fluid.embedding(
         input=src,
         size=[vocab_size, hid_size],
         param_attr=fluid.ParamAttr(
             name="emb",
             initializer=fluid.initializer.XavierInitializer(),
             learning_rate=emb_lr_x))
+    emb_src = fluid.layers.squeeze(input=emb_src, axes=[1])
 
     emb_src_drop = fluid.layers.dropout(emb_src, dropout_prob=drop_out)
 
@@ -90,7 +91,7 @@ def train_bpr_network(vocab_size, neg_size, hid_size, drop_out=0.2):
     gru_h0_drop = fluid.layers.dropout(gru_h0, dropout_prob=drop_out)
 
     label_re = fluid.layers.sequence_reshape(input=label, new_dim=1)
-    emb_label = fluid.layers.embedding(
+    emb_label1 = fluid.embedding(
         input=label_re,
         size=[vocab_size, hid_size],
         param_attr=fluid.ParamAttr(
@@ -98,6 +99,7 @@ def train_bpr_network(vocab_size, neg_size, hid_size, drop_out=0.2):
             initializer=fluid.initializer.XavierInitializer(),
             learning_rate=emb_lr_x))
 
+    emb_label = fluid.layers.squeeze(input=emb_label1, axes=[1])
     emb_label_drop = fluid.layers.dropout(emb_label, dropout_prob=drop_out)
 
     gru_exp = fluid.layers.expand(
@@ -120,19 +122,20 @@ def train_cross_entropy_network(vocab_size, neg_size, hid_size, drop_out=0.2):
     gru_lr_x = 1.0
     fc_lr_x = 1.0
     # Input data
-    src = fluid.layers.data(name="src", shape=[1], dtype="int64", lod_level=1)
-    pos_label = fluid.layers.data(
-        name="pos_label", shape=[1], dtype="int64", lod_level=1)
-    label = fluid.layers.data(
-        name="label", shape=[neg_size + 1], dtype="int64", lod_level=1)
+    src = fluid.data(name="src", shape=[None, 1], dtype="int64", lod_level=1)
+    pos_label = fluid.data(
+        name="pos_label", shape=[None, 1], dtype="int64", lod_level=1)
+    label = fluid.data(
+        name="label", shape=[None, neg_size + 1], dtype="int64", lod_level=1)
 
-    emb_src = fluid.layers.embedding(
+    emb_src = fluid.embedding(
         input=src,
         size=[vocab_size, hid_size],
         param_attr=fluid.ParamAttr(
             name="emb",
             initializer=fluid.initializer.XavierInitializer(),
             learning_rate=emb_lr_x))
+    emb_src = fluid.layers.squeeze(input=emb_src, axes=[1])
 
     emb_src_drop = fluid.layers.dropout(emb_src, dropout_prob=drop_out)
 
@@ -154,13 +157,14 @@ def train_cross_entropy_network(vocab_size, neg_size, hid_size, drop_out=0.2):
     gru_h0_drop = fluid.layers.dropout(gru_h0, dropout_prob=drop_out)
 
     label_re = fluid.layers.sequence_reshape(input=label, new_dim=1)
-    emb_label = fluid.layers.embedding(
+    emb_label1 = fluid.embedding(
         input=label_re,
         size=[vocab_size, hid_size],
         param_attr=fluid.ParamAttr(
             name="emb",
             initializer=fluid.initializer.XavierInitializer(),
             learning_rate=emb_lr_x))
+    emb_label = fluid.layers.squeeze(input=emb_label1, axes=[1])
 
     emb_label_drop = fluid.layers.dropout(emb_label, dropout_prob=drop_out)
 
@@ -180,8 +184,8 @@ def train_cross_entropy_network(vocab_size, neg_size, hid_size, drop_out=0.2):
 
 
 def infer_network(vocab_size, batch_size, hid_size, dropout=0.2):
-    src = fluid.layers.data(name="src", shape=[1], dtype="int64", lod_level=1)
-    emb_src = fluid.layers.embedding(
+    src = fluid.data(name="src", shape=[None, 1], dtype="int64", lod_level=1)
+    emb_src = fluid.embedding(
         input=src, size=[vocab_size, hid_size], param_attr="emb")
     emb_src_drop = fluid.layers.dropout(
         emb_src, dropout_prob=dropout, is_test=True)
@@ -198,20 +202,18 @@ def infer_network(vocab_size, batch_size, hid_size, dropout=0.2):
     gru_h0_drop = fluid.layers.dropout(
         gru_h0, dropout_prob=dropout, is_test=True)
 
-    all_label = fluid.layers.data(
-        name="all_label",
-        shape=[vocab_size, 1],
-        dtype="int64",
-        append_batch_size=False)
-    emb_all_label = fluid.layers.embedding(
+    all_label = fluid.data(
+        name="all_label", shape=[vocab_size, 1], dtype="int64")
+    emb_all_label = fluid.embedding(
         input=all_label, size=[vocab_size, hid_size], param_attr="emb")
+    emb_all_label = fluid.layers.squeeze(input=emb_all_label, axes=[1])
     emb_all_label_drop = fluid.layers.dropout(
         emb_all_label, dropout_prob=dropout, is_test=True)
 
     all_pre = fluid.layers.matmul(
         gru_h0_drop, emb_all_label_drop, transpose_y=True)
 
-    pos_label = fluid.layers.data(
-        name="pos_label", shape=[1], dtype="int64", lod_level=1)
+    pos_label = fluid.data(
+        name="pos_label", shape=[None, 1], dtype="int64", lod_level=1)
     acc = fluid.layers.accuracy(input=all_pre, label=pos_label, k=20)
     return acc

PaddleRec/gru4rec/text2paddle.py

@@ -2,6 +2,11 @@ import sys
 import six
 import collections
 import os
+import sys
+if six.PY2:
+    reload(sys)
+    sys.setdefaultencoding('utf-8')
+
 
 def word_count(input_file, word_freq=None):
     """
@@ -25,11 +30,11 @@ def build_dict(min_word_freq=0, train_dir="", test_dir=""):
     word_freq = collections.defaultdict(int)
     files = os.listdir(train_dir)
     for fi in files:
-        with open(train_dir + '/' + fi, "r") as f:
+        with open(os.path.join(train_dir, fi), "r", encoding='utf-8') as f:
             word_freq = word_count(f, word_freq)
     files = os.listdir(test_dir)
     for fi in files:
-        with open(test_dir + '/' + fi, "r") as f:
+        with open(os.path.join(test_dir, fi), "r", encoding='utf-8') as f:
             word_freq = word_count(f, word_freq)
 
     word_freq = [x for x in six.iteritems(word_freq) if x[1] > min_word_freq]
@@ -39,13 +44,16 @@ def build_dict(min_word_freq=0, train_dir="", test_dir=""):
     return word_idx
 
 
-def write_paddle(word_idx, train_dir, test_dir, output_train_dir, output_test_dir):
+def write_paddle(word_idx, train_dir, test_dir, output_train_dir,
+                 output_test_dir):
     files = os.listdir(train_dir)
     if not os.path.exists(output_train_dir):
         os.mkdir(output_train_dir)
     for fi in files:
-        with open(train_dir + '/' + fi, "r") as f:
-            with open(output_train_dir + '/' + fi, "w") as wf:
+        with open(os.path.join(train_dir, fi), "r", encoding='utf-8') as f:
+            with open(
+                    os.path.join(output_train_dir, fi), "w",
+                    encoding='utf-8') as wf:
                 for l in f:
                     l = l.strip().split()
                     l = [word_idx.get(w) for w in l]
@@ -57,8 +65,10 @@ def write_paddle(word_idx, train_dir, test_dir, output_train_dir, output_test_di
     if not os.path.exists(output_test_dir):
         os.mkdir(output_test_dir)
     for fi in files:
-        with open(test_dir + '/' + fi, "r") as f:
-            with open(output_test_dir + '/' + fi, "w") as wf:
+        with open(os.path.join(test_dir, fi), "r", encoding='utf-8') as f:
+            with open(
+                    os.path.join(output_test_dir, fi), "w",
+                    encoding='utf-8') as wf:
                 for l in f:
                     l = l.strip().split()
                     l = [word_idx.get(w) for w in l]
@@ -66,9 +76,11 @@ def write_paddle(word_idx, train_dir, test_dir, output_train_dir, output_test_di
                         wf.write(str(w) + " ")
                     wf.write("\n")
 
-def text2paddle(train_dir, test_dir, output_train_dir, output_test_dir, output_vocab):
+
+def text2paddle(train_dir, test_dir, output_train_dir, output_test_dir,
+                output_vocab):
     vocab = build_dict(0, train_dir, test_dir)
-    with open(output_vocab, "w") as wf:
+    with open(output_vocab, "w", encoding='utf-8') as wf:
         wf.write(str(len(vocab)) + "\n")
         #wf.write(str(vocab))
     write_paddle(vocab, train_dir, test_dir, output_train_dir, output_test_dir)
@@ -79,4 +91,5 @@ test_dir = sys.argv[2]
 output_train_dir = sys.argv[3]
 output_test_dir = sys.argv[4]
 output_vocab = sys.argv[5]
-text2paddle(train_dir, test_dir, output_train_dir, output_test_dir, output_vocab)
+text2paddle(train_dir, test_dir, output_train_dir, output_test_dir,
+            output_vocab)

PaddleRec/gru4rec/train.py

@@ -144,8 +144,7 @@ def train():
             gpu_num = device[1]
             print("kpis\teach_pass_duration_gpu%s\t%s" %
                   (gpu_num, total_time / epoch_idx))
-            print("kpis\ttrain_ppl_gpu%s\t%s" %
-                (gpu_num, ce_ppl))
+            print("kpis\ttrain_ppl_gpu%s\t%s" % (gpu_num, ce_ppl))
         else:
             cpu_num = device[1]
             threads_num = device[2]
@@ -169,4 +168,5 @@ def get_device(args):
 
 
 if __name__ == "__main__":
+    utils.check_version()
     train()

PaddleRec/gru4rec/train_sample_neg.py

@@ -128,4 +128,5 @@ def train():
 
 
 if __name__ == "__main__":
+    utils.check_version()
     train()

PaddleRec/gru4rec/utils.py

@@ -6,6 +6,7 @@ import numpy as np
 import paddle.fluid as fluid
 import paddle
 import os
+import io
 
 
 def to_lodtensor(data, place):
@@ -86,7 +87,7 @@ def to_lodtensor_bpr_test(raw_data, vocab_size, place):
 
 
 def get_vocab_size(vocab_path):
-    with open(vocab_path, "r") as rf:
+    with io.open(vocab_path, "r", encoding='utf-8') as rf:
         line = rf.readline()
         return int(line.strip())
 
@@ -110,12 +111,28 @@ def prepare_data(file_dir,
             batch_size * 20)
     else:
         vocab_size = get_vocab_size(vocab_path)
-        reader = paddle.batch(
+        reader = fluid.io.batch(
             test(
                 file_dir, buffer_size, data_type=DataType.SEQ), batch_size)
     return vocab_size, reader
 
 
+def check_version():
+    """
+     Log error and exit when the installed version of paddlepaddle is
+     not satisfied.
+     """
+    err = "PaddlePaddle version 1.6 or higher is required, " \
+          "or a suitable develop version is satisfied as well. \n" \
+          "Please make sure the version is good with your code." \
+
+    try:
+        fluid.require_version('1.6.0')
+    except Exception as e:
+        logger.error(err)
+        sys.exit(1)
+
+
 def sort_batch(reader, batch_size, sort_group_size, drop_last=False):
     """
     Create a batched reader.
@@ -170,7 +187,8 @@ def reader_creator(file_dir, n, data_type):
     def reader():
         files = os.listdir(file_dir)
         for fi in files:
-            with open(file_dir + '/' + fi, "r") as f:
+            with io.open(
+                    os.path.join(file_dir, fi), "r", encoding='utf-8') as f:
                 for l in f:
                     if DataType.SEQ == data_type:
                         l = l.strip().split()

PaddleRec/multiview_simnet/README.md

@@ -3,6 +3,8 @@
 ## Introduction
 In personalized recommendation scenario, a user often is provided with several items from personalized interest matching model. In real world application, a user may have multiple views of features, say user-id, age, click-history of items, search queries. A item, e.g. news, may also have multiple views of features like news title, news category, images in news and so on. Multi-view Simnet is matching a model that combine users' and items' multiple views of features into one unified model. The model can be used in many industrial product like Baidu's feed news. The model is adapted from the paper A Multi-View Deep Learning(MV-DNN) Approach for Cross Domain User Modeling in Recommendation Systems, WWW 2015. The difference between our model and the MV-DNN is that we also consider multiple feature views of users.
 
+**Now all models in PaddleRec require PaddlePaddle version 1.6 or higher, or suitable develop version.**
+
 We also recommend users to take a look at the [IPython Notebook demo](https://aistudio.baidu.com/aistudio/projectDetail/122294)
 
 ## Dataset

PaddleRec/multiview_simnet/infer.py

@@ -31,6 +31,22 @@ logger = logging.getLogger("fluid")
 logger.setLevel(logging.INFO)
 
 
+def check_version():
+    """
+     Log error and exit when the installed version of paddlepaddle is
+     not satisfied.
+     """
+    err = "PaddlePaddle version 1.6 or higher is required, " \
+          "or a suitable develop version is satisfied as well. \n" \
+          "Please make sure the version is good with your code." \
+
+    try:
+        fluid.require_version('1.6.0')
+    except Exception as e:
+        logger.error(err)
+        sys.exit(1)
+
+
 def parse_args():
     parser = argparse.ArgumentParser("multi-view simnet")
     parser.add_argument("--train_file", type=str, help="Training file")
@@ -116,4 +132,5 @@ def main():
 
 
 if __name__ == "__main__":
+    check_version()
     main()

PaddleRec/multiview_simnet/nets.py

@@ -55,7 +55,6 @@ class CNNEncoder(object):
             bias_attr=self.param_name + ".bias")
 
 
-
 class GrnnEncoder(object):
     """ grnn-encoder """
 
@@ -64,8 +63,7 @@ class GrnnEncoder(object):
         self.hidden_size = hidden_size
 
     def forward(self, emb):
-        fc0 = nn.fc(
-            input=emb, 
+        fc0 = nn.fc(input=emb,
                     size=self.hidden_size * 3,
                     param_attr=self.param_name + "_fc.w",
                     bias_attr=False)
@@ -125,34 +123,34 @@ class MultiviewSimnet(object):
     def train_net(self):
         # input fields for query, pos_title, neg_title
         q_slots = [
-            io.data(
-                name="q%d" % i, shape=[1], lod_level=1, dtype='int64')
+            fluid.data(
+                name="q%d" % i, shape=[None, 1], lod_level=1, dtype='int64')
             for i in range(len(self.query_encoders))
         ]
         pt_slots = [
-            io.data(
-                name="pt%d" % i, shape=[1], lod_level=1, dtype='int64')
+            fluid.data(
+                name="pt%d" % i, shape=[None, 1], lod_level=1, dtype='int64')
             for i in range(len(self.title_encoders))
         ]
         nt_slots = [
-            io.data(
-                name="nt%d" % i, shape=[1], lod_level=1, dtype='int64')
+            fluid.data(
+                name="nt%d" % i, shape=[None, 1], lod_level=1, dtype='int64')
             for i in range(len(self.title_encoders))
         ]
 
         # lookup embedding for each slot
         q_embs = [
-            nn.embedding(
+            fluid.embedding(
                 input=query, size=self.emb_shape, param_attr="emb")
             for query in q_slots
         ]
         pt_embs = [
-            nn.embedding(
+            fluid.embedding(
                 input=title, size=self.emb_shape, param_attr="emb")
             for title in pt_slots
         ]
         nt_embs = [
-            nn.embedding(
+            fluid.embedding(
                 input=title, size=self.emb_shape, param_attr="emb")
             for title in nt_slots
         ]
@@ -174,9 +172,18 @@ class MultiviewSimnet(object):
         nt_concat = nn.concat(nt_encodes)
 
         # projection of hidden layer
-        q_hid = nn.fc(q_concat, size=self.hidden_size, param_attr='q_fc.w', bias_attr='q_fc.b')
-        pt_hid = nn.fc(pt_concat, size=self.hidden_size, param_attr='t_fc.w', bias_attr='t_fc.b')
-        nt_hid = nn.fc(nt_concat, size=self.hidden_size, param_attr='t_fc.w', bias_attr='t_fc.b')
+        q_hid = nn.fc(q_concat,
+                      size=self.hidden_size,
+                      param_attr='q_fc.w',
+                      bias_attr='q_fc.b')
+        pt_hid = nn.fc(pt_concat,
+                       size=self.hidden_size,
+                       param_attr='t_fc.w',
+                       bias_attr='t_fc.b')
+        nt_hid = nn.fc(nt_concat,
+                       size=self.hidden_size,
+                       param_attr='t_fc.w',
+                       bias_attr='t_fc.b')
 
         # cosine of hidden layers
         cos_pos = nn.cos_sim(q_hid, pt_hid)
@@ -205,23 +212,23 @@ class MultiviewSimnet(object):
 
     def pred_net(self, query_fields, pos_title_fields, neg_title_fields):
         q_slots = [
-            io.data(
-                name="q%d" % i, shape=[1], lod_level=1, dtype='int64')
+            fluid.data(
+                name="q%d" % i, shape=[None, 1], lod_level=1, dtype='int64')
             for i in range(len(self.query_encoders))
         ]
         pt_slots = [
-            io.data(
-                name="pt%d" % i, shape=[1], lod_level=1, dtype='int64')
+            fluid.data(
+                name="pt%d" % i, shape=[None, 1], lod_level=1, dtype='int64')
             for i in range(len(self.title_encoders))
         ]
         # lookup embedding for each slot
         q_embs = [
-            nn.embedding(
+            fluid.embedding(
                 input=query, size=self.emb_shape, param_attr="emb")
             for query in q_slots
         ]
         pt_embs = [
-            nn.embedding(
+            fluid.embedding(
                 input=title, size=self.emb_shape, param_attr="emb")
             for title in pt_slots
         ]
@@ -236,8 +243,14 @@ class MultiviewSimnet(object):
         q_concat = nn.concat(q_encodes)
         pt_concat = nn.concat(pt_encodes)
         # projection of hidden layer
-        q_hid = nn.fc(q_concat, size=self.hidden_size, param_attr='q_fc.w', bias_attr='q_fc.b')
-        pt_hid = nn.fc(pt_concat, size=self.hidden_size, param_attr='t_fc.w', bias_attr='t_fc.b')
+        q_hid = nn.fc(q_concat,
+                      size=self.hidden_size,
+                      param_attr='q_fc.w',
+                      bias_attr='q_fc.b')
+        pt_hid = nn.fc(pt_concat,
+                       size=self.hidden_size,
+                       param_attr='t_fc.w',
+                       bias_attr='t_fc.b')
         # cosine of hidden layers
         cos = nn.cos_sim(q_hid, pt_hid)
         return cos

PaddleRec/multiview_simnet/train.py

@@ -88,6 +88,22 @@ def parse_args():
     return parser.parse_args()
 
 
+def check_version():
+    """
+     Log error and exit when the installed version of paddlepaddle is
+     not satisfied.
+     """
+    err = "PaddlePaddle version 1.6 or higher is required, " \
+          "or a suitable develop version is satisfied as well. \n" \
+          "Please make sure the version is good with your code." \
+
+    try:
+        fluid.require_version('1.6.0')
+    except Exception as e:
+        logger.error(err)
+        sys.exit(1)
+
+
 def start_train(args):
     if args.enable_ce:
         SEED = 102
@@ -170,4 +186,5 @@ def main():
 
 
 if __name__ == "__main__":
+    check_version()
     main()

PaddleRec/ssr/README.md

@@ -12,6 +12,10 @@ Sequence Semantic Retrieval(SSR) Model shares the similar idea with Multi-Rate D
 - The idea of SSR is to model a user's personalized interest of an item through matching model structure, and the representation of a news item can be computed online even the news item does not exist in training dataset.
 - With the representation of news items, we are able to build an vector indexing service online for news prediction and this is the retrieval part of SSR.
 
+## Version
+**Now all models in PaddleRec require PaddlePaddle version 1.6 or higher, or suitable develop version.**
+
+
 ## Dataset
 Dataset preprocessing follows the method of [GRU4Rec Project](https://github.com/PaddlePaddle/models/tree/develop/fluid/PaddleRec/gru4rec). Note that you should reuse scripts from GRU4Rec project for data preprocessing.
 
@@ -39,7 +43,7 @@ cpu 单机多卡训练
 CPU_NUM=10 python train.py --train_dir train_data --use_cuda 0 --parallel 1 --batch_size 50 --model_dir model_output --num_devices 10
 ```
 
-本地模拟多机训练
+本地模拟多机训练, 不支持windows.
 ``` bash
 sh cluster_train.sh
 ```

PaddleRec/ssr/infer.py

@@ -120,6 +120,7 @@ def infer(args, vocab_size, test_reader):
 
 
 if __name__ == "__main__":
+    utils.check_version()
     args = parse_args()
     start_index = args.start_index
     last_index = args.last_index

PaddleRec/ssr/nets.py

@@ -86,16 +86,17 @@ class SequenceSemanticRetrieval(object):
         return correct
 
     def train(self):
-        user_data = io.data(name="user", shape=[1], dtype="int64", lod_level=1)
-        pos_item_data = io.data(
-            name="p_item", shape=[1], dtype="int64", lod_level=1)
-        neg_item_data = io.data(
-            name="n_item", shape=[1], dtype="int64", lod_level=1)
-        user_emb = nn.embedding(
+        user_data = fluid.data(
+            name="user", shape=[None, 1], dtype="int64", lod_level=1)
+        pos_item_data = fluid.data(
+            name="p_item", shape=[None, 1], dtype="int64", lod_level=1)
+        neg_item_data = fluid.data(
+            name="n_item", shape=[None, 1], dtype="int64", lod_level=1)
+        user_emb = fluid.embedding(
             input=user_data, size=self.emb_shape, param_attr="emb.item")
-        pos_item_emb = nn.embedding(
+        pos_item_emb = fluid.embedding(
             input=pos_item_data, size=self.emb_shape, param_attr="emb.item")
-        neg_item_emb = nn.embedding(
+        neg_item_emb = fluid.embedding(
             input=neg_item_data, size=self.emb_shape, param_attr="emb.item")
         user_enc = self.user_encoder.forward(user_emb)
         pos_item_enc = self.item_encoder.forward(pos_item_emb)

PaddleRec/ssr/reader.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 import random
+import io
 
 
 class Dataset:
@@ -33,7 +34,7 @@ class YoochooseVocab(Vocab):
     def load(self, filelist):
         idx = 0
         for f in filelist:
-            with open(f, "r") as fin:
+            with io.open(f, "r", encoding='utf-8') as fin:
                 for line in fin:
                     group = line.strip().split()
                     for item in group:
@@ -64,7 +65,7 @@ class YoochooseDataset(Dataset):
     def _reader_creator(self, filelist, is_train):
         def reader():
             for f in filelist:
-                with open(f, 'r') as fin:
+                with io.open(f, 'r', encoding='utf-8') as fin:
                     line_idx = 0
                     for line in fin:
                         ids = line.strip().split()

PaddleRec/ssr/train.py

@@ -137,8 +137,7 @@ def train(args):
             gpu_num = device[1]
             print("kpis\teach_pass_duration_gpu%s\t%s" %
                   (gpu_num, total_time / epoch_idx))
-            print("kpis\ttrain_acc_gpu%s\t%s" %
-                (gpu_num, ce_acc))
+            print("kpis\ttrain_acc_gpu%s\t%s" % (gpu_num, ce_acc))
         else:
             cpu_num = device[1]
             threads_num = device[2]
@@ -165,4 +164,5 @@ def main():
 
 
 if __name__ == "__main__":
+    utils.check_version()
     main()

PaddleRec/ssr/utils.py

@@ -4,10 +4,11 @@ import os
 import logging
 import paddle.fluid as fluid
 import paddle
+import io
 
 
 def get_vocab_size(vocab_path):
-    with open(vocab_path, "r") as rf:
+    with io.open(vocab_path, "r", encoding='utf-8') as rf:
         line = rf.readline()
         return int(line.strip())
 
@@ -16,7 +17,7 @@ def construct_train_data(file_dir, vocab_path, batch_size):
     vocab_size = get_vocab_size(vocab_path)
     files = [file_dir + '/' + f for f in os.listdir(file_dir)]
     y_data = reader.YoochooseDataset(vocab_size)
-    train_reader = paddle.batch(
+    train_reader = fluid.io.batch(
         paddle.reader.shuffle(
             y_data.train(files), buf_size=batch_size * 100),
         batch_size=batch_size)
@@ -27,10 +28,26 @@ def construct_test_data(file_dir, vocab_path, batch_size):
     vocab_size = get_vocab_size(vocab_path)
     files = [file_dir + '/' + f for f in os.listdir(file_dir)]
     y_data = reader.YoochooseDataset(vocab_size)
-    test_reader = paddle.batch(y_data.test(files), batch_size=batch_size)
+    test_reader = fluid.io.batch(y_data.test(files), batch_size=batch_size)
     return test_reader, vocab_size
 
 
+def check_version():
+    """
+     Log error and exit when the installed version of paddlepaddle is
+     not satisfied.
+     """
+    err = "PaddlePaddle version 1.6 or higher is required, " \
+          "or a suitable develop version is satisfied as well. \n" \
+          "Please make sure the version is good with your code." \
+
+    try:
+        fluid.require_version('1.6.0')
+    except Exception as e:
+        logger.error(err)
+        sys.exit(1)
+
+
 def infer_data(raw_data, place):
     data = [dat[0] for dat in raw_data]
     seq_lens = [len(seq) for seq in data]

PaddleRec/tagspace/README.md

@@ -26,6 +26,8 @@ TagSpace模型的介绍可以参阅论文[#TagSpace: Semantic Embeddings from Ha
 
 Tagspace模型学习文本及标签的embedding表示，应用于工业级的标签推荐，具体应用场景有feed新闻标签推荐。
 
+**Now all models in PaddleRec require PaddlePaddle version 1.6 or higher, or suitable develop version.**
+
 同时推荐用户参考[ IPython Notebook demo](https://aistudio.baidu.com/aistudio/projectDetail/122298)
 
 ## 数据下载及预处理

PaddleRec/tagspace/infer.py

@@ -71,6 +71,7 @@ def infer(test_reader, vocab_tag, use_cuda, model_path, epoch):
 
 
 if __name__ == "__main__":
+    utils.check_version()
     args = parse_args()
     start_index = args.start_index
     last_index = args.last_index

PaddleRec/tagspace/net.py

@@ -2,19 +2,30 @@ import paddle.fluid as fluid
 import paddle.fluid.layers.nn as nn
 import paddle.fluid.layers.tensor as tensor
 import paddle.fluid.layers.control_flow as cf
-import paddle.fluid.layers.io as io
 
-def network(vocab_text_size, vocab_tag_size, emb_dim=10, hid_dim=1000, win_size=5, margin=0.1, neg_size=5):
+
+def network(vocab_text_size,
+            vocab_tag_size,
+            emb_dim=10,
+            hid_dim=1000,
+            win_size=5,
+            margin=0.1,
+            neg_size=5):
     """ network definition """
-    text = io.data(name="text", shape=[1], lod_level=1, dtype='int64')
-    pos_tag = io.data(name="pos_tag", shape=[1], lod_level=1, dtype='int64')
-    neg_tag = io.data(name="neg_tag", shape=[1], lod_level=1, dtype='int64')
-    text_emb = nn.embedding(
+    text = fluid.data(name="text", shape=[None, 1], lod_level=1, dtype='int64')
+    pos_tag = fluid.data(
+        name="pos_tag", shape=[None, 1], lod_level=1, dtype='int64')
+    neg_tag = fluid.data(
+        name="neg_tag", shape=[None, 1], lod_level=1, dtype='int64')
+    text_emb = fluid.embedding(
         input=text, size=[vocab_text_size, emb_dim], param_attr="text_emb")
-    pos_tag_emb = nn.embedding(
+    text_emb = fluid.layers.squeeze(input=text_emb, axes=[1])
+    pos_tag_emb = fluid.embedding(
         input=pos_tag, size=[vocab_tag_size, emb_dim], param_attr="tag_emb")
-    neg_tag_emb = nn.embedding(
+    pos_tag_emb = fluid.layers.squeeze(input=pos_tag_emb, axes=[1])
+    neg_tag_emb = fluid.embedding(
         input=neg_tag, size=[vocab_tag_size, emb_dim], param_attr="tag_emb")
+    neg_tag_emb = fluid.layers.squeeze(input=neg_tag_emb, axes=[1])
 
     conv_1d = fluid.nets.sequence_conv_pool(
         input=text_emb,
@@ -23,20 +34,20 @@ def network(vocab_text_size, vocab_tag_size, emb_dim=10, hid_dim=1000, win_size=
         act="tanh",
         pool_type="max",
         param_attr="cnn")
-    text_hid = fluid.layers.fc(input=conv_1d, size=emb_dim, param_attr="text_hid")
+    text_hid = fluid.layers.fc(input=conv_1d,
+                               size=emb_dim,
+                               param_attr="text_hid")
     cos_pos = nn.cos_sim(pos_tag_emb, text_hid)
     mul_text_hid = fluid.layers.sequence_expand_as(x=text_hid, y=neg_tag_emb)
     mul_cos_neg = nn.cos_sim(neg_tag_emb, mul_text_hid)
-    cos_neg_all = fluid.layers.sequence_reshape(input=mul_cos_neg, new_dim=neg_size)
+    cos_neg_all = fluid.layers.sequence_reshape(
+        input=mul_cos_neg, new_dim=neg_size)
     #choose max negtive cosine
     cos_neg = nn.reduce_max(cos_neg_all, dim=1, keep_dim=True)
     #calculate hinge loss
     loss_part1 = nn.elementwise_sub(
         tensor.fill_constant_batch_size_like(
-                input=cos_pos,
-                shape=[-1, 1],
-                value=margin,
-                dtype='float32'),
+            input=cos_pos, shape=[-1, 1], value=margin, dtype='float32'),
         cos_pos)
     loss_part2 = nn.elementwise_add(loss_part1, cos_neg)
     loss_part3 = nn.elementwise_max(

PaddleRec/tagspace/text2paddle.py

@@ -4,6 +4,11 @@ import collections
 import os
 import csv
 import re
+import sys
+if six.PY2:
+    reload(sys)
+    sys.setdefaultencoding('utf-8')
+
 
 def word_count(column_num, input_file, word_freq=None):
     """
@@ -13,10 +18,11 @@ def word_count(column_num, input_file, word_freq=None):
         word_freq = collections.defaultdict(int)
     data_file = csv.reader(input_file)
     for row in data_file:
-        for w in re.split(r'\W+',row[column_num].strip()):
-            word_freq[w]+= 1
+        for w in re.split(r'\W+', row[column_num].strip()):
+            word_freq[w] += 1
     return word_freq
 
+
 def build_dict(column_num=2, min_word_freq=0, train_dir="", test_dir=""):
     """
     Build a word dictionary from the corpus,  Keys of the dictionary are words,
@@ -25,11 +31,11 @@ def build_dict(column_num=2, min_word_freq=0, train_dir="", test_dir=""):
     word_freq = collections.defaultdict(int)
     files = os.listdir(train_dir)
     for fi in files:
-        with open(train_dir + '/' + fi, "r") as f:
+        with open(os.path.join(train_dir, fi), "r", encoding='utf-8') as f:
             word_freq = word_count(column_num, f, word_freq)
     files = os.listdir(test_dir)
     for fi in files:
-        with open(test_dir + '/' + fi, "r") as f:
+        with open(os.path.join(test_dir, fi), "r", encoding='utf-8') as f:
             word_freq = word_count(column_num, f, word_freq)
 
     word_freq = [x for x in six.iteritems(word_freq) if x[1] > min_word_freq]
@@ -39,13 +45,16 @@ def build_dict(column_num=2, min_word_freq=0, train_dir="", test_dir=""):
     return word_idx
 
 
-def write_paddle(text_idx, tag_idx, train_dir, test_dir, output_train_dir, output_test_dir):
+def write_paddle(text_idx, tag_idx, train_dir, test_dir, output_train_dir,
+                 output_test_dir):
     files = os.listdir(train_dir)
     if not os.path.exists(output_train_dir):
         os.mkdir(output_train_dir)
     for fi in files:
-        with open(train_dir + '/' + fi, "r") as f:
-            with open(output_train_dir + '/' + fi, "w") as wf:
+        with open(os.path.join(train_dir, fi), "r", encoding='utf-8') as f:
+            with open(
+                    os.path.join(output_train_dir, fi), "w",
+                    encoding='utf-8') as wf:
                 data_file = csv.reader(f)
                 for row in data_file:
                     tag_raw = re.split(r'\W+', row[0].strip())
@@ -61,8 +70,10 @@ def write_paddle(text_idx, tag_idx, train_dir, test_dir, output_train_dir, outpu
     if not os.path.exists(output_test_dir):
         os.mkdir(output_test_dir)
     for fi in files:
-        with open(test_dir + '/' + fi, "r") as f:
-            with open(output_test_dir + '/' + fi, "w") as wf:
+        with open(os.path.join(test_dir, fi), "r", encoding='utf-8') as f:
+            with open(
+                    os.path.join(output_test_dir, fi), "w",
+                    encoding='utf-8') as wf:
                 data_file = csv.reader(f)
                 for row in data_file:
                     tag_raw = re.split(r'\W+', row[0].strip())
@@ -74,18 +85,21 @@ def write_paddle(text_idx, tag_idx, train_dir, test_dir, output_train_dir, outpu
                         wf.write(str(w) + " ")
                     wf.write("\n")
 
-def text2paddle(train_dir, test_dir, output_train_dir, output_test_dir, output_vocab_text, output_vocab_tag):
+
+def text2paddle(train_dir, test_dir, output_train_dir, output_test_dir,
+                output_vocab_text, output_vocab_tag):
     print("start constuct word dict")
     vocab_text = build_dict(2, 0, train_dir, test_dir)
-    with open(output_vocab_text, "w") as wf:
+    with open(output_vocab_text, "w", encoding='utf-8') as wf:
         wf.write(str(len(vocab_text)) + "\n")
 
     vocab_tag = build_dict(0, 0, train_dir, test_dir)
-    with open(output_vocab_tag, "w") as wf:
+    with open(output_vocab_tag, "w", encoding='utf-8') as wf:
         wf.write(str(len(vocab_tag)) + "\n")
 
     print("construct word dict done\n")
-    write_paddle(vocab_text, vocab_tag, train_dir, test_dir, output_train_dir, output_test_dir)
+    write_paddle(vocab_text, vocab_tag, train_dir, test_dir, output_train_dir,
+                 output_test_dir)
 
 
 train_dir = sys.argv[1]
@@ -94,4 +108,5 @@ output_train_dir = sys.argv[3]
 output_test_dir = sys.argv[4]
 output_vocab_text = sys.argv[5]
 output_vocab_tag = sys.argv[6]
-text2paddle(train_dir, test_dir, output_train_dir, output_test_dir, output_vocab_text, output_vocab_tag)
+text2paddle(train_dir, test_dir, output_train_dir, output_test_dir,
+            output_vocab_text, output_vocab_tag)

PaddleRec/tagspace/train.py

@@ -114,7 +114,8 @@ def train():
                     "neg_tag": lod_neg_tag
                 },
                 fetch_list=[avg_cost.name, correct.name])
-            ce_info.append(float(np.sum(correct_val)) / (args.num_devices * batch_size))
+            ce_info.append(
+                float(np.sum(correct_val)) / (args.num_devices * batch_size))
             if batch_id % args.print_batch == 0:
                 print("TRAIN --> pass: {} batch_num: {} avg_cost: {}, acc: {}"
                       .format(pass_idx, (batch_id + 10) * batch_size,
@@ -128,8 +129,7 @@ def train():
         save_dir = "%s/epoch_%d" % (model_dir, epoch_idx)
         feed_var_names = ["text", "pos_tag"]
         fetch_vars = [cos_pos]
-        fluid.io.save_inference_model(save_dir, feed_var_names, fetch_vars,
-                                      exe)
+        fluid.io.save_inference_model(save_dir, feed_var_names, fetch_vars, exe)
     # only for ce
     if args.enable_ce:
         ce_acc = 0
@@ -143,8 +143,7 @@ def train():
             gpu_num = device[1]
             print("kpis\teach_pass_duration_gpu%s\t%s" %
                   (gpu_num, total_time / epoch_idx))
-            print("kpis\ttrain_acc_gpu%s\t%s" %
-                (gpu_num, ce_acc))
+            print("kpis\ttrain_acc_gpu%s\t%s" % (gpu_num, ce_acc))
         else:
             cpu_num = device[1]
             threads_num = device[2]
@@ -168,4 +167,5 @@ def get_device(args):
 
 
 if __name__ == "__main__":
+    utils.check_version()
     train()

PaddleRec/tagspace/utils.py

@@ -8,6 +8,11 @@ import numpy as np
 import paddle.fluid as fluid
 import paddle
 import csv
+import io
+if six.PY2:
+    reload(sys)
+    sys.setdefaultencoding('utf-8')
+
 
 def to_lodtensor(data, place):
     """ convert to LODtensor """
@@ -24,12 +29,29 @@ def to_lodtensor(data, place):
     res.set_lod([lod])
     return res
 
+
 def get_vocab_size(vocab_path):
-    with open(vocab_path, "r") as rf:
+    with io.open(vocab_path, "r") as rf:
         line = rf.readline()
         return int(line.strip())
 
 
+def check_version():
+    """
+     Log error and exit when the installed version of paddlepaddle is
+     not satisfied.
+     """
+    err = "PaddlePaddle version 1.6 or higher is required, " \
+          "or a suitable develop version is satisfied as well. \n" \
+          "Please make sure the version is good with your code." \
+
+    try:
+        fluid.require_version('1.6.0')
+    except Exception as e:
+        logger.error(err)
+        sys.exit(1)
+
+
 def prepare_data(file_dir,
                  vocab_text_path,
                  vocab_tag_path,
@@ -45,19 +67,25 @@ def prepare_data(file_dir,
         reader = sort_batch(
             paddle.reader.shuffle(
                 train(
-                    file_dir, vocab_tag_size, neg_size,
-                    buffer_size, data_type=DataType.SEQ),
+                    file_dir,
+                    vocab_tag_size,
+                    neg_size,
+                    buffer_size,
+                    data_type=DataType.SEQ),
                 buf_size=buffer_size),
-            batch_size, batch_size * 20)
+            batch_size,
+            batch_size * 20)
     else:
         vocab_tag_size = get_vocab_size(vocab_tag_path)
         vocab_text_size = 0
         reader = sort_batch(
             test(
                 file_dir, vocab_tag_size, buffer_size, data_type=DataType.SEQ),
-            batch_size, batch_size * 20)
+            batch_size,
+            batch_size * 20)
     return vocab_text_size, vocab_tag_size, reader
 
+
 def sort_batch(reader, batch_size, sort_group_size, drop_last=False):
     """
     Create a batched reader.
@@ -107,11 +135,13 @@ def sort_batch(reader, batch_size, sort_group_size, drop_last=False):
 class DataType(object):
     SEQ = 2
 
+
 def train_reader_creator(file_dir, tag_size, neg_size, n, data_type):
     def reader():
         files = os.listdir(file_dir)
         for fi in files:
-            with open(file_dir + '/' + fi, "r") as f:
+            with io.open(
+                    os.path.join(file_dir, fi), "r", encoding='utf-8') as f:
                 for l in f:
                     l = l.strip().split(",")
                     pos_index = int(l[0])
@@ -123,7 +153,7 @@ def train_reader_creator(file_dir, tag_size, neg_size, n, data_type):
                     max_iter = 100
                     now_iter = 0
                     sum_n = 0
-                    while(sum_n < neg_size) :
+                    while (sum_n < neg_size):
                         now_iter += 1
                         if now_iter > max_iter:
                             print("error : only one class")
@@ -135,13 +165,16 @@ def train_reader_creator(file_dir, tag_size, neg_size, n, data_type):
                             sum_n += 1
                     if n > 0 and len(text) > n: continue
                     yield text, pos_tag, neg_tag
+
     return reader
 
+
 def test_reader_creator(file_dir, tag_size, n, data_type):
     def reader():
         files = os.listdir(file_dir)
         for fi in files:
-            with open(file_dir + '/' + fi, "r") as f:
+            with io.open(
+                    os.path.join(file_dir, fi), "r", encoding='utf-8') as f:
                 for l in f:
                     l = l.strip().split(",")
                     pos_index = int(l[0])
@@ -153,11 +186,13 @@ def test_reader_creator(file_dir, tag_size, n, data_type):
                         tag = []
                         tag.append(ii)
                         yield text, tag, pos_tag
+
     return reader
 
 
 def train(train_dir, tag_size, neg_size, n, data_type=DataType.SEQ):
     return train_reader_creator(train_dir, tag_size, neg_size, n, data_type)
 
+
 def test(test_dir, tag_size, n, data_type=DataType.SEQ):
     return test_reader_creator(test_dir, tag_size, n, data_type)

PaddleRec/word2vec/README.md

@@ -20,6 +20,8 @@
 ## 介绍
 本例实现了skip-gram模式的word2vector模型。
 
+**目前模型库下模型均要求使用PaddlePaddle 1.6及以上版本或适当的develop版本。**
+
 同时推荐用户参考[ IPython Notebook demo](https://aistudio.baidu.com/aistudio/projectDetail/124377)
 
 ## 数据下载
@@ -36,7 +38,7 @@ mv 1-billion-word-language-modeling-benchmark-r13output/training-monolingual.tok
 
 ```bash
 mkdir data
-wget https://paddlerec.bj.bcebos.com/word2vec/1-billion-word-language-modeling-benchmark-r13output.tar
+wget --no-check-certificate https://paddlerec.bj.bcebos.com/word2vec/1-billion-word-language-modeling-benchmark-r13output.tar
 tar xvf 1-billion-word-language-modeling-benchmark-r13output.tar
 mv 1-billion-word-language-modeling-benchmark-r13output/training-monolingual.tokenized.shuffled/ data/
 ```
@@ -45,7 +47,7 @@ mv 1-billion-word-language-modeling-benchmark-r13output/training-monolingual.tok
 
 ```bash
 mkdir data
-wget https://paddlerec.bj.bcebos.com/word2vec/text.tar
+wget --no-check-certificate https://paddlerec.bj.bcebos.com/word2vec/text.tar
 tar xvf text.tar
 mv text data/
 ```
@@ -95,7 +97,7 @@ python train.py -h
 OPENBLAS_NUM_THREADS=1 CPU_NUM=5 python train.py --train_data_dir data/convert_text8 --dict_path data/test_build_dict --num_passes 10 --batch_size 100 --model_output_dir v1_cpu5_b100_lr1dir --base_lr 1.0 --print_batch 1000 --with_speed --is_sparse
 ```
 
-本地单机模拟多机训练
+本地单机模拟多机训练, 目前暂不支持windows。
 
 ```bash
 sh cluster_train.sh
@@ -106,9 +108,9 @@ sh cluster_train.sh
 
 ```bash
 #全量数据集测试集
-wget https://paddlerec.bj.bcebos.com/word2vec/test_dir.tar
+wget --no-check-certificate https://paddlerec.bj.bcebos.com/word2vec/test_dir.tar
 #样本数据集测试集
-wget https://paddlerec.bj.bcebos.com/word2vec/test_mid_dir.tar
+wget --no-check-certificate https://paddlerec.bj.bcebos.com/word2vec/test_mid_dir.tar
 ```
 
 预测命令，注意词典名称需要加后缀"_word_to_id_", 此文件是预处理阶段生成的。

PaddleRec/word2vec/infer.py

@@ -10,6 +10,9 @@ import paddle.fluid as fluid
 import paddle
 import net
 import utils
+if six.PY2:
+    reload(sys)
+    sys.setdefaultencoding('utf-8')
 
 
 def parse_args():
@@ -76,15 +79,12 @@ def infer_epoch(args, vocab_size, test_reader, use_cuda, i2w):
                 for data in test_reader():
                     step_id += 1
                     b_size = len([dat[0] for dat in data])
-                    wa = np.array(
-                        [dat[0] for dat in data]).astype("int64").reshape(
-                            b_size, 1)
-                    wb = np.array(
-                        [dat[1] for dat in data]).astype("int64").reshape(
-                            b_size, 1)
-                    wc = np.array(
-                        [dat[2] for dat in data]).astype("int64").reshape(
-                            b_size, 1)
+                    wa = np.array([dat[0] for dat in data]).astype(
+                        "int64").reshape(b_size)
+                    wb = np.array([dat[1] for dat in data]).astype(
+                        "int64").reshape(b_size)
+                    wc = np.array([dat[2] for dat in data]).astype(
+                        "int64").reshape(b_size)
 
                     label = [dat[3] for dat in data]
                     input_word = [dat[4] for dat in data]
@@ -93,9 +93,8 @@ def infer_epoch(args, vocab_size, test_reader, use_cuda, i2w):
                                        "analogy_a": wa,
                                        "analogy_b": wb,
                                        "analogy_c": wc,
-                                       "all_label":
-                                       np.arange(vocab_size).reshape(
-                                           vocab_size, 1).astype("int64"),
+                                       "all_label": np.arange(vocab_size)
+                                       .reshape(vocab_size).astype("int64"),
                                    },
                                    fetch_list=[pred.name, values],
                                    return_numpy=False)
@@ -143,15 +142,12 @@ def infer_step(args, vocab_size, test_reader, use_cuda, i2w):
                     for data in test_reader():
                         step_id += 1
                         b_size = len([dat[0] for dat in data])
-                        wa = np.array(
-                            [dat[0] for dat in data]).astype("int64").reshape(
-                                b_size, 1)
-                        wb = np.array(
-                            [dat[1] for dat in data]).astype("int64").reshape(
-                                b_size, 1)
-                        wc = np.array(
-                            [dat[2] for dat in data]).astype("int64").reshape(
-                                b_size, 1)
+                        wa = np.array([dat[0] for dat in data]).astype(
+                            "int64").reshape(b_size)
+                        wb = np.array([dat[1] for dat in data]).astype(
+                            "int64").reshape(b_size)
+                        wc = np.array([dat[2] for dat in data]).astype(
+                            "int64").reshape(b_size)
 
                         label = [dat[3] for dat in data]
                         input_word = [dat[4] for dat in data]
@@ -162,7 +158,7 @@ def infer_step(args, vocab_size, test_reader, use_cuda, i2w):
                                 "analogy_b": wb,
                                 "analogy_c": wc,
                                 "all_label":
-                                np.arange(vocab_size).reshape(vocab_size, 1),
+                                np.arange(vocab_size).reshape(vocab_size),
                             },
                             fetch_list=[pred.name, values],
                             return_numpy=False)
@@ -185,6 +181,7 @@ def infer_step(args, vocab_size, test_reader, use_cuda, i2w):
 
 
 if __name__ == "__main__":
+    utils.check_version()
     args = parse_args()
     start_index = args.start_index
     last_index = args.last_index

PaddleRec/word2vec/net.py

@@ -23,10 +23,10 @@ import paddle.fluid as fluid
 def skip_gram_word2vec(dict_size, embedding_size, is_sparse=False, neg_num=5):
 
     datas = []
-    input_word = fluid.layers.data(name="input_word", shape=[1], dtype='int64')
-    true_word = fluid.layers.data(name='true_label', shape=[1], dtype='int64')
-    neg_word = fluid.layers.data(
-        name="neg_label", shape=[neg_num], dtype='int64')
+    input_word = fluid.data(name="input_word", shape=[None, 1], dtype='int64')
+    true_word = fluid.data(name='true_label', shape=[None, 1], dtype='int64')
+    neg_word = fluid.data(
+        name="neg_label", shape=[None, neg_num], dtype='int64')
 
     datas.append(input_word)
     datas.append(true_word)
@@ -37,7 +37,7 @@ def skip_gram_word2vec(dict_size, embedding_size, is_sparse=False, neg_num=5):
 
     words = fluid.layers.read_file(py_reader)
     init_width = 0.5 / embedding_size
-    input_emb = fluid.layers.embedding(
+    input_emb = fluid.embedding(
         input=words[0],
         is_sparse=is_sparse,
         size=[dict_size, embedding_size],
@@ -45,33 +45,31 @@ def skip_gram_word2vec(dict_size, embedding_size, is_sparse=False, neg_num=5):
             name='emb',
             initializer=fluid.initializer.Uniform(-init_width, init_width)))
 
-    true_emb_w = fluid.layers.embedding(
+    true_emb_w = fluid.embedding(
         input=words[1],
         is_sparse=is_sparse,
         size=[dict_size, embedding_size],
         param_attr=fluid.ParamAttr(
             name='emb_w', initializer=fluid.initializer.Constant(value=0.0)))
 
-    true_emb_b = fluid.layers.embedding(
+    true_emb_b = fluid.embedding(
         input=words[1],
         is_sparse=is_sparse,
         size=[dict_size, 1],
         param_attr=fluid.ParamAttr(
             name='emb_b', initializer=fluid.initializer.Constant(value=0.0)))
-    neg_word_reshape = fluid.layers.reshape(words[2], shape=[-1, 1])
-    neg_word_reshape.stop_gradient = True
+    input_emb = fluid.layers.squeeze(input=input_emb, axes=[1])
+    true_emb_w = fluid.layers.squeeze(input=true_emb_w, axes=[1])
+    true_emb_b = fluid.layers.squeeze(input=true_emb_b, axes=[1])
 
-    neg_emb_w = fluid.layers.embedding(
-        input=neg_word_reshape,
+    neg_emb_w = fluid.embedding(
+        input=words[2],
         is_sparse=is_sparse,
         size=[dict_size, embedding_size],
         param_attr=fluid.ParamAttr(
             name='emb_w', learning_rate=1.0))
-
-    neg_emb_w_re = fluid.layers.reshape(
-        neg_emb_w, shape=[-1, neg_num, embedding_size])
-    neg_emb_b = fluid.layers.embedding(
-        input=neg_word_reshape,
+    neg_emb_b = fluid.embedding(
+        input=words[2],
         is_sparse=is_sparse,
         size=[dict_size, 1],
         param_attr=fluid.ParamAttr(
@@ -86,8 +84,7 @@ def skip_gram_word2vec(dict_size, embedding_size, is_sparse=False, neg_num=5):
         true_emb_b)
     input_emb_re = fluid.layers.reshape(
         input_emb, shape=[-1, 1, embedding_size])
-    neg_matmul = fluid.layers.matmul(
-        input_emb_re, neg_emb_w_re, transpose_y=True)
+    neg_matmul = fluid.layers.matmul(input_emb_re, neg_emb_w, transpose_y=True)
     neg_matmul_re = fluid.layers.reshape(neg_matmul, shape=[-1, neg_num])
     neg_logits = fluid.layers.elementwise_add(neg_matmul_re, neg_emb_b_vec)
     #nce loss
@@ -111,22 +108,18 @@ def skip_gram_word2vec(dict_size, embedding_size, is_sparse=False, neg_num=5):
 
 
 def infer_network(vocab_size, emb_size):
-    analogy_a = fluid.layers.data(name="analogy_a", shape=[1], dtype='int64')
-    analogy_b = fluid.layers.data(name="analogy_b", shape=[1], dtype='int64')
-    analogy_c = fluid.layers.data(name="analogy_c", shape=[1], dtype='int64')
-    all_label = fluid.layers.data(
-        name="all_label",
-        shape=[vocab_size, 1],
-        dtype='int64',
-        append_batch_size=False)
-    emb_all_label = fluid.layers.embedding(
+    analogy_a = fluid.data(name="analogy_a", shape=[None], dtype='int64')
+    analogy_b = fluid.data(name="analogy_b", shape=[None], dtype='int64')
+    analogy_c = fluid.data(name="analogy_c", shape=[None], dtype='int64')
+    all_label = fluid.data(name="all_label", shape=[vocab_size], dtype='int64')
+    emb_all_label = fluid.embedding(
         input=all_label, size=[vocab_size, emb_size], param_attr="emb")
 
-    emb_a = fluid.layers.embedding(
+    emb_a = fluid.embedding(
         input=analogy_a, size=[vocab_size, emb_size], param_attr="emb")
-    emb_b = fluid.layers.embedding(
+    emb_b = fluid.embedding(
         input=analogy_b, size=[vocab_size, emb_size], param_attr="emb")
-    emb_c = fluid.layers.embedding(
+    emb_c = fluid.embedding(
         input=analogy_c, size=[vocab_size, emb_size], param_attr="emb")
     target = fluid.layers.elementwise_add(
         fluid.layers.elementwise_sub(emb_b, emb_a), emb_c)

PaddleRec/word2vec/preprocess.py

@@ -6,6 +6,10 @@ import six
 import argparse
 import io
 import math
+import sys
+if six.PY2:
+    reload(sys)
+    sys.setdefaultencoding('utf-8')
 prog = re.compile("[^a-z ]", flags=0)
 
 
@@ -110,10 +114,14 @@ def filter_corpus(args):
     if not os.path.exists(args.output_corpus_dir):
         os.makedirs(args.output_corpus_dir)
     for file in os.listdir(args.input_corpus_dir):
-        with io.open(args.output_corpus_dir + '/convert_' + file, "w") as wf:
         with io.open(
-                    args.input_corpus_dir + '/' + file, encoding='utf-8') as rf:
-                print(args.input_corpus_dir + '/' + file)
+                os.path.join(args.output_corpus_dir, 'convert_' + file),
+                "w",
+                encoding='utf-8') as wf:
+            with io.open(
+                    os.path.join(args.input_corpus_dir, file),
+                    encoding='utf-8') as rf:
+                print(os.path.join(args.input_corpus_dir, file))
                 for line in rf:
                     signal = False
                     line = text_strip(line)

PaddleRec/word2vec/train.py

@@ -12,6 +12,12 @@ import six
 import reader
 from net import skip_gram_word2vec
 
+import utils
+import sys
+if six.PY2:
+    reload(sys)
+    sys.setdefaultencoding('utf-8')
+
 logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s')
 logger = logging.getLogger("fluid")
 logger.setLevel(logging.INFO)
@@ -224,5 +230,6 @@ def train(args):
 
 
 if __name__ == '__main__':
+    utils.check_version()
     args = parse_args()
     train(args)

PaddleRec/word2vec/utils.py

@@ -7,12 +7,13 @@ import paddle.fluid as fluid
 import paddle
 import os
 import preprocess
+import io
 
 
 def BuildWord_IdMap(dict_path):
     word_to_id = dict()
     id_to_word = dict()
-    with open(dict_path, 'r') as f:
+    with io.open(dict_path, 'r', encoding='utf-8') as f:
         for line in f:
             word_to_id[line.split(' ')[0]] = int(line.split(' ')[1])
             id_to_word[int(line.split(' ')[1])] = line.split(' ')[0]
@@ -22,10 +23,26 @@ def BuildWord_IdMap(dict_path):
 def prepare_data(file_dir, dict_path, batch_size):
     w2i, i2w = BuildWord_IdMap(dict_path)
     vocab_size = len(i2w)
-    reader = paddle.batch(test(file_dir, w2i), batch_size)
+    reader = fluid.io.batch(test(file_dir, w2i), batch_size)
     return vocab_size, reader, i2w
 
 
+def check_version():
+    """
+     Log error and exit when the installed version of paddlepaddle is
+     not satisfied.
+     """
+    err = "PaddlePaddle version 1.6 or higher is required, " \
+          "or a suitable develop version is satisfied as well. \n" \
+          "Please make sure the version is good with your code." \
+
+    try:
+        fluid.require_version('1.6.0')
+    except Exception as e:
+        logger.error(err)
+        sys.exit(1)
+
+
 def native_to_unicode(s):
     if _is_unicode(s):
         return s
@@ -75,7 +92,8 @@ def reader_creator(file_dir, word_to_id):
     def reader():
         files = os.listdir(file_dir)
         for fi in files:
-            with open(file_dir + '/' + fi, "r") as f:
+            with io.open(
+                    os.path.join(file_dir, fi), "r", encoding='utf-8') as f:
                 for line in f:
                     if ':' in line:
                         pass